Golmehr Ashrafpoor

Emilie Bollache

Alban Redheuil

Alain De Cesare

Alain Giron

Carine Defrance

Arshid Azarine

Ludivine Perdrix

Magalie Ladouceur

Benoit Diebold

Benoît Diebold

Elie Mousseaux

Nadjia Kachenoura
email: nadjia.kachenoura@inserm.fr

A velocity-encoded magnetic resonance imaging study

Keywords: Diastole, Aging, MRI, Reference Values, Left Ventricle

Introduction

Age-related alterations of the heart include concentric left ventricular (LV) remodeling [START_REF] Cheng | Correlates of echocardiographic indices of cardiac remodeling over the adult life course: longitudinal observations from the Framingham Heart Study[END_REF] along with altered diastolic function [2; 3], although ejection fraction remains frequently preserved. Indeed, previous studies indicated that an apparently preserved systolic function, as assessed by a normal ejection fraction, was found in 30 to 50% of elderly subjects with heart failure [4; 5]. Furthermore, diastolic dysfunction contributes substantially to heart failure in the elderly [6; 7]. Accordingly, an accurate longitudinal assessment of LV diastolic function could be of major usefulness in predicting heart failure onset [START_REF] Kane | Progression of left ventricular diastolic dysfunction and risk of heart failure[END_REF]. However, distinguishing physiological from pathological variations in LV diastolic filling requires a priori knowledge on expected physiological age-related subclinical changes in asymptomatic individuals. Doppler echocardiography is the modality of choice for the non-invasive evaluation of LV diastolic dysfunction in clinical routine, based on transmitral blood flow maximal velocities and mitral annulus longitudinal velocities. Previous echocardiographic studies demonstrated that healthy aging is associated with altered LV diastolic function as reflected by a decrease in transmitral early filling peak velocity (E) along with an increase in atrial peak velocity (A), resulting in a decrease in E/A ratio [START_REF] Benjamin | Determinants of Doppler indexes of left ventricular diastolic function in normal subjects (the Framingham Heart Study)[END_REF][10][11][12][START_REF] Yamada | Effect of aging on diastolic left ventricular myocardial velocities measured by pulsed tissue Doppler imaging in healthy subjects[END_REF] . A decrease in mitral annular early peak velocity (E'), associated with an overall increase in filling pressures (E/E') [10; 11; 14; 15], and prolonged deceleration [11] and isovolumic relaxation [10; 16] times have also been found to be associated to healthy aging.

Recent studies demonstrated the ability of phase-contrast cardiovascular magnetic resonance (MRI-PC) to assess diastolic dysfunction [17][18][19][20][21][22][23], resulting in relevant findings as confirmed by a head-to-head comparison with echocardiography as a clinical reference [17; 20-22; 24].

Although MRI is an accurate non-invasive modality for the evaluation of global LV systolic function [25], as well as myocardial viability [26; 27], its routine usefulness for the assessment of diastolic function is not established. To date, MRI-PC studies have evaluated diastolic function in small groups of healthy controls and patients with known diastolic dysfunction [20][21][22][23]. However, to the best of our knowledge, no systematic study has reported age-related variations of MRI-PC diastolic function indices and provided normal values.

Accordingly, we analyzed MRI-PC data of 100 healthy volunteers, with equal distribution of males and females, to characterize age-related variations in diastolic function and to report MRI age-associated normal values.

Materials and methods

Study population

We studied 100 healthy volunteers (age=42±15years, 50 females) who underwent an MRI exam for the evaluation of LV systolic and diastolic function. Subjects were prospectively recruited by advertisement from the community to complete 5 age groups with an equal distribution of females and males. Group 1: 20 to 29 years, group 2: 30 to 39 years, group 3: 40 to 49 years, group 4: 50 to 59 years, and group 5 ≥ 60 years. The resulting population had a mixed racial background with a majority of Caucasians. All subjects were asymptomatic and had no history of cardiovascular disease, and no diabetes. Body mass index (BMI) was <30kg/m² and all subjects had a normal electrocardiogram. Subjects with cardiovascular risk factors such as hypertension, hypercholesterolemia, and smoking were excluded from this study. The study protocol was approved by the institutional review board and informed consent was obtained from all participants.

Cardiac magnetic resonance imaging

MRI was performed using a 1.5T magnet (Signa HDx, GEMS, Waukesha, WI, USA) with an 8-channel cardiac-phased array surface coil. Retrospective ECG-gated PC pulse sequences were used to acquire two series of images during two consecutive breath-holds, at the tips of the mitral leaflets perpendicular to the transmitral inflow: 1) transmitral through-plane flow velocity (slice thickness= 8 mm, Field of view was 400-480 mm, acquisition matrix was 256x128, interpolated to 256x256 resulting in a spatial resolution of 1.56 to 1.88 mm , encoding velocity Venc=180cm/s, echo time TE=2.5-3.1ms, repetition time TR=5-7.6ms, views per segment=2; temporal resolution=10 to 15 ms after applying view sharing technique[28]), and 2) longitudinal myocardial velocity (slice thickness= 8 mm, field of view was 400-480 mm, acquisition matrix 256x128, interpolated to 256x256 resulting in a spatial resolution of 1.56 to 1.88 mm,, TE=4-5ms, TR=6.5-9.5ms, views per segment=2; temporal resolution=15 to 20ms after applying view sharing technique. The acquisition was first performed using Venc=20 cm/s, and was repeated with Venc=15 cm/s in case of noisy images). A 50% rectangular field of view centred on the mitral annulus was used to avoid wrap-around artefacts.

Prior to PC data, cine standard steady-state free precession (SSFP) sequences were acquired in long and short axis views during breath-holdings to cover the whole left heart using the following averaged scan parameters: acquisition matrix = 260×192, TR = 3.7 ms, TE = 1.5 ms, flip angle = 50°, pixel size = 0.74 mm × 0.74 mm, slice thickness = 8 mm, views per segment=12, temporal resolution was 15ms after applying a view sharing technique. SSFP cine acquisitions were analyzed using QMASS ® software (Medis, version 6, Leiden, the Netherlands), resulting in LV end-diastolic (EDV) and end-systolic (ESV) volumes, LV mass, left atrial end-diastolic and end-systolic volumes. Moreover mid-LV myocardial wall thickness was calculated on the anterior, lateral, inferior, and septal segments.

Brachial systolic (SBP) and diastolic (DBP) blood pressures were measured simultaneously to the MRI-PC acquisitions.

Automated analysis of MRI-PC data (Fig. 1)

Transmitral blood flow and myocardial MRI-PC images were analyzed by an operator blinded to clinical data using custom software [17], which was previously described and tested on controls and patients with severe aortic valve stenosis in comparison to Doppler echocardiography. Briefly, this software enables: 1) a colour-coded display of PC velocity images designed to distinguish through-plane velocities in both directions, 2) a semiautomated delineation of transmitral and aortic flows, resulting in mean velocity, maximal velocity and flow-rate curves throughout the cardiac cycle, 3) a semi-automated delineation of the myocardium, resulting in maximal myocardial longitudinal velocity curve throughout the cardiac cycle, and 4) an automated peak velocities and flow-rates detection as well as semiautomated down-slopes and up-slopes linear fitting on flow-rate curves to estimate temporal parameters (Fig. 2). For linear fitting of slopes, a manual interaction which consists in delimiting an appropriate portion of the filling flow-rate curve remained possible in case of erroneous automated fitting. This software was used to estimate the following diastolic parameters: 1) conventional transmitral early (E) and atrial (A) peak velocities as well as transmitral early (Ef) and atrial (Af) peak flow-rates and filling volume (FV), which was estimated as the area under the transmitral flow-rate curve between the automatically detected beginning and end of filling period, 2) deceleration time (DT), isovolumic relaxation time (IVRT), and 3) myocardial longitudinal (E') peak velocity on basal lateral and septal wall. Of note, the same dataset used for transmitral flow analysis was used to extract aortic ejection flow-rate curves and thus end of ejection time, which was used for IVRT estimation.

A second operator blinded to clinical data and to measurements of the first operator also performed the analysis of MRI-PC data on a sub-group of 30 subjects, including 6 subjects randomly selected in each age group.

Statistical Analysis

Normality of continuous variables distribution was tested using Shapiro-Wilk's test.

Differences across age groups were tested using a one-way ANOVA test, and a Tukey's range test was used to assess the significance of differences between all age groups (2 to 5) as compared to group 1. For all tests, p value<0.05 was considered as statistically significant.

Independent associations between age and LV diastolic parameters were performed using multivariate linear regression with adjustment for basic characteristics (gender, BMI, heart rate, SBP and DBP) as well as LV remodeling parameters and myocardial wall thickness.

The inter-observer variability was studied using the coefficient of variation defined as the standard deviation of the differences between the two series of measurements performed by the two operators, divided by the mean of the measurements. Analyses were performed using STATA 12, Statacorp LP.

Results

Baseline characteristics, pressures, as well as MRI parameters of LV function, myocardial wall thickness and left atrial volumes are summarized in table 1, for the five age groups.

Pressures were significantly higher with increasing age. LV volumes were slightly lower in older subjects resulting in a moderate increase in LV ejection fraction with age. While there were no significant differences in LV mass indexed to body surface area between age groups, LV mass indexed to end-diastolic volume increased significantly with age. A mild increase in myocardial wall thickness with age was also observed. A slight increase in left atrial enddiastolic volumes with age was noted; however such trend was no longer significant when volumes were indexed to body surface area. Indeed, a significant difference was found only for comparison between group1 <30 years and group 5 ≥ 60 years.

Reproducibility of diastolic parameters measurement

For each subject, the processing time for MRI-PC data was less than 5 minutes. Diastolic indices estimation was reproducible as reflected by overall low variability averaged over the sub-group of 30 volunteers: 0.31% for E, 0.80% for A, 0.70% for Ef, 2.70% for Af, 2.15% for FV, 3.25% for DT, 3.73% for IVRT, and 6.26% for E'.

Associations of Diastolic Function with Age

Diastolic parameters averaged for the five age groups, representing the distribution of normal LV filling MRI-PC indices over age, along with SSFP left atrial end-systolic volumes are summarized in table 2. Differences across age groups were statistically significant for all parameters and the expected trends were found. For comparisons against the group 1, differences were statistically significant for groups 3, 4 and 5 for E', E/A, Ef/Af and Af/FV. Temporal parameters (DT, IVRT) and E/E' increased with age, with a significant increase only in elderly groups, as compared to group1. Linear regression analysis of age-related variations in LV diastolic parameters is summarized in Fig. 3, while highlighting males and females. Again, the strongest correlations were obtained for E' and for the flow-rate-related Ef/Af, Ef/FV, and Af/FV ratios. Conversely to MRI-PC diastolic parameters, left atrial endsystolic volumes increased only slightly with age and when indexed to body surface area significant differences were found only between group1 < 30 years and group5 ≥60 years.

Associations with age were adjusted for basic characteristics such as gender, BMI, heart rate, SBP and DBP (Table 3-Model 2). Resulting multivariate analysis showed that all diastolic parameters were independently associated with age. In addition, gender was an independent correlate of Ef/Af, DT, Ef/FV and IVRT; and heart rate was an independent correlate of E' and Ef/Af.

Associations of Diastolic Function with LV Remodeling

MRI-PC parameters of LV relaxation such as E', DT, IVRT were significantly associated with LV remodeling index (respectively r=-0.51, r=0.49, r=0.48, all p<0.0001) and global mid-LV wall thickness (respectively r=-0.42, r=0.53, r=0.55, all p<0.0001). Multivariate analysis (Table 3-model 3 and4) revealed that such associations were independent of age, gender, body size and blood pressure.

Discussion

This study demonstrates that a quantitative MRI-PC method can provide consistent diastolic function parameters able to independently characterize subclinical age-related variations of diastolic function in healthy volunteers. Consequently, age-related reference ranges were provided and were shown to vary significantly across age groups, highlighting the importance of using age-adapted values as standard of reference when evaluating MRI studies. In addition, LV remodeling as well as myocardial wall thickness were found to be strong independent correlates of MRI-PC diastolic parameters related to LV and myocardial relaxation, such as E', DT, and IVRT.

First, MRI measurements of LV mass and systolic function, myocardial wall thickness and left atrial volumes obtained in our healthy volunteers were reliable, as reflected by the expected trends observed with age. Indeed, the decrease in LV volumes along with the moderate increase in LV ejection fraction with age were previously described in a large population [START_REF] Cheng | Age-related left ventricular remodeling and associated risk for cardiovascular outcomes: the Multi-Ethnic Study of Atherosclerosis[END_REF]. Also, as expected, we observed a significant increase in both LV mass indexed to end-diastolic volume [START_REF] Cheng | Age-related left ventricular remodeling and associated risk for cardiovascular outcomes: the Multi-Ethnic Study of Atherosclerosis[END_REF] and myocardial wall thickness [START_REF] Cheng | Correlates of echocardiographic indices of cardiac remodeling over the adult life course: longitudinal observations from the Framingham Heart Study[END_REF] with age. Finally, in line with those previously presented [29] left atrial volumes indexed to body surface area did not vary with age.

Only few MRI studies, based on cine SSFP [30] or tagging [1; 16; 31; 32] data, evaluated age-related variations of diastolic function parameters. Indeed, Hollingsworth et al. [30] estimated LV early filling volume in relation to stroke volume from cine SSFP MRI data of 49 healthy subjects and showed its significant decrease with age. Moreover, studies based on MRI tagging showed a significant decrease with age in mean early diastolic strain and strain rate [START_REF] Cheng | Age-related left ventricular remodeling and associated risk for cardiovascular outcomes: the Multi-Ethnic Study of Atherosclerosis[END_REF], as well as an increase in the torsion to shortening ratio [30]. However, our study is the first to report a comprehensive evaluation of age-related variations of diastolic function parameters with corresponding reference ranges estimated by MRI-PC. MRI-PC -specific values measured with a standardized semi-automated method can be considered as a first step toward robust clinical evaluation of diastolic function using MRI. This a priori knowledge is crucial since the variety of reference ranges available in echocardiography cannot directly be transposed and used in MRI-PC studies. Indeed, differences between MRI-PC and echocardiographic diastolic parameters are expected, and previously shown in various studies [23; 33]. Such differences are mainly due to technical differences between the two modalities such as: 1) differences in temporal resolution which is lower in MRI-PC despite recent and continuing developments, 2) differences in measurement site for E', since echocardiography measurements are performed at the level of the mitral annulus while MRI-PC measurements are performed on a basal short axis slice. Also, in our study, MRI-PC data were acquired during breath-hold while echocardiography was commonly performed during free breathing, which could induce differences in heart rate and thus in the transmitral flow pattern.

Overall, the strength of univariate associations with age is comparable to those previously reported in Doppler echocardiography. Indeed, we found the known age-related decrease in E/A ratio [34; 35] as well as in maximal longitudinal myocardial diastolic velocity E ' [12; 14; 15]. This latter relationship with age was equivalent to those previously reported in the echocardiographic literature. Indeed, Sun et al. [15] provided reference echocardiographic values on a group of 100 healthy volunteers aged between 18 to 76 years and showed a significant decrease in early longitudinal myocardial peak diastolic velocity (E') (r=-0.64, p>0.0001). A similar trend was reported by Yamada et al. [START_REF] Yamada | Effect of aging on diastolic left ventricular myocardial velocities measured by pulsed tissue Doppler imaging in healthy subjects[END_REF] on a group of 80 healthy subjects (r=-0.61, p<0.0001). This decrease in E' with age was associated with an independent increase in LV filling pressures [36; 37], as estimated by the E/E' ratio.

Moreover, consistently with the known increase in deceleration time [38] and isovolumic relaxation time [10] with age, strong associations between MRI-PC DT as well as IVRT and age were found in our study. Furthermore, MRI-PC parameters of LV relaxation such as E', DT, IVRT were significantly associated with LV remodeling index and global myocardial wall thickness, independent of age and basic characteristics (Table 3). Such associations were in agreement with expected age-related variations in myocardial structure, as well as LV function and hemodynamics. Indeed, age-related LV concentric remodeling and stiffness as well as increase in myocardial wall thickness [START_REF] Lakatta | Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises: Part II: the aging heart in health: links to heart disease[END_REF] may result from alterations in myocardial tissue secondary to changes in myocardial extracellular matrix [39] and increased arterial afterload, and may lead to an increase in end-diastolic filling pressures [START_REF] Lakatta | Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises: Part II: the aging heart in health: links to heart disease[END_REF]. The association between cardiac remodeling and diastolic function in elder subjects [START_REF] Lakatta | Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises: Part II: the aging heart in health: links to heart disease[END_REF], even asymptomatic, may compromise cardiac reserve.

An original feature of the present study was that in addition to conventional velocity-based diastolic parameters, flow-related parameters (Ef/Af, Ef/FV) were extracted from MRI-PC resulting in stronger associations with age (Fig. 3). The reliability of such parameters might be due to the fact that flow-rate curves are less sensitive 1) to data noise than peak velocities, since they are estimated from averaged velocities and 2) to the slight mismatch between the acquisition plane and the true perpendicular to the transmitral flow. In addition flow-rate curves provided reliable and reproducible temporal LV filling parameters (DT, IVRT). The strong and independent associations between these latter parameters (DT, IVRT) and LV concentric remodeling confirm their relevance. Of note, the ability of flow-rate parameters to accurately detect diastolic dysfunction was demonstrated in a previous study on a group of 35 controls and 18 patients with a severe aortic valve stenosis [17]. This is to the best of our knowledge the first study to provide age-related reference ranges of MRI-PC diastolic parameters while evaluating their consistency by comparisons against LV remodeling and myocardial wall thickness. The addition of such reproducible and fast diastolic function evaluation to routine MRI exams already including systolic function and scar assessment would enable a comprehensive evaluation with diagnostic and prognostic implications of the LV at the expense of limited additional examination time (two breathholds).

A limitation of our study is the imbalance in subjects effectives between age groups. In particular, the number of healthy volunteers aged >70years was limited. However, it was difficult to enrol subjects without cardiovascular co-morbidities in such age range. Another limitation, to MRI acquisitions, included potential phase offset errors [40], which were not corrected in the present study but were minimized using a 50% rectangular field of view centred on the mitral annulus. Alternatively, such errors could be corrected using a region of interest positioned in stationary tissue. Also, the use of through-plane MRI-PC sequences might have induced a misalignment between blood flow jet orientation and the perpendicular to the acquisition plane. Indeed, absolute measurements would be more appropriate when using 3D MRI-PC acquisitions with retrospective valve tracking that account for the real direction of the flow and mitral annulus motion [24; 33], although such techniques require further improvements in terms of temporal [33] and spatial [41] resolutions, as well as acquisition duration.

As a conclusion, MRI-PC combined with a fast and robust automated technique resulted in consistent diastolic function parameters, which were able to characterize sub-clinical agerelated variations in LV diastolic function in healthy individuals. Practically, diastolic function assessment could be added to clinical routine cardiac MRI as a "one-stop shop" imaging modality for the diagnostic and prognostic evaluation of patients with heart failure and suspected cardiomyopathy. Tables Table 1. Baseline characteristics, LV function parameters and left atrial end-diastolic volumes for the five age groups



  Abbreviations and Acronyms

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Table 2 .

 2 MRI-PC diastolic function parameters and left atrial systolic volumes according to age group. Mean values along with ranges defined as mean ± 2 standard deviations are provided.

	20 -29 years	30 -39 years	40 -49 years	50 -59 years	≥ 60 years	Across groups p value
	E' Lateral (cm/s)


Christel Gérardin
email: christel.ducroz-gerardin@iplesp.upmc.fr

Perceval Wajsbürt

Pascal Vaillant

Ali Bellamine

Fabrice Carrat

Xavier Tannier

Multilabel classification of medical concepts for patient clinical profile identification

Keywords: biomedical concepts, multilabel classification, NER, transformers, multilingual NLP

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

The widespread use of electronic health records (EHRs) has provided access to a large amount of health data. In addition to International Classification of Disease (ICD10) coding and biological examination data, a significant amount of patient information comes from narrative records, which are unstructured data. The exploitation of unstructured data has been made possible by significant advances in natural language processing (NLP) algorithms, including new language modeling algorithms [START_REF] Pennington | Glove: Global vectors for word representation[END_REF][START_REF] Peters | Deep contextualized word representations[END_REF][START_REF] Delvin | BERT : Pre-training of deep bidirectional transformers for language understanding[END_REF]. These algorithms have proven to be very efficient in extracting information for various medical applications, including mortality prediction [START_REF] Zhang | Time-Aware Transformer-based Network for Clinical Notes Series Prediction[END_REF], cohort identification [START_REF] Soni | Patient Cohort Retrieval using Transformer Language Models[END_REF], and decision support [START_REF] Feng | Explainable clinical decision support from text[END_REF][START_REF] Shang | Pre-training of graph augmented transformers for medication recommendation[END_REF], especially in English. However, in French or other languages, efforts are still needed to reach the same level of performance.

We call a patient's phenotype the list of observable characteristics; in our case, the main pathological domain of a symptom or a disease, such as "cardiovascular" or "infections". A disorder corresponds to a disease, a pathological symptom or function. A concept is a generic name for a biomedical term or expression, such as "anuria", "fever", or "Sjögren's syndrome". The MeSH (Medical Subject Headings1 ) terminology was developed by the US National Library of Medicine and is structured like a tree with main categories A (anatomy), B (organisms), C (diseases), etc. and subcategories C01 (infections), C04

(neoplasms), etc. and finally concepts (i.e. leaves). There is a bilingual French-English MeSH version2 used in this work.

In our study, we propose an end-to-end approach to automatically extract the main classes of symptoms and diseases from clinical notes. The list of these classes of interest corresponds to the MeSH Category C (diseases) headings, such as infectious diseases, neoplasms, musculoskeletal diseases, digestive diseases, eye diseases, etc. These classes are of particular interest since they almost directly represent all medical specializations/organ types (see the complete list of classes in Table 2). These classes are called MeSH-C labels in the rest of the article; MeSH-C is the ensemble of all medical concepts in MeSH category C. The MeSH terminology has several advantages: it exists in English and French, is part of the UMLS vocabulary and contains thousands of medical concepts in a tree structure.

This automatic extraction, allowing the targeting of symptoms and pathologies specific to an organ, can be exploited for several medical applications. In the field of pharmacovigilance, it can help to detect side effects of drugs, especially on large databases, where one can automatically retrieve "ocular" or "digestive" or "infectious" disorders present in the EHR without reading any of the reports in person. In the epidemiological domain, one can also automatically extract patients with similar phenotypes, i.e., with the same type of organic lesions and select them as eligible patients for (e.g., a clinical trial or a case/control or cohort study). In clinical practice, clinicians could also analyze or extract past complications for one or more patients. For example, a rheumatologist might be interested in selecting all patients with ocular, renal or skin complications of lupus and could extract them automatically with our method. Furthermore, it is interesting to note that some diseases have multiple labels in the MeSH-C classification (for instance, Diabetes Mellitus type 1 appears in Nutritional and Metabolic Disorder (C18), Endocrine System (C19) and Immune System Diseases (C20)), making it possible to quickly detect such a disease by cross-referencing all labels.

Such examples of natural language processing for the selection of clinical trial cohorts [START_REF] Chen | Clinical trial cohort selection based on multi-level rule-based natural language processing system[END_REF] or pharmacovigilance studies [START_REF] Bayer | ADE Eval: An Evaluation of Text Processing Systems for Adverse Event Extraction from Drug Labels for Pharmacovigilance[END_REF] have already been proposed but were task specific.

We see this classification problem as the task of finding concept mentions in the texts. If a MeSH-C concept is found in the textual report and if this concept is not negated, hypothetical, or related to someone other than the patient, then we consider that the patient can be labeled by that concept and, thus, by the associated class.

The MeSH terminology category C contains thousands of concepts. It is not possible to find a corpus containing all these concepts. A fully supervised learning strategy is therefore impossible. For this reason, it is necessary to use the terminology itself and the lists of terms associated with the classes to guide the system.

In this article, we focus on French texts. Healthcare reports related to patient care are and will always be written in the local languages of each country; therefore, it is crucial to ensure that advances in artificial intelligence are not limited to English documents. However, this raises additional challenges due to the much more limited resources existing in languages other than English [START_REF] Névéol | Clinical natural language processing in languages other than english: opportunities and challenges[END_REF], whether in terms of available corpora, thesaurus coverage or availability of pretrained language models.

For this reason, we experimented with different approaches to take advantage of English terminologies and the latest multilingual embedding models.

Our work on this end-to-end classification system for French clinical documents leads to several contributions:

-We trained a named entity recognition system to produce candidate terms for MeSH-C classification; this system is able to discard negated or hypothetical occurrences of concepts, as well as those not related to the patient.

-We used available terminology resources in English and French to reduce the need for annotated data while maintaining good generalizability. The system does not depend on the nature of the documents or on the objective of the final task (e.g., cohort extraction, pharmacovigilance study).

-In the recent dataset DEFT 2021, the first annotated corpus for French MeSH classification [START_REF] Grouin | Classification de cas cliniques et évaluation automatique de réponses d'étudiants: présentation de la campagne DEFT 2021 (Clinical cases classification and automatic evaluation of student answers: Presentation of the DEFT 2021 Challenge)[END_REF],

we show that our approach leads to good results even without any labeled data for the classification step. This leads to similar results to those obtained with manually optimized handcrafted rules for the DEFT dataset [START_REF] Hiot | DOING@DEFT : utilisation de lexiques pour une classification efficace de cas cliniques[END_REF].

-We also compare the contribution of multilingual versus monolingual models and resources. 3In the next section, we detail the different sets of documents and terms used to train our model and then describe the different steps of the pipeline: model overview, named entity recognition algorithm, gender classification and multilabel classification.

Material

DEFT 2021 dataset

The DEFT 2021 dataset [START_REF] Grouin | Classification de cas cliniques et évaluation automatique de réponses d'étudiants: présentation de la campagne DEFT 2021 (Clinical cases classification and automatic evaluation of student answers: Presentation of the DEFT 2021 Challenge)[END_REF] consists of 275 clinical cases annotated, among others, with:

-the mention of the sign or symptom and disease type entities -the characteristics associated with these mentions (e.g., negation, hypothesis, link with someone other than the patient).

-for some of these mentions, the MeSH-C labels were annotated in association with the symptom and disease annotation. Table 1 shows the entire list of possible labels.

-at the document level, an aggregation of these MeSH-C labels (list of all labels occurring at least once in the document).

Figure 1 provides a concrete understanding of all these annotations.

The objective of the task is to perform phenotyping for each case, i.e., to determine the clinical profile of the case by extracting the pathological features described by the MeSH C chapter headings. Table 2 shows the number of documents and words in the dataset, with the split between training and test datasets provided by the challenge organizers. (highlighted), there is an entity label "disease", "sign or symptom" and the negation/hypothesis/link to someone else attribute. Each of the positive entities can be mapped to several MeSH-C chapter headings (corresponding to the "Mention-level MeSH-C label", i.e., the label for each concept). For instance, the extracted mention "myeloma" is labeled with the labels "tumor", "immune" and "hemic". The patient-level MeSH-C labels (bottom) are the labels that we seek to predict for each original text. Annotations from this training DEFT set will be used to train the named entity recognition NER algorithm, train the multilabel classifier and train the gender classifier (steps 1, 2, and 3 in Figure 3). 1. The thumbnail represents the number of labels per document.

MeSH-C level

Terminological resources (term sets)

Due to this unbalanced distribution and the small volume of the DEFT 2021 training dataset, we also used terms related to MeSH-C from the UMLS terminology. From now on, we will refer to this resource as the term set. The Unified Medical Language System ® (UMLS ® ) brings together three knowledge sources: a metathesaurus, a semantic network and a specialist lexicon and lexical tools. In this work, we only worked on the metathesaurus that unifies concepts from more than 200 vocabularies in the biomedical domain [START_REF] Bodenreider | The Unified Medical Language System (UMLS): integrating biomedical terminology[END_REF]. A concept is an entry of a particular terminology and corresponds to a specific notion of this terminology. Each concept is mapped to one or more terms (or synonyms), possibly in different languages. A unique concept identifier (CUI) is assigned to each concept in the UMLS. For example, the MeSH concept "Breast Neoplasms" (from branch C04 -tumors) is associated with the terms "breast carcinoma", "breast cancer", "mammary carcinoma", "cancer du sein" (French), etc. This MeSH concept is also mapped to its equivalent UMLS concept "Breast Carcinoma" (C0678222), which can lead to other terms from other terminologies.

To obtain synonyms to augment our training term set, we first retrieved the concept unique identifier (CUI) of the MeSH-C terms from the UMLS and then extracted all synonyms related to the CUI in French and English. A complete list of all ontologies used to construct our training term sets can be found in Appendix 1. The bilingual databases were built using PymedTermino2 [START_REF] Lamy | PyMedTermino: an open-source generic API for advanced terminology services[END_REF], a Python package that provides easy access to key medical terminologies. We also experimented with an automatic machine translation into French from English terms. For this, we used a state-of-the-art pretrained translation system "opus-mt-en-fr" [START_REF] Tiedemann | OPUS-MT-building open translation services for the world[END_REF] from the Hugging face library [START_REF] Wolf | Transformers : State-of-the-art natural language processing[END_REF].

These term sets will be used to train both the monolingual and multilingual multilabel term classifiers (step 3 in Figure 3). Table 3 lists all the term sets used, along with the model they trained, synthesizing the three main approaches described above:

• French only (FR): the set of terms in the DEFT dataset and all the French UMLS vocabularies listed in Appendix 1 mapped to MeSH terms.

• multilingual with French and English terms (FR-EN): all terms from the DEFT dataset terms and all the French and English UMLS vocabularies mapped to MeSH terms.

• French terms and translated English terms (FR-tr): the same as the previous set but with all the English terms translated. vocabularies mapped to MeSH terms. For the "English translated and French synonyms", the same English terms were translated. All models mentioned will be described in Section 3.

Multilabel

Methods

System overview

Figure 3 describes the general architecture of the proposed system. First, a named entity recognition system extracts mentions of the entities: "disease" and "sign or symptom (sosy)" (step 1 in Figure 3).

We consider these entities as clues for MeSH-C labels at the patient level. From these mentions, we discard:

-concepts that are negated, hypothetical or associated with someone other than the patient;

-concepts corresponding to negative outcomes (e.g., normal exam, negative analysis).

For this system, we merge the entities "disease" and "sosy" into one to reach the entities to be extracted: "sosy disease", "sosy disease absent" (i.e., negated), "sosy disease hypothetical", and "sosy disease non associated" (i.e., relative to another person). This merger is justified, in our opinion, by the semantic proximity of the two entities. Indeed, in MeSH-C, many terms are found in both categories. For example, "amnesia", "amblyopia", and "hearing loss" are cited both in the section "Diseases of the nervous system" and in the section "Pathologic conditions, signs and symptoms". This fusion also has the advantage of grouping the syntactic contexts related to negation, hypothesis, and family medical history to ensure better learning of these non trivial notions. We will show in Section 4 that this assumption is also supported by preliminary results obtained by our NER algorithm, which performed better with than without this fusion step.

In addition, MeSH Chapters C12 (female urogenital diseases) and C13 (male urogenital diseases) can sometimes be distinguished only by the gender of the patient (for example, anuria, adrenal tumor, pyelonephritis). Therefore, it is necessary to build a classifier that predicts the gender of the patient from the content of the report (step 2 in Figure 3).

Once the terms of interest are extracted by the system, a classifier predicts the MeSH-C chapters related to each term (step 3). This is, thus, a multilabel classifier (each term can be labeled by none, one or several of the 22 classes represented in the dataset, aggregating female and male in urogen). We trained this classifier with the annotated terms of the DEFT training dataset but also with the FR, FR-EN or FRtr term sets described in Section 2 based on our experiments.

Finally, we aggregate the extracted term-level information to predict document-level classes.

The following sections detail each of these steps. MeSH-C labels are aggregated at the document level for each patient observation (4).

Named entity recognition (NER)

The named entity recognition model is illustrated in Figure 4. The model exhaustively keeps scores of all possible spans before prediction; it consists of a BERT transformer [START_REF] Delvin | BERT : Pre-training of deep bidirectional transformers for language understanding[END_REF], which has become a standard way to represent the textual input of a neural network, followed by a bidirectional long short-term memory LSTM [START_REF] Hochreiter | Long Short-Term Memory[END_REF], similar to the method in [START_REF] Yu | Named Entity Recognition as Dependency Parsing[END_REF]. The extracted spans are triplets (begin, end, label).

Each word in the text is first split into word pieces and passed through the transformer. The representations of the last 4 BERT layers are averaged with learnable weights, and the word pieces of a word are max-pooled to build its representation. Char-CNN encoding [START_REF] Lample | Neural Architectures for Named Entity Recognition[END_REF] of the word is concatenated to the max-pooled representation to obtain the word representation.

These word representations are passed through a three-layer highway LSTM with learnable gating weights [START_REF] Jaeyoung | Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition[END_REF]. We apply the sigmoid function to obtain probabilities. During the prediction, we select the triplets (begin, end, label) that have a probability greater than 0.5.

The model is trained via a binary cross-entropy objective with the Adam optimizer [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF]. We use a linear decay learning rate schedule with a 10% warm-up and two initial learning rates: 4.10 -5 for the transformer and 9.10 -3 for the other parameters.

Gender Classification

All DEFT documents were labeled with gender. To train a classifier to determine gender, we extracted a large number of candidate features and assessed their relevance. An observation of the documents first determined that in the vast majority of cases, in this type of document, the information describing the patient is found in the first sentence. Therefore, we weighted the variables by their distance from the beginning of the text (according to a weighting function of the order number of the sentence in the document, starting at 1 for the first sentence and decreasing linearly to 0.5 for the last). We then identified the variables that seemed significant during a first qualitative survey of the training corpus.

The most significant feature is (1) the gender of the word patient (in French, "patient" is a male while "patiente" is a female). The other significant features are, in order of importance: (2) the gender of adjectives applied to humans; (3) The number of occurrences of morphemes referring to sex-specific biological or medical concepts (e.g., peni-, uter-, testi-, vagin-, with a list built from MeSH terms, made available with the code; (4) The gender of civil honorifics ("M. ", " Mr", "Mr"), (" Ms", " Ms. "); [START_REF] Zhang | Time-Aware Transformer-based Network for Clinical Notes Series Prediction[END_REF] The gender of common nouns frequently used to designate a human individual (woman, man, child...);

(6) The gender of personal third-person singular pronouns used in the text; [START_REF] Feng | Explainable clinical decision support from text[END_REF] The explicit indication of gender.; and (8) the gender of first names, determined from an INSEE5 reference list of the most frequently given first names in France and of the associated gender.

We extracted the morphosyntactic categories (POS, gender, number) and the syntactic dependencies using the stanza library [START_REF] Qi | Stanza: A Python natural language processing toolkit for many human languages[END_REF].

We trained a supervised classifier, AdaBoost [START_REF] Freund | A decision-theoretic generalization of on-line learning and an application to boosting[END_REF], based on these data to determine the gender prediction function from a text document.

To validate this approach, we trained the classifier on 80% of the provided training data and validated it on a 20% set.

Multilabel classification

We perform a preliminary filter on the NER output to remove the physiological findings (normal exam, negative analysis6 ). Indeed, these items are often annotated as sosy in the DEFT dataset but should not result in a MeSH-C annotation, since MeSH-C classification focuses only on pathological information.

For example, "negative HIV serology" or "normal cardiovascular examination" were annotated as "signs and symptoms" in the DEFT dataset. These terms do not correspond to a pathological condition or disease and therefore do not belong to the MeSH-C classification, thus needing to be removed. This filtering is provided by simple regular expressions. An example of this filtering is shown in Appendix 2. This is a minor step different from the negation detection performed by the NER step.

The MeSH-C classification model consists of a pretrained transformer [START_REF] Vaswani | Attention is all you need[END_REF] including a final linear output layer. We used either BERT embeddings [START_REF] Delvin | BERT : Pre-training of deep bidirectional transformers for language understanding[END_REF] trained on French data only (CamemBERT [START_REF] Martin | CamemBERT : a tasty French language model[END_REF],

model camembert-large) or a multilingual "bert-base-multilingual-cased", both from the HuggingFace library [START_REF] Wolf | Transformers : State-of-the-art natural language processing[END_REF]. To enable a multilabel classification, the loss function is the binary cross-entropy, summed over all classes. We used an Adam optimizer [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF] with a linear decreasing training step, starting at 1.10 - 5 . For the prediction, the scores are calculated by the sigmoid function as output.

We used the terminology training sets shown in Table 3 to have the classifier learn to map each entity extracted by NER to its label(s).

We used a 20% validation set (see next section) to choose the best number of epochs and the logit threshold above which a class is positive. The threshold retained for the final prediction maximizes the precision score on the validation set, which leads to better preliminary results. This metric is preferred to the F1 score because the document-level step (step 4 in Figure 3) aggregates possibly redundant information, which mechanically increases recall.

Validation set

Given the unbalanced representation of each label in the DEFT training dataset (see Figure 2), we chose to build the validation term set with the same class distribution as the DEFT training dataset (as opposed to a random selection which would have led to a distribution similar to the classes inside the UMLS, i.e., unrepresentative of the real documents). Once the best model is selected and the threshold is computed on the validation term set, we use a last step of fine-tuning the classification model for 10 epochs on the validation term set. This last step enables the model to "see" the whole vocabulary at least once.

Experimental setups

Our set of experiments aims to show how the volume and the language of the terminologies used influence the results. Thus, we propose the results of the system trained on the three term sets described in Section 2.1 (i.e., "FR", "FR-EN", "FR-tr"). For the bilingual FR-EN training term set, we compare two pretrained embeddings: the French CamemBERT model (camembert-large) and a multilingual BERT (bert-multingual-base; note that there is no "large" multilingual BERT available).

We also compare our results to those of other DEFT participants: a system based on a list of terms manually curated specifically for the DEFT dataset [START_REF] Hiot | DOING@DEFT : utilisation de lexiques pour une classification efficace de cas cliniques[END_REF], a direct multilabel classification system, i.e., taking the entire text as input, without using the intermediate notion of concept mention [START_REF] Billami | Participation de Berger-Levrault (BL.Research) à DEFT 2021 : de l'apprentissage des seuils de validation à la classification multi-labels de documents[END_REF].

Finally, we performed ablation studies to estimate the impact of the different steps in our system:

• As a gold standard reference for the NER model, the DEFT organizers provide the annotations for the entity types "disease" and "sosy'' in the test dataset, enabling us to assess separately the NER performances. For each experiment, we then add a run called "gold mentions", which uses gold standard named entities instead of the step 1 NER system.

• We also provide results without the final fine-tuning on the validation set ("no FT").

• Finally, we removed part of the FR-EN term set from the DEFT training dataset to show the results obtained in an unsupervised setup (i.e., only terms from terminologies, none from a human annotation). We called this run "FR-EN no DEFT". We evaluated our system using three scores for training, validation and test performance: microprecision, microrecall and micro-F1 score, the most common metrics for multilabel classification.

All scores presented in this paper are the average of 5 runs performed with different random seeds to mitigate the effect of initialization and training order.

We also provide carbon footprint estimates for each configuration, as provided by the CarbonTracker tool [START_REF] Wolff Anthony | Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models[END_REF].7

Results

The results are presented in Table 4, where our main runs constitute the runs with our "end-to-end" algorithm: our NER system associated with different classifiers. For each different experimental setup, we show the average score results over 5 runs. Our best results are obtained with the bilingual approach with an F1 score of 0.811 for NER extraction and an F1 score of 0.819 for Gold mentions. The last finetuning step improves the F1-score by 1.1 percentage points on average over the 5 experiments (from +0.007 to +0.016).

Note that the threshold selected for classification from the sigmoid output was almost always the same (0.99), which is a good outcome for the robustness of the system.

We also evaluated the performances of intermediate steps 1, NER and 2, gender classification. The NER system detects the "disease, sign and symptom" mentions, excluding the negation, hypothesis and information not related to the patient, with a precision of 0.93 and a recall of 0.88 (F1 score: 0.90). As mentioned in Section 3.1, the NER system detected the merged mentions of "sign and symptom" and "disease", improving the F1 score by 0.1 on the validation set. The gender classification obtains a perfect score (no error).

For the DEFT challenge, we initially only used a restricted term set for the classifier, containing only the name of each concept in French, without synonyms, leading to a training term set of 9,363 terms.

The official results obtained were F = 0.770 with our NER extraction and a CamemBERT-large classifier and F = 0.775 with the Gold mentions.

We also compared the different carbon footprints: interestingly, the multilingual BERT with 110 million parameters has a much lower approximate carbon footprint than the CamemBERT-large, which has a total of 340 million parameters to train. We also see that the carbon footprint is directly related to the size of the training term set, even though the number of training epochs required is higher for smaller term sets. Examples of multilabel misclassification are shown in Appendix 3 with the model trained on the translated terms (FR-tr). Examples of erroneous results include "fever at 39.1 degrees C" mislabeled "infections" (Line 1); "ureteral valve in the form of an endoluminal transverse fold… " mislabeled "cardiovascular", most likely because of the terms "valve" and "endoluminal" (Line 2); and "proliferation index assessed by anti-ki67 antibodies is high" mislabeled "immune", most likely because of the term "antibodies".

Recall

In addition to the expected label-level results for the shared task corresponding to the objective of our work, we also calculated the number of patients in the dataset for whom we were able to correctly assign all labels. These results range from 32.3% with the worst of our model to 46% with the best.

These results are to be expected given the large number of labels to be found in a case (see Figure 2).

Table 5 shows the results for each class with our best model (i.e., multilingual BERT on the French and English term sets). We can see that our system gives homogenous results from one class to another even if the initial distribution is very heterogeneous.

Recall

Precision F1

Discussion

Although the differences between the four main runs are not very high, it is interesting to note that the joint use of terms in both languages with multilingual embeddings is the most efficient. It is particularly noteworthy that a monolingual space with translated terms performs worse than a multilingual space. This is especially true since the French model used is a "large" model (340 M parameters), while the multilingual model is a "base" model (110 M parameters). The large models generally outperform the base models in almost all tasks.

Comparison of the different experiments

The results obtained by the multilingual version show that our method could easily be adapted to any other similar language to obtain better performance by taking advantage of the large vocabularies of biomedical concepts of UMLS in English.

It is encouraging to see that the unsupervised setup ("FR-EN No DEFT") leads to an acceptable F1score of 0.75, showing that it is possible to obtain reasonable results without any annotated data. This observation is also reinforced by the fact that the results per class are relatively similar, with few exceptions, as shown in Table 5. This would not have been the case in a classical supervised learning approach, where an expected result is that underrepresented classes obtain much worse results than the others.

It is also interesting to observe that the use of gold-standard mentions (experiments "Gold-mentions") increases the overall results by only a small margin. The NER results are not perfect (F1 = 0.90), but this small difference can be explained by the fact that the redundancy of mentions in a document can help erase some NER errors through the document-level aggregation step.

Finally, our best models lead to results very similar to those of hand-curated terminology matching [START_REF] Hiot | DOING@DEFT : utilisation de lexiques pour une classification efficace de cas cliniques[END_REF] 8 , with a much better generalization potential. In that study, the authors used the MeSH lexicon and manually processed this lexicon, removing terms leading to false negatives and positives in the training corpus. Our training resources can be built quickly by a few queries in the UMLS database without correction, which makes our approach easily adaptable to other classes or languages. As we have shown, it can even run with decent performance without any annotated data, while they are needed for curating a terminology through a trial-and-error methodology.

Because our algorithm is based on the MeSH-C classification, we had to determine the gender of the patient as an intermediate step. This has two major drawbacks. First, gender as a social construct is used to determine a biological trait. Second, it does not address intersex or transgender urological or gynecological issues and may lead to sexual reductionism, as described in [START_REF] Hamidi | Gender Recognition or Gender Reductionism ? The Social Implications of Embedded Gender Recognition Systems[END_REF].

In other experiments, inspired by the performance of the BioBERT [START_REF] Lee | BioBERT: a pre-trained biomedical language representation model for biomedical text mining[END_REF] and clinicalBERT [START_REF] Alsentzer | Publicly available clinical BERT embeddings[END_REF] models, we tried to fine-tune the CamemBERT-large language model on the 4,000 French open access biomedical articles on EuropePMC 9 , but this did not result in major improvements. This is probably because the CamemBERT-large model is already trained on a large volume of heterogeneous data.

Moreover, the volume of 4,000 articles was probably insufficient to allow a real contribution to the model. Unfortunately, as with most languages except for English, there are often too few accessible biomedical resources available to improve performance, which justifies the need to use multilingual models.

This work enables one to automatically detect medical categories from clinical narratives. The next step of this work will be to directly create a representation of the patient from the embeddings of the labeled terms. For instance, in the case of a text explaining that a patient with glaucoma has the flu, the labels with our algorithm would be 'eye' and 'infection', and a relevant representation of the patient would be the concatenation of the 'glaucoma' and 'flu' embeddings. This representation can lead to a finer phenotyping of the patient and enables, for example, computation of the similarity of patients. This representation is inspired by the "Deep-Patient" model [START_REF] Miotto | Deep patient: an unsupervised representation to predict the future of patients from the electronic health records[END_REF], except that our features are based on transformer embedding and filtered by a classification algorithm.

Comparison with previous work

As mentioned above, the extraction of the main pathological characteristics of a clinical case corresponds to a phenotyping of the patient. In recent years, several studies have been carried out on the phenotyping of patients from the EHR.

Gerhmann et al. [START_REF] Gehrmann | Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives[END_REF] compared deep learning-and concept extraction-based methods for patient phenotyping in English. More precisely, they assess the performance of convolutional neural networks for narrative-based patient phenotyping, comparing it to cTAKES (Mayo clinical Text Analysis and Knowledge Extraction System) [START_REF] Savova | Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications[END_REF] to predict 10 disorders. They obtained an improvement of the F1 score ranging from 2 to 26 points (except for one disorder).

Yang et al. [START_REF] Yang | Combining deep learning with token selection for patient phenotyping from electronic health record[END_REF] proposed a method combining a CNN-based deep learning neural network and natural language processing to predict ten disorders from English clinical narratives. The CNN processes inputs at the word and sentence levels. Similar to our approach, the authors used different sample sizes of the

Figure 1 :

 1 Figure 1: Annotations provided in the DEFT 2021 corpus. For each medical concept of interest

Figure 2

 2 Figure 2 shows the distribution of labels in the training dataset for illustrative purposes. The label path_sosy (Pathological Conditions, Signs and Symptoms) appears in 141 texts, while stomatognathic is present in only 3 texts. The number of labels per document is also presented (median = 3).

Figure 2 :

 2 Figure 2: Distribution of labels in the DEFT training dataset. The y-axis represents the number of documents, and all labels presented are listed inTable 1. The thumbnail represents the number of

Figure 3 :

 3 Figure 3: General system architecture. First, named entity recognition is performed, trained on the annotated DEFT dataset to extract positive medical concepts (1). In parallel, the gender classifier, also trained on the DEFT dataset, determines the written gender of the patient (2). Then, a multilabel classifier assigns a MeSH-C label to each extracted term (3). This multilabel classifier is trained with DEFT annotations and French and English UMLS vocabularies mapped to MeSH-C terms. Finally, all

Figure 4 :

 4 Figure 4: NER system architecture. Each word is projected into nlabels to begin representations and nlabels to end representations. Finally, each triplet (B, E, L) is scored as a dot product between the begin representation of label L at position B and the end representation of label L at position E.

Chapter name Label

	C20	Immune System Diseases	immune
	C21	Disorders of Environmental Origin	(missing in the dataset)
	C22	Animal Diseases	(missing in the dataset)
	C23	Pathological Conditions, Signs and Symptoms	path_sosy
	C24	Occupational Diseases	(missing in the dataset)
	C25	Chemically Induced Disorders	chemical
	C26	Wounds and Injuries	injuries
	C01	Infections	infections
	C04	Neoplasms	tumors
	C05	Musculoskeletal diseases	musculoskeletal
	C06	Digestive System Diseases	digestive
	C07	Stomatognathic Diseases	stomatognathic
	C08	Respiratory Tract Diseases	respiratory
	C09	Otorhinolaryngologic Diseases	ENT
	C10	Nervous System Diseases	nervous
	C11	Eye Diseases	eye
	C12	Male Urogenital Diseases	male_uro *
	C13	Female Urogenital Diseases and Pregnancy Complications	female_uro *
	C14	Cardiovascular Diseases	cardiovascular
	C15	Hemic and Lymphatic Diseases	hemic
	C16	Congenital, Hereditary, and Neonatal Diseases and	congenital
		Abnormalities
	C17	Skin and Connective Tissue Diseases	skin
	C18	Nutritional and Metabolic Diseases	nutritional
	C19	Endocrine System Diseases	endocrine

Table 1 :

 1 List of MeSH-C descriptive headings and the short names used in this paper4 . * male_uro and female_uro are grouped together into a urogen class in our first-step classification.

		Number of documents	Number of words
	Training dataset	167	57,174
	Test dataset	108	34,258
	Total	275	91,432

Table 2 :

 2 DEFT 2021 corpus statistics.

classifier training term sets (number of term/ label couples) Model trained

	French synonyms (FR)	42,912	camemBERT
	English and French synonyms (FR-EN)	308,043	camemBERT
			and
			multilingual
			BERT
	English Translated and French synonyms (FR-tr)	209,145	camemBERT

Table 3 :

 3

Different term sets used for training the multilabel classifier in our experiments. "French

synonyms" correspond to the DEFT dataset annotated terms, and all French UMLS vocabularies correspond to MeSH terms. For the "English and French synonyms" set, we added English UMLS

Table 4 :

 4 Results of our different experimental setups. The names of the runs are detailed in the previous section. "Gold mentions" uses gold standard named entities (i.e., manually annotated) instead of the step 1 NER system. "No FT" corresponds to the results without the final fine-tuning on the validation set.

			Carbon
	Precision	F1	footprint
			(eq CO2)

Table 5 :

 5 Results class by class with the best model.

https://www.nlm.nih.gov/mesh/meshhome.html

http://mesh.inserm.fr/FrenchMesh/

The code for all experiments described in this paper is available at the following URL: https://github.com/xtannier/MeSH-C_classification

To rely on the latest version of the NIH MeSH, we merged the three classes "infectious disease", "viral disease" and "parasitic disease" into one, which was not the case in the DEFT 2021 challenge. The results and comparison with other participants are still possible since the DEFT test dataset only contained 4 "viral" terms and 1 "parasitic" term. In any case this difference led to an underestimation of our results.

https://www.insee.fr/fr/statistiques/2540004?sommaire=4767262

This is different from negated or hypothetical concepts, in which processing is included into the supervised NER system as described in Section 3.1.

Note that these estimates remain very approximate, taking into account neither the execution environment nor the method of energy production at the place of the experiments. CarbonTracker computes its estimates by using the average carbon intensity in the European Union in 2017.

To rely on the latest version of the NIH MeSH, we merged the three classes "infectious disease", "viral disease" and "parasitic disease" into one, leading our results to be underestimated when compared to the original benchmark. However, with only 5 occurrences of "parasitic disease" and "viral disease" in the test set, this underestimation is marginal.

training dataset. The authors also used word2vec [START_REF] Mikolov | Linguistic regularities in continuous space word representations[END_REF] word embeddings. The obtained results range from an F1 score of 63% for "Chronic Pain" to 86% for "Depression".

Aside from the other participants in the DEFT 2021 challenge, no other articles have the exact same objective. However, Weng et al. [START_REF] Weng | Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach[END_REF] proposed a classification of clinical notes into medical subdomains.

Their Natural Language Processing (NLP) pipeline is based on cTAKES [START_REF] Savova | Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications[END_REF] and on the UMLS metathesaurus. The best performing algorithm was a convolutional recurrent neural network with neural word embeddings (fastText [START_REF] Bojanowski | Enriching word vectors with subword information[END_REF]), with AUCs of 0.975 and 0.995, respectively, for each of their datasets, and F1 scores of 0.845 and 0.870. Using two different datasets, the overall prediction portability from one dataset to another gave an F1 score of 0.7.

Compared to the abovementioned studies, the originality of our work lies in the fact that our classification is not as broad as the medical subdomain classification task or as narrow as the disease classification task but rather in between, enabling the rapid detection of pathological characteristics with good performance in the French language using a multilingual system. [START_REF] Wajsbürt | Medical concept normalization in French using multilingual terminologies and contextual embeddings[END_REF] shares the same objective of exploring the possibility of combining multilingual resources in the same space for concept classification. Their application task is different, but their conclusions on this topic align with ours: they also found that a multilingual approach performs better than a translated approach and constitutes a good alternative for languages other than English. However, the variety of English models remains much higher, especially for the sciences and medical fields (BioBERT [START_REF] Lee | BioBERT: a pre-trained biomedical language representation model for biomedical text mining[END_REF], clinicalBERT [START_REF] Alsentzer | Publicly available clinical BERT embeddings[END_REF]), and annotated data remain massively more important in English, thus requiring NLP in other languages to continue to progress.

Conclusion

In this work, we proposed a multilabel classification of clinical narratives with all the headings of MeSH-C chapters, leading to a 22-label classification with good performance. This multilabel classification allows rapid extraction of the pathological domain for the phenotyping of patients. We tested several vocabularies to train our classifiers. Interestingly, our bilingual approach with UMLS English and French vocabularies leads to the best results, suggesting that our method could be used for any other similar language.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.