Napthaline Gerard

Sarah Ben Othman

Pajanivel Rangandin

Marc Broucqsault

Slim Hammadi

Symbolic Artificial Intelligence to diagnose Tuberculosis using Ontology

Keywords: PTB diagnosis, ontology, domain ontology, knowledge base

Pulmonary Tuberculosis (PTB) is an infectious disease caused by a bacterium called Mycobacterium tuberculosis. This paper aims to create Symbolic Artificial Intelligence (SAI) system to diagnose PTB using clinical and paraclinical data. Usually, the automatic PTB diagnosis is based on either microbiological tests or lung X-rays. It is challenging to identify PTB accurately due to similarities with other diseases in the lungs. X-ray alone is not sufficient to diagnose PTB. Therefore, it is crucial to implement a system that can diagnose based on all paraclinical data. Thus, we propose in this paper a new PTB ontology that stores all paraclinical tests and clinical symptoms. Our SAI system includes domain ontology and a knowledge base with performance indicators and proposes a solution to diagnose current and future PTB also abnormal patients. Our approach is based on a real database of more than four years from our collaborators at Pondicherry hospital in India.

Introduction

Pulmonary Tuberculosis (PTB) is a disease caused by bacteria called Mycobacterium Tuberculosis [START_REF]Tuberculosis fact sheet[END_REF]. It is estimated that over one-quarter of the world's population has been infected with Mycobacterium tuberculosis rivaling the impact of HIV/AIDS [START_REF]Global tuberculosis report[END_REF]. TB disease in the lungs or throat is infectious and can be contagious. TB in other parts of the body, such as the kidney or spine, is usually not contagious. There are many types of testing to diagnose TB namely Symptom screening, Chest Xray, etc. [START_REF]Diagnostics for pulmonary tuberculosis[END_REF]. It is challenging to diagnose TB to date in a hospital because most lung diseases have similar clinical symptoms, and the early stage of TB is not specific. It is very difficult for doctors to identify the issues due to various similar lungs disease, initially diagnosis takes a long time for each patient.The automatic TB diagnosis system is developed to reduce the burden, reduce time and increase accuracy. Still, the automatic diagnosis of TB is one of the most essential and difficult health problems in the real world. Therefore, early diagnoses of TB are very important to effectively treat patients before getting high levels of risk. Timely diagnosis and initiation of therapy are also affected by patient delay and health system delays [START_REF] Sreeramareddy | Time delays in diagnosis of pulmonary tuberculosis: a systematic review of the literature[END_REF]. Data insufficiency may affect the accuracy of diagnosis on patients. Thus, how to train a robust TB diagnosis model using insufficient data is a more challenging task. Fortunately, clinical knowledge is composed of relationships between different medical events (i.e., symptoms or diseases). The introduction of clinical knowledge into diagnosis provides additional information. So, taking these relationships into account improves the reliability and accuracy of the diagnosis results [START_REF] Choi | Graphbased attention model for healthcare representation learning[END_REF].

To address these issues, we present an SAI system that contains a domain ontology with a diagnostic module and a knowledge base. In this context, we propose domain ontology to establish a biological diagnosis system for PTB to avoid data misclassification. In the literature, there are some existing ontologies for domain knowledge, knowledge base, and diagnosis such as Diabetes ontology (DO) for diabetes disease diagnosis for case base knowledge [START_REF] El-Sappagh | A fuzzy ontology modeling for case base knowledge in diabetes mellitus domain[END_REF]. The existing TB ontology is a draft ontology for TB clinical data that had already been developed by the clinical data interchange standards consortium. They generated a set of terms and their definitions related to TB diagnosis and treatment based on usage in various TB databases and from several TB studies. This work was later completed under the critical path to TB drug regimens initiative [START_REF]The critical path to TB drug regimens[END_REF]. To the best of our knowledge, there is no complete TB diagnosis ontology already published. Thus, the proposed ontology is a novel approach to diagnosing PTB using clinical and paraclinical data for accurate and original results. In addition, our ontology acts as an intelligent system using logical expression and reasoning as an SAI system. We conduct this work as an international project among Mahatma Gandhi Medical College & Research Institute, Pondicherry-India, and Lille university -France.

Methods

Symbolic AI solves the problem by using logical thinking rules, and reasoning. The reasoning and the rules created through human intervention also refer to humanreadable and explainable processes. Symbolic AI contains domain ontology, knowledge base, and diagnostic modules are shown below (Figure 1). It is very important to choose the right methodology for the new ontology. To define the domain concepts, we use the standard medical manual of PTB from India to build a healthcare domain ontology. The domain ontology in PTB also converts our raw data into structured data. PTB specialists have validated the PTB ontology and each element defined and the relations between classes, then we describe all its elements with the OWL 2 language in ontology form. Table 1 presents an overview of the properties defined in PTB ontology.

Knowledge base

A knowledge-based system (KBS) is a form of artificial intelligence (AI) that aims to capture the knowledge of human experts to support decision-making. The techniques are defined by explicit symbolic methods, such as formal methods and programming languages, and are usually used for deductive knowledge [8]. It is usually associated with knowledge bases and expert systems and is a continuation of the von Neumann and Turing machines [START_REF] Benderskaya | Multidisciplinary trends in modern artificial intelligence: Turing's way[END_REF]. In particular, rule-based systems have the advantage of rule modularity, as the rules are discrete and autonomous knowledge units that can easily be inserted or removed from a knowledge base [START_REF] Hatzilygeroudis | Neuro symbolical approaches for knowledge representation in expert systems[END_REF]. Finally, the maintenance of rule bases is difficult as it requires complex verification and validation.

After creating the domain ontology, we define the reasoning model to realize a functional ontology and integrate it into a decision-support system. We use the SWRL (Semantic Web Rule Language), a rule language for the semantic web, combining the OWL-DL language and RuleML (Rule Markup Language) to create a range for each test for all possible scenarios of the PTB. These are integrated into the resulting ontology to reason semantically based on rules, for example:

PTBontology:Patient_ID(?X)^has_Gender(?Z,Female)^has_CPK_MB_creatine_kinase_myocardial_ban d(?X,?Y)^swrlb:greaterThan(?Y,"26"^^xsd:integer)^swrlb:greaterThan(?Y,"192"^^xsd:integer) -> Normal(?X)

In addition, this model provides rules that assess abnormal cases and TB cases by proposing solutions to help healthcare workers. Thus, each rule is described with the properties and instances defined in the ontology. It is based on performance indicators to give an accurate assessment. We also categorized each paraclinical test into three categories: average range, above average, and below average. The defined rule base has been validated by the medical PTB specialist. The exploitation of semantic rules can also be used to manage missing data.

Results

To implement the PTB domain ontology, we used Protégé 5.5 (Figure 2). It is an opensource software [START_REF] Liu | SOFERS: scenario ontology for emergency response system[END_REF] that provides a plug-and-play environment for rapid application development. Protégé is the most familiar tool for developing ontology. Developers can create the ontology easily through its graphical interface without having to worry about the syntax language. Thus, we integrate the SWRLTAB tool into protégé for creating and exploiting SWRL rules. The resulting ontology contains 250 classes, 38 object properties, 167 data properties, and 678 patients. Each paraclinical test is stored in the form of object property and data property. Our PTB ontology system is shown in figure 2. This is a visual representation of ontology in protégé. The SWRL rules that are created by the SWRLTAB and Protégé tools are generated with the inference engine. The rule base contains 519 semantic rules (Figure 3).

Discussion

Ontology evaluation requires formal and appropriate evaluation criteria. Therefore, there is no standard TB diagnosis ontology to measure its similarity to our ontology. Ontologies in the same domain are necessary, but in the literature, there is no complete ontology to diagnose Pulmonary Tuberculosis using all the paraclinical tests already published and which we could use. Our system contains eight categories of paraclinical tests, containing each different test type. In total there are 173 tests. By evaluating our PTB ontology, most patients who did not get TB positive by the microbiological test, have negative results for other types of tests. This ontology was validated by a general physician and a PTB specialist. The proposed method is also very useful in accurately diagnosing PTB.

Conclusions

In this paper, we proposed an SAI system to diagnose TB in ontology with biological data. In collaboration with 2 doctors and to know deep knowledge about biological data and their impact, we designed PTB ontology by gathering all paraclinical tests without losing any information about the patients. Our ontology system is the best solution to reduce the issue with laboratory TB test complexity also our physicians are satisfied with our ontology. The performance is good also, we are processing to train in the hospital for a better result. In future work, combining clinical reports and X-rays of the same patient list with help of time sequencing such as the ODE -RNN (Ordinary Differential Equation Recurrent Neural Network) method which predicts due to the complexity of data and loss of accuracy.

Figure 1 .

 1 Figure 1. SAI system and the process of the system

Figure 2 .

 2 Figure 2. PTB Ontology -Graphical representative of our system

Figure 3 .

 3 Figure 3. Implementation of SWRL rule

Table 1 .

 1 The table classifies the order and information contained in our ontology

	Demographic	Clinical information Preliminary Diagnose	Types of Diagnosis
	information
	Patient Id	Fever	Sputum NAAT	Arterial Blood Gas
	Patient Age	Cold	CB NAAT	Bio-Chemistry
	Patient gender	Bleeding Cough	Sputum AFB	Electrolytes
			Sputum line Probe	HIV
			Sputum Liquid Culture	Lipid Profile
			& Sensitivity
			Sputum Solid Culture &	Pathology
			sensitivity	Liver Function Test
				Microbiological
				investigation

Corresponding Author: Napthaline Gerard, napthalineg@gmail.com.