Ontology MSA to Differential diagnosis

Abstract
Ontology is an information system that transforms knowledge into a special conceptualization. This study aimed to construct the ontology of multiple system atrophy through a review of the literature, the analysis of the symptoms, drugs, tools, pathology, and factors affecting the disease, and the search of the co-words in the manuscripts of the articles on the disease published in Medline from 1983 to 2020-12-08. This article reports the results of an applied study conducted using the co-word analysis based on the conceptual network as well as the concept hierarchy extraction methods. The analysis of the studies on multiple system atrophy showed the effectiveness of this ontology in unders (Preprint)
Authors: Khazaneha Mahdiyeh , Osareh Farideh , Shafie Kaveh 
Published: December 2020

BACKGROUND

1. Introduction
Ontology is an information system that transforms knowledge into a special conceptualization [1]. When described semantically, ontology becomes a glossary, when described in a logical language (concepts and relations between words), becomes a formal ontology, and when logical ontologies are coded in a computational language, formal ontology becomes computational ontology[2]. Finally, ontologies focus on the general classifications and domains that underlie ontology [1, 3]. Another type of ontology is complex and advanced ontology. Applications of this type of ontology include creating precise semantic relationships to improve and strengthen automated retrieval systems and processes, machine reasoning, and the semantic web, however, it is costly and time-consuming[4-7]. Ontology has been defined as a set of new concepts that with a common understanding of these facts, including all the factors of interpretation of concepts and relationships in that particular domain has led to a new classification in that domain [2, 6, 7]. The applied ontology approach has had a great impact on all branches of information science. It has extended its applications in the world into domains such as intelligent user interfaces[8], data access [9], data warehousing [8], semantic web standards, and qualitative modeling in physical and medical systems [10]. One of the most important applications of ontology is its approach to diseases. Over the past three decades, with the spread of medical data, medical ontologies and controlled medical terminologies have played an important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems, biomedical research, and controlled terminologies. To ensure interactions between different fields of medicine, data are used as modeling and knowledge reasoning [11].
Multiple system atrophy is a degenerative disorder characterized by the symptoms and signs of parkinsonism (slowness of movement, sphincter stiffness, and disturbances, or cerebellar disorders)[12]. The core of this disease is the presence of sphincter disorders in addition to Parkinson's. Multiple atrophy system can have two clinical signs: A: Multiple system atrophy-parkinsonian type (MSA-P)[13] that causes symptoms similar to Parkinson disease but with a more rapid course and with an inappropriate response and B: Multiple system atrophy-cerebellar type (MSA-c)[14] in which Parkinsonism is associated with primary symptoms featuring ataxia (i.e. problems with balance and coordination) [12]. At the beginning of the disease, it is clinically difficult to differentiate the multiple atrophy system from Parkinson's disease, and there are no suitable paraclinical tools for it. Eventually, this ontology was assessed by a support vector machine (SVM), which is a supervised machine learning algorithm proposed by Vapnik[15] to solve classification problems. It is based on finding an optimal separating hyperplane in an N-dimensional space which distinctly classifies the data points [16]. SVM was used to solve multi-class classification problem[17]. The focus of the study
This study pursues the following objectives:
A.	The construction of an ontology to facilitate and support automatic reasoning for multiple system atrophy in which semantic search and enrichment takes place
B.	Development of new medical semantic algorithms and semantic enrichment algorithms using new classifications and relationships
C.	A complete ontology web language (OWL) that adheres to W3C semantic web standards.
D.	Modeling of classification MSA by SVM.

OBJECTIVE

The main objective of the present study was to construct the ontology of multiple system atrophy through publishd articles, the analysis of the symptoms, drugs, tools, pathology, and factors affecting the disease, and the search of the co-words in the manuscripts of the articles on the disease published in Medline from 1983 to 2020-12-08.

METHODS

4. Research methodology
This applied study was conducted using the co-word analysis method. Co-word analysis is a content analysis method based on concept hierarchy extraction techniques to develop relevant ontologies.
4.1 Dataset
The dataset used in this study included all studies published in the Medline[22]. In this study were desighned strategy related to database and extracted based on title, abstract, and keywords. In this study, only journal articles in the mentioned database published from 1988 to 2020 were included in the data analysis, and all conference papers and books were excluded.
4.2 Data collection
The data were collected using the Medline database. To this end, the terms including word synonyms and prepositions were first standardized as mesh terms based on the National Library Medicine (NLM) subject headings. Finally, a total of 7923 journal articles published from 1988 to 2020-12-08 were retrieved and extracted from the Medline Database using the following search strategy:
TS=("Atroph* Multiple System*" OR "Multiple System Atroph*" OR "Multisystem* Atroph*" OR "Atroph* Multisystem*" OR "Atroph* Multisystem*" OR "Multisystem* Atroph*" OR "Multisystem* Atrophy" OR "Atroph* Multisystem*" OR "Atroph* Multisystem*" OR "Multisystem* Atroph*" OR "Multiple System Atroph* Syndrome" OR "multiple system atroph*" OR "Shy-drager-syndrome" OR "Shy drager syndrome") Timespan: 1988-2020-12-08.

4.3 Procedure in Ontology
The data were collected in the form of 14 textual files from the Clarivate Analytics database. Then, the collected data were normalized. The extracted concepts were then processed as follows: (A) The relevant concepts were extracted using co-ward analysis (tokenization), (B) the concepts were selected semantically in the related domain (segmentation), (C) Specific tags in four domains (clinical manifestations, diagnostic tools, therapies, and pathology) were assigned to the concepts (POS tagging) based on the opinions of a neurologist, (D) the concepts were stemmed and standardized based on medical titles (NLM and Meta map) using VOSviewer, SciMAT, Protégé, and Excel software.
4.3.1 Procedure in MATLAB framework
The text classification involves data pre-processing, training the classification model, and evaluation. First the text data are imported and labeled. Then, the data is transformed into an understandable format to train the classifier. Tokenization was used in the pre-processing phase.
Processing steps
1. Input: T
2. Output: ßs, ßp
3. // tokenization
4. For each term in T
5. α.add (term)
6. End
8. // sentence detection
9. sentenceFinalPuncWords = {".", "?", "!", "!!", "!!!", "??", "?!", "!?"}
10. sentence = Empty
11. For each token in α
12. IF(token in sentenceFinalPuncWords)
13. ß.add(sentence)
14. sentence.empty()
15. Else
16. sentence.add(token)
17. End
18. End
19. // stop words removal
20. stopWords = {"without", "also", "must", "might",..}
21. For each sentence in ß
22. For each word in stopWords
23. IF (word in the sentence)
24. remove the word from the sentence
25. End
26. End
27. ßs.add(sentence)
28. End
30. // POS Tagging
31. For each sentence in ß
32. // calling POS Tagging service to POS tag sentence
33.. ßp.add(POSTaggor)sentence))

4.4 Semantic annotation for multiple system atrophy
The studies on multiple system atrophy were analyzed using the N-Gram model, which is a continuous sequence of N-words or tokens in a given sentence or continuous sequence of text. This step was one of the most important steps in building a framework for data analysis. In this step, the related concepts were categorized in terms of semantics, boundaries between entries, links between the entries, and the classification of concepts in these entries. Besides, semantic units were grouped into four domains of clinical manifestations, tools, therapies, and pathology, each with its own subcategories and features. To this end, the three-token N-Gram model was used and the results were included in the ontology corpus and National Library Medicine (NLM) and Unified Medical Language System (UMLS) were used to standardize the concepts. The identified classes were connected by Is-a relations, which are part of ontological relations as displayed in Table 1.

Table 1: An example of the main categories for the MSA ontology and their related features
Class: clinical feature
Superclasses (1)
•	Multiple System Atrophy
Disjoints (4)
clinical feature, investigation, management, pathophysiology
Usage (4)
•	affect the domain (affect some clinical features)
•	does not affect the domain clinical features
•	has domain clinical feature
•	less effect on the domain clinical features

4.5 Extracting and determining relationships using graph method and syntactic patterns
At this stage, the relationships between the new concepts were extracted and coded by an ontology reasoning engine.
Linking a term to ontology is queried via SPARQL and the attribute tag is linked to the term in question via rdflable. The match between the term and its features is of particular importance. The rdfs: class and rdf: type are matched as the case with rdflable which has the same properties and rdf: subclass. It should be noted that rdfs: is Defined By was used to describe the classes.
The Type Unique Identifier (TUI) module was used to enrich the concepts. To this end, after selecting a concept, its features are defined in the desired module. In these modules, enriching concepts with semantic relations is of special importance. In this study, 4 modules including clinical features, management, pathophysiology, and MSA investigation were enriched as shown in Table 2.
Table 2: The management module for multiple system atrophy and the related enriched concepts
Semantic type of Management	The instance of Linked biomedical
Description	Concepts
Medical treatment	Composed of a variety of drugs	botulinum_toxin
Non-medical treatment	Consists of non-pharmaceutical types	positive_airway

Table 3: The pathology module for multiple system atrophy and the related enriched concepts

Semantic type of Pathophysiology	The instance of Linked biomedical
Description	Concepts
Pathology	An abundant cytosolic protein that plays a critical role in the structure of multilamellar myelin. Myelin basic protein binds to the cytosolic sides of myelin cell membranes and causes a tight adhesion between opposing cell membranes	Myelin
Genetic	One species of Drosophila, in particular, D. melanogaster, has been heavily used in research in genetics and is a common model organism in developmental	model_genetic
Mechanism	Reducing C-terminal truncation mitigates synucleinopathy and neurodegeneration in a transgenic model of multiple system atrophy	alpha_synuclein_aggression
Histology	Neurons whose primary neurotransmitter is DOPAMINE	dopaminergic_neurons

Table 4: The investigation module for multiple system atrophy and the related
enriched concepts

Semantic type of Investigation	The instance of Linked biomedical
Description	Concepts
Biopsy
Removal and pathologic examination of specimens from the living body	Autopsy
Genetic test	Detection of a MUTATION; GENOTYPE; KARYOTYPE; or specific ALLELES associated with genetic traits, heritable diseases, or predisposition to a disease, or that may lead to the disease in descendants. It includes prenatal genetic testing	genome_wide_association_study
Imaging	The use of molecularly targeted imaging probes to localize	Anisotropy
Lab test	The method used for repeated blood pressure readings is made while the patient undergoes normal daily activities. It allows quantitative analysis of the high blood pressure load over time, can help distinguish between types of hypertension and can assess the effectiveness of antihypertensive therapy."	Blood_Pressure_Monitoring,_Ambulatory
Biomarker

Table 5: Clinical features (signs and symptoms) module for multiple system atrophy and the related enriched concepts

Semantic type of
Clinical feature	The instance of linked biomedical
Description	Concepts
Sign and symptom	Inability to perform particular purposive actions, as a result of brain damage.	kinesis
History	55-99, more –men	Aging

The enrichment model of MSA concepts and their modules address the classes covering the concepts and all secondary concepts such as semantic synonyms and definitions of related concepts placed in the Medical Semantic Annotation section. Besides, word definitions and synonyms were provided using NLM and Aminoff's Neurology text, and the related items were described in the annotation.

4.5 The relationships in the multiple system atrophy ontology
Concepts such as diseases, drugs, and treatments are the most fundamental part of the ontology of diseases. Accordingly, to determine these relations, two types of relations must be determined: taxonomic and non-taxonomic relations. In taxonomic relations, the UR1 for each concept shows the specific search algorithm and the hierarchical relationships for each concept. The non-taxonomic relationships were extracted in this study using the syntactic patterns method. To this end, the relations of the two concepts are shown in a free text. Some of these concepts are drawn and recognized through semantic relations by co-word analysis.
In general, it can be suggested that the encoding process in concepts, synonyms, definition, and taxonomic and non-taxonomic modules in the Ontology Web Language (OWL) is automated. Finally, in this process, new relationships are extracted in which the last subclass has its main class properties. For example:

Figure 1: Relationships between the subclasses
5. Evaluation of ontologies
There are two methods for evaluating ontologies: (1) Evaluation method based on previous ontologies and (2) Task-based evaluation method, in which each component has its role and characteristics evaluated separately according to the opinion of subject-matter experts. Metamap (48) and MGREP (49) were used to standardize and determine the role of words. A metamap is either a semi-automated or fully automated list of medical concepts used to compare concepts derived from article analysis and evaluate them based on expert opinions.

RESULTS

Results
To construct the MSA ontology, 7923 documents and articles published from 1983 to 2020-12-08 were retrieved from Medline using the search strategy. A total of 6530 original articles were evaluated by co-word analysis to construct the ontology. This ontology is the basis for automated reasoning, annotation, and large-scale analysis. The developed MSA ontology included 1497 concepts that were divided into 4 enrichment modules, 512 classes and subclasses, and 757 concepts with logical relations. The identified classes included 107 attributes. It should be noted that 210 tokens were identified as instances. In this ontology, 4 main modules were extracted including clinical manifestations, treatments, tools, and pathology.
Table 6: An example of class analysis in the MSA oncology
Instances	Subclass	Class
Abductor paralysis	Sign	Clinical feature
Anorectal function	Sign	Clinical feature
Antecollis	Sign	Clinical feature
Anterocollis	Sign	Clinical feature
Anxiety	Symptom	Clinical feature
Urodynamics	Lab test	Investigation
Polysomnography	Lab test	Investigation
Agonist therapy	Medical Tx	Management
Apomorphine	Medical Tx	Management

The developed model included main modules and the main classes and subclasses. The classes and some subclasses had the same features, and this led to the equivalence of some concepts in the subclasses. These concepts were standardized through UMLS and NLM. Understanding each of the classes and subclasses of the module subset plays an important role in the diagnosis of this disease, and the concepts defined under their domain play a more specific role. Any of the identified concepts can be used in subsequent clinical studies for the differential diagnosis of this disease.
The Clinical Feature class included two subclasses: History and signs & symptoms accounting for all aspects that may play a role in the onset of the disease, including genetic and environmental factors as displayed in Figure 2:

Figure 2: The entograph of clinical feature classes and subclasses in the MSA ontology

Figure 3 shows the output of the investigation module that covers all the tools that are effective in diagnosing multiple system atrophy:

Figure 3: Classification of MSA diagnostic tools
The management module deals with factors effective in pharmacological and non-pharmacological treatment of multiple system atrophy. First, effective drugs were identified, and then the range of information was completed based on the connections in the classes and subclasses. The medical category shows effective drugs for treating the disease (Figure 4):

Figure 4: Drug segmentation of the management module in the MSA ontology
The domain information in the pathophysiology module is divided into 4 sections: History, genetics, mechanisms, and pathology. The module is the most extensive module of the ontology that identifies all aspects effective in diagnosing multiple system atrophy, as well as biological processes, pathogenic mechanisms, and new words that were shown to be effective in these processes.
In the annotation module, the concepts with affect relationships were considered as objects. This module included the concepts that were extracted through data mining and approved by the neurologist as the effective concepts in the differential diagnosis of multiple system atrophy (Figure 5):

Figure 5: Affect annotation in the Object module of the MSA ontology
Ontology evaluation
The used dataset in this experiment consisted of 6530 articles. A total of 1497 concepts were extracted from the areicles including 250 text samples that were divided into 4 categories: Clinical features, Investigation, Management, and Pathophysiology. Then, the text classifier was trained using a pre-classified text. The dataset was split into two training and test data. To this end, 80% of the dataset was used as the training data to train the SVM model, and the remaining 20% was used as the test data to predict unlabelled data. Finally, the text classifier was evaluated by comparing the estimated labels with true labels. The main evaluation measure, i.e. accuracy, was then evaluated. The accuracy results are illustrated in Table (A). In this table, sensitivity and specificity of the proposed model are also reported. Additionally, the procedure taken to calculate these three measurements is presented in equations 1 to 3. The class distribution plot is also presented in figure (A), which indicates each class abundance. Moreover, the confusion matrix, which describes the performance of the SVM classification model, is shown in Figure (B).
Equation 1: Accuracy = (Correctly Classified Samples )/(Classified Samples)
Equation 2: Sensitivity = (Correctly Classified Positive Samples)/(True Positive Samples)
Equation 3: Specificity = (Correctly Classified Negative Samples)/(True Negative Samples)
Table (A): The statistical measures of the model performance
Accuracy	88%
Sensitivity	83%
Specificity	99%

Figure(A): The Frequency of each class that distributed in four classes.

Figure(B): The confusion matrix of the classification problem.

CONCLUSIONS

Ontologies provide a common understanding of concepts within a particular domain. The analysis of MSA studies showed the effectiveness of this ontology in four aspects. This ontology provides specialists with an overview of all aspects of the disease. Using objective instances, this ontology enables neurologists to study aspects of this disease. The purpose of developing this ontology was to draw an entograph to automate semantic concepts related to multiple system atrophy. The major contributions of this study are as follows:
	Drawing an enthograph to show aspects of the disease
	Automatic knowledge sharing and management for multiple system atrophy
	Enrichment of concepts based on UMLS and NLM
	Standardization of concepts based on mesh and UMLS
	Identification of the main concepts in the differential diagnosis as objects and evaluation of these concepts based on the opinions of a neurologist
	In addition, SVM model can be used as a classifier model to predict the evolution ontology of MSA disease
Aknowledgment
We authores appreciate Amir hossien Tahmoursi for his corporation in data analysis.