Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naive Bayes classifier

被引：10

作者：

Shen, Ying ^{[1
]}

Li, Yaliang ^{[2
]}

Zheng, Hai-Tao ^{[3
]}

Tang, Buzhou ^{[4
]}

Yang, Min ^{[5
]}

机构：

[1] Peking Univ, Sch Elect & Comp Engn, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China

[2] Alibaba Grp, Bellevue, WA USA

[3] Tsinghua Univ, Sch Informat Sci & Technol, Grad Sch Shenzhen, Shenzhen 518055, Peoples R China

[4] Harbin Inst Technol Shenzhen, Shenzhen 518055, Peoples R China

[5] Chinese Acad Sci, SIAT, Shenzhen 518055, Peoples R China

来源：

BMC BIOINFORMATICS | 2019年 / 20卷 / 1期

基金：

中国国家自然科学基金;

关键词：

Ontology; Probability; Uncertainty reasoning; naive Bayes classifier; EXTRACTION; INFERENCE;

D O I：

10.1186/s12859-019-2924-0

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

BackgroundOntology has attracted substantial attention from both academia and industry. Handling uncertainty reasoning is important in researching ontology. For example, when a patient is suffering from cirrhosis, the appearance of abdominal vein varices is four times more likely than the presence of bitter taste. Such medical knowledge is crucial for decision-making in various medical applications but is missing from existing medical ontologies. In this paper, we aim to discover medical knowledge probabilities from electronic medical record (EMR) texts to enrich ontologies. First, we build an ontology by identifying meaningful entity mentions from EMRs. Then, we propose a symptom-dependency-aware naive Bayes classifier (SDNB) that is based on the assumption that there is a level of dependency among symptoms. To ensure the accuracy of the diagnostic classification, we incorporate the probability of a disease into the ontology via innovative approaches.ResultsWe conduct a series of experiments to evaluate whether the proposed method can discover meaningful and accurate probabilities for medical knowledge. Based on over 30,000 deidentified medical records, we explore 336 abdominal diseases and 81 related symptoms. Among these 336 gastrointestinal diseases, the probabilities of 31 diseases are obtained via our method. These 31 probabilities of diseases and 189 conditional probabilities between diseases and the symptoms are added into the generated ontology.ConclusionIn this paper, we propose a medical knowledge probability discovery method that is based on the analysis and extraction of EMR text data for enriching a medical ontology with probability information. The experimental results demonstrate that the proposed method can effectively identify accurate medical knowledge probability information from EMR data. In addition, the proposed method can efficiently and accurately calculate the probability of a patient suffering from a specified disease, thereby demonstrating the advantage of combining an ontology and a symptom-dependency-aware naive Bayes classifier.

引用

页数：14