disease_owl
- Ontology has attracted much attention from both academia and industry. Handling uncertainty reasoning is important in research on 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. We first build an ontology by discovering meaningful entity mentions from EMRs. Then, we propose a symptom dependency-aware naïve Bayes classifier that is built on the assumption that there is a particular level of dependency among symptoms. To ensure the accuracy of diagnostic classification, we add the value of the probability of a disease to the ontology in innovative ways. Results: We conduct a series of experiments to demonstrate that 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 through our method. These 31 probabilities of disease and 189 conditional probabilities between diseases and symptoms are added to the generated ontology. Conclusion: In this paper, we propose a medical knowledge probability discovery method based on the analysis and extraction of EMR text data to enrich a medical ontology with probability information. The experimental results show that the proposed method can effectively discover accurate medical knowledge probability information from EMR data. Further, the proposed method can efficiently and accurately calculate the probability of a patient suffering from a specific disease, revealing the advantage of the combination of ontology and the symptom dependency-aware naïve Bayes classifier.