Text and Ontology Driven Clinical Decision Support System

This thesis discusses our ongoing research in the domain of text and ontology driven clinical decision support system. The proposed framework uses text analytics to extract clinical entities from electronic health records and semantic web analytics to generate a domain specific knowledge base (KB) of patients’ clinical facts. Clinical Rules expressed in the Semantic Web Language OWL are used to reason over the KB to infer additional facts about the patient. The KB is then queried to provide clinically relevant information to the physicians. In the first phase, standard text pre processing techniques such as section tagging, dependency parsing, gazetteer lists are used filter clinical terms from the raw data.

In the second phase, a domain specific medical ontology is used to establish relation between the extracted clinical terms. The output of this phase is a Resource Description Framework KB that stores all possible medical facts about the patient. In the final phase, an OWL reasoner and clinical rules are used to infer additional facts about patient and generate a richer KB. This KB can then be queried for a variety of clinical tasks. To demonstrate a proof of concept of this framework, we have used discharge summaries from the cardiovascular domain and determined the TIMI Risk Score and San Francisco Syncope Score for a patient. The goal of this research is to combine factual knowledge about patients, procedural knowledge (clinical rules), and structured knowledge (medical ontologies) to develop a clinical decision support system.