Text and Ontology Driven Clinical Decision Support System

Vast amounts of information are present in unstructured format in physicians' notes. Text processing techniques can be used to extract clinically relevant entities from such data. The extracted entities can then be mapped to concepts from medical ontologies to generate a structured Knowledge Base (KB) of patient facts. Clinical Rules written over this KB could then be used to develop systems that can help with a variety of clinical tasks such as decision support alerts in diagnostic process. We propose a generic text and ontology driven information extraction framework. In the first phase, preprocessing techniques such as section tagging, dependency parsing, gazetteer lists are used filter clinical terms from the raw data. The clinical records are parsed using Clinical Text Analysis and Knowledge Extraction System, to extract prior medical history, medications, observations, laboratory results etc. For every concept we consider its polarity, section in which the concept occurs, the associated numerical value, synonyms etc. 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 KB that stores 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 which can then be queried for a variety of clinical tasks. To demonstrate a proof of concept, we use discharge summaries from the cardiovascular domain to determine the TIMI Risk Score and San Francisco Syncope score for a patient

Text and Ontology Driven Clinical Decision Support System

Vast amounts of information are present in unstructured format in physicians' notes. Text processing techniques can be used to extract clinically relevant entities from such data. The extracted entities can then be mapped to concepts from medical ontologies to generate a structured Knowledge Base (KB) of patient facts. Clinical Rules written over this KB could then be used to develop systems that can help with a variety of clinical tasks such as decision support alerts in diagnostic process. We propose a generic text and ontology driven information extraction framework. In the first phase, preprocessing techniques such as section tagging, dependency parsing, gazetteer lists are used filter clinical terms from the raw data. The clinical records are parsed using Clinical Text Analysis and Knowledge Extraction System, to extract prior medical history, medications, observations, laboratory results etc. For every concept we consider its polarity, section in which the concept occurs, the associated numerical value, synonyms etc. 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 KB that stores 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 which can then be queried for a variety of clinical tasks. To demonstrate a proof of concept, we use discharge summaries from the cardiovascular domain to determine the TIMI Risk Score and San Francisco Syncope score for a patient