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Extracting Patient Case Profiles with Domain-Specific Semantic Categories

Extracting Patient Case Profiles with Domain-Specific Semantic Categories

Yitao Zhang, Jon Patrick
Copyright: © 2009 |Pages: 15
ISBN13: 9781605662749|ISBN10: 1605662747|ISBN13 Softcover: 9781616925284|EISBN13: 9781605662756
DOI: 10.4018/978-1-60566-274-9.ch014
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MLA

Zhang, Yitao, and Jon Patrick. "Extracting Patient Case Profiles with Domain-Specific Semantic Categories." Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, IGI Global, 2009, pp. 273-287. https://doi.org/10.4018/978-1-60566-274-9.ch014

APA

Zhang, Y. & Patrick, J. (2009). Extracting Patient Case Profiles with Domain-Specific Semantic Categories. In V. Prince & M. Roche (Eds.), Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration (pp. 273-287). IGI Global. https://doi.org/10.4018/978-1-60566-274-9.ch014

Chicago

Zhang, Yitao, and Jon Patrick. "Extracting Patient Case Profiles with Domain-Specific Semantic Categories." In Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, 273-287. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-274-9.ch014

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Abstract

The fast growing content of online articles of clinical case studies provides a useful source for extracting domain-specific knowledge for improving healthcare systems. However, current studies are more focused on the abstract of a published case study which contains little information about the detailed case profiles of a patient, such as symptoms and signs, and important laboratory test results of the patient from the diagnostic and treatment procedures. This paper proposes a novel category set to cover a wide variety of semantics in the description of clinical case studies which distinguishes each unique patient case. A manually annotated corpus consisting of over 5000 sentences from 75 journal articles of clinical case studies has been created. A sentence classification system which identifies 13 classes of clinically relevant content has been developed. A golden standard for assessing the automatic classifications has been established by manual annotation. A maximum entropy (MaxEnt) classifier is shown to produce better results than a Support Vector Machine (SVM) classifier on the corpus.

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