Care episode retrieval: distributional semantic models for information retrieval in the clinical domain

BMC Medical Informatics and Decision Making

Proceedings

Erwin Marsi¹  Hans Moen²  Filip Ginter³  Tapio Salakoski⁴  Laura-Maria Peltonen⁵  Sanna Salanterä⁵ 

[1] Department of Computer and Information Science, Norwegian University of Science and Technology, Sem Saelands vei 9, 7491, Trondheim, Norway;Department of Computer and Information Science, Norwegian University of Science and Technology, Sem Saelands vei 9, 7491, Trondheim, Norway;Department of Information Technology, University of Turku, Joukahaisenkatu 3-5, 20520, Turku, Finland;Department of Nursing Science, University of Turku, Lemminkäisenkatu 1, 20520, Turku, Finland;Department of Information Technology, University of Turku, Joukahaisenkatu 3-5, 20520, Turku, Finland;Department of Information Technology, University of Turku, Joukahaisenkatu 3-5, 20520, Turku, Finland;Turku Centre for Computer Science (TUCS), Joukahaisenkatu 3-5, 20520, Turku, Finland;Department of Nursing Science, University of Turku, Lemminkäisenkatu 1, 20520, Turku, Finland;Turku University Hospital, Kiinamyllynkatu 4-8, 20521, Turku, Finland;

关键词: Information Retrieval;    Natural Language Processing;    Discharge Summary;    Query Expansion;    Mean Average Precision;   

DOI  :  10.1186/1472-6947-15-S2-S2

来源: Springer

【 摘 要 】

Patients' health related information is stored in electronic health records (EHRs) by health service providers. These records include sequential documentation of care episodes in the form of clinical notes. EHRs are used throughout the health care sector by professionals, administrators and patients, primarily for clinical purposes, but also for secondary purposes such as decision support and research. The vast amounts of information in EHR systems complicate information management and increase the risk of information overload. Therefore, clinicians and researchers need new tools to manage the information stored in the EHRs. A common use case is, given a - possibly unfinished - care episode, to retrieve the most similar care episodes among the records. This paper presents several methods for information retrieval, focusing on care episode retrieval, based on textual similarity, where similarity is measured through domain-specific modelling of the distributional semantics of words. Models include variants of random indexing and the semantic neural network model word2vec. Two novel methods are introduced that utilize the ICD-10 codes attached to care episodes to better induce domain-specificity in the semantic model. We report on experimental evaluation of care episode retrieval that circumvents the lack of human judgements regarding episode relevance. Results suggest that several of the methods proposed outperform a state-of-the art search engine (Lucene) on the retrieval task.

【 授权许可】

CC BY   
© Moen et al.; licensee BioMed Central Ltd. 2015