Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records.
Physicians are required to interpret, abstract and present in free-text large amounts of clinical data in their daily tasks. This is especially true for chronic-disease domains, but holds also in other clinical domains. We have recently developed a prototype system, CliniText, which, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge, combines the computational mechanisms of knowledge-based temporal data abstraction, textual summarization, abduction, and natural-language generation techniques, to generate an intelligent textual summary of longitudinal clinical data. We demonstrate our methodology, and the feasibility of providing a free-text summary of longitudinal electronic patient records, by generating summaries in two very different domains - Diabetes Management and Cardiothoracic surgery. In particular, we explain the process of generating a discharge summary of a patient who had undergone a Coronary Artery Bypass Graft operation, and a brief summary of the treatment of a diabetes patient for five years.
Medical Informatics Research Center, Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer Sheva, Israel.
Ayelet
A
Goldstein
Medical Informatics Research Center, Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer Sheva, Israel.
Yuval
Y
Shahar
D057286:Electronic Health Records / Q000145:classification*; D016247:Information Storage and Retrieval / Q000379:methods*; D051188:Knowledge Bases; D008137:Longitudinal Studies; D000069550:Machine Learning; D009323:Natural Language Processing; D018875:Vocabulary, Controlled