Implementation of a System for Intelligent Summarization of Longitudinal Clinical Records. Goldstein, A. & Shahar, Y. In Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., & ten Teije, A., editors, Process Support and Knowledge Representation in Health Care, of Lecture Notes in Computer Science, pages 68–82, Cham, 2013. Springer International Publishing.
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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 also in other clinical domains. In our previous work, we have suggested a general framework for performing this task, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge. We have recently developed a prototype system, CliniText, which demonstrates our ideas. Our prototype combines 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 both our methodology, and the feasibility of providing a free-text summary of longitudinal electronic patient records, by generating a discharge summary of a patient from the MIMIC database, who had undergone a Coronary Artery Bypass Graft operation.
@inproceedings{goldstein_implementation_2013,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Implementation of a {System} for {Intelligent} {Summarization} of {Longitudinal} {Clinical} {Records}},
	isbn = {978-3-319-03916-9},
	doi = {10.1007/978-3-319-03916-9_6},
	abstract = {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 also in other clinical domains. In our previous work, we have suggested a general framework for performing this task, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge. We have recently developed a prototype system, CliniText, which demonstrates our ideas. Our prototype combines 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 both our methodology, and the feasibility of providing a free-text summary of longitudinal electronic patient records, by generating a discharge summary of a patient from the MIMIC database, who had undergone a Coronary Artery Bypass Graft operation.},
	language = {en},
	booktitle = {Process {Support} and {Knowledge} {Representation} in {Health} {Care}},
	publisher = {Springer International Publishing},
	author = {Goldstein, Ayelet and Shahar, Yuval},
	editor = {Riaño, David and Lenz, Richard and Miksch, Silvia and Peleg, Mor and Reichert, Manfred and ten Teije, Annette},
	year = {2013},
	keywords = {Abductive Reasoning, Discharge Summary, Final Text, Nasal Cannula, Natural Language Generation, notion},
	pages = {68--82},
}

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