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. doi abstract bibtex 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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