TY - GEN
T1 - Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records
AU - Goldstein, Ayelet
AU - Shahar, Yuval
N1 - Publisher Copyright:
© 2015 IMIA and IOS Press.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - Knowledge Representation
KW - Natural Language Generation
KW - Summarization
KW - Temporal Abstraction
UR - http://www.scopus.com/inward/record.url?scp=84952063340&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-564-7-594
DO - 10.3233/978-1-61499-564-7-594
M3 - Conference contribution
C2 - 26262120
AN - SCOPUS:84952063340
T3 - Studies in Health Technology and Informatics
SP - 594
EP - 598
BT - MEDINFO 2015
A2 - Georgiou, Andrew
A2 - Sarkar, Indra Neil
A2 - de Azevedo Marques, Paulo Mazzoncini
PB - IOS Press
T2 - 15th World Congress on Health and Biomedical Informatics, MEDINFO 2015
Y2 - 19 August 2015 through 23 August 2015
ER -
