Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records

Ayelet Goldstein; Yuval Shahar

doi:10.3233/978-1-61499-564-7-594

Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records

Ayelet Goldstein
, Yuval Shahar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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 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.

Original language	English
Title of host publication	MEDINFO 2015
Subtitle of host publication	eHealth-Enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics
Editors	Andrew Georgiou, Indra Neil Sarkar, Paulo Mazzoncini de Azevedo Marques
Publisher	IOS Press
Pages	594-598
Number of pages	5
ISBN (Electronic)	9781614995630
DOIs	https://doi.org/10.3233/978-1-61499-564-7-594
State	Published - 1 Jan 2015
Event	15th World Congress on Health and Biomedical Informatics, MEDINFO 2015 - Sao Paulo, Brazil Duration: 19 Aug 2015 → 23 Aug 2015

Publication series

Name	Studies in Health Technology and Informatics
Volume	216
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	15th World Congress on Health and Biomedical Informatics, MEDINFO 2015
Country/Territory	Brazil
City	Sao Paulo
Period	19/08/15 → 23/08/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Knowledge Representation
Natural Language Generation
Summarization
Temporal Abstraction

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/978-1-61499-564-7-594

Cite this

Goldstein, A., & Shahar, Y. (2015). Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records. In A. Georgiou, I. N. Sarkar, & P. M. de Azevedo Marques (Eds.), MEDINFO 2015: eHealth-Enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics (pp. 594-598). (Studies in Health Technology and Informatics; Vol. 216). IOS Press. https://doi.org/10.3233/978-1-61499-564-7-594

Goldstein, Ayelet ; Shahar, Yuval. / Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records. MEDINFO 2015: eHealth-Enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics. editor / Andrew Georgiou ; Indra Neil Sarkar ; Paulo Mazzoncini de Azevedo Marques. IOS Press, 2015. pp. 594-598 (Studies in Health Technology and Informatics).

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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 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.",

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Goldstein, A & Shahar, Y 2015, Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records. in A Georgiou, IN Sarkar & PM de Azevedo Marques (eds), MEDINFO 2015: eHealth-Enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics. Studies in Health Technology and Informatics, vol. 216, IOS Press, pp. 594-598, 15th World Congress on Health and Biomedical Informatics, MEDINFO 2015, Sao Paulo, Brazil, 19/08/15. https://doi.org/10.3233/978-1-61499-564-7-594

Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records. / Goldstein, Ayelet; Shahar, Yuval.
MEDINFO 2015: eHealth-Enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics. ed. / Andrew Georgiou; Indra Neil Sarkar; Paulo Mazzoncini de Azevedo Marques. IOS Press, 2015. p. 594-598 (Studies in Health Technology and Informatics; Vol. 216).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records

AU - Goldstein, Ayelet

AU - Shahar, Yuval

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

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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 -

Goldstein A, Shahar Y. Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records. In Georgiou A, Sarkar IN, de Azevedo Marques PM, editors, MEDINFO 2015: eHealth-Enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics. IOS Press. 2015. p. 594-598. (Studies in Health Technology and Informatics). doi: 10.3233/978-1-61499-564-7-594

Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this