Prognostic data-driven clinical decision support-Formulation and implications

Ruty Rinott; Boaz Carmeli; Carmel Kent; Daphna Landau; Yonatan Maman; Yoav Rubin; Noam Slonim

doi:10.3233/978-1-60750-806-9-140

Prognostic data-driven clinical decision support-Formulation and implications

Ruty Rinott, Boaz Carmeli, Carmel Kent, Daphna Landau, Yonatan Maman, Yoav Rubin, Noam Slonim

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

Abstract

Existing Clinical Decision Support Systems (CDSSs) typically rely on rule-based algorithms and focus on tasks like guidelines adherence and drug prescribing and monitoring. However, the increasing dominance of Electronic Health Record technologies and personalized medicine suggest great potential for prognostic data-driven CDSS. A major goal for such systems would be to accurately predict the outcome of patients' candidate treatments by statistical analysis of the clinical data stored at a Health Care Organization. We formally define the concepts involved in the development of such a system, highlight an inherent difficulty arising from bias in treatment allocation, and propose a general strategy to address this difficulty. Experiments over hypertension clinical data demonstrate the validity of our approach.

Original language	English
Title of host publication	User Centred Networked Health Care - Proceedings of MIE 2011
Publisher	IOS Press
Pages	140-144
Number of pages	5
ISBN (Print)	9781607508052
DOIs	https://doi.org/10.3233/978-1-60750-806-9-140
State	Published - 2011
Externally published	Yes
Event	23rd International Conference of the European Federation for Medical Informatics, MIE 2011 - Oslo, Norway Duration: 28 Aug 2011 → 31 Aug 2011

Publication series

Name	Studies in Health Technology and Informatics
Volume	169
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	23rd International Conference of the European Federation for Medical Informatics, MIE 2011
Country/Territory	Norway
City	Oslo
Period	28/08/11 → 31/08/11

Keywords

Clinical Decision Support
Data Driven
Machine Learning
Prognostic

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/978-1-60750-806-9-140

Cite this

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abstract = "Existing Clinical Decision Support Systems (CDSSs) typically rely on rule-based algorithms and focus on tasks like guidelines adherence and drug prescribing and monitoring. However, the increasing dominance of Electronic Health Record technologies and personalized medicine suggest great potential for prognostic data-driven CDSS. A major goal for such systems would be to accurately predict the outcome of patients' candidate treatments by statistical analysis of the clinical data stored at a Health Care Organization. We formally define the concepts involved in the development of such a system, highlight an inherent difficulty arising from bias in treatment allocation, and propose a general strategy to address this difficulty. Experiments over hypertension clinical data demonstrate the validity of our approach.",

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Rinott, R, Carmeli, B, Kent, C, Landau, D, Maman, Y, Rubin, Y & Slonim, N 2011, Prognostic data-driven clinical decision support-Formulation and implications. in User Centred Networked Health Care - Proceedings of MIE 2011. Studies in Health Technology and Informatics, vol. 169, IOS Press, pp. 140-144, 23rd International Conference of the European Federation for Medical Informatics, MIE 2011, Oslo, Norway, 28/08/11. https://doi.org/10.3233/978-1-60750-806-9-140

Prognostic data-driven clinical decision support-Formulation and implications. / Rinott, Ruty; Carmeli, Boaz; Kent, Carmel et al.
User Centred Networked Health Care - Proceedings of MIE 2011. IOS Press, 2011. p. 140-144 (Studies in Health Technology and Informatics; Vol. 169).

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

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AB - Existing Clinical Decision Support Systems (CDSSs) typically rely on rule-based algorithms and focus on tasks like guidelines adherence and drug prescribing and monitoring. However, the increasing dominance of Electronic Health Record technologies and personalized medicine suggest great potential for prognostic data-driven CDSS. A major goal for such systems would be to accurately predict the outcome of patients' candidate treatments by statistical analysis of the clinical data stored at a Health Care Organization. We formally define the concepts involved in the development of such a system, highlight an inherent difficulty arising from bias in treatment allocation, and propose a general strategy to address this difficulty. Experiments over hypertension clinical data demonstrate the validity of our approach.

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Prognostic data-driven clinical decision support-Formulation and implications

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this