Evicase: An evidence-based case structuring approach for personalized healthcare

Boaz Carmeli; Paolo Casali; Anna Goldbraich; Abigail Goldsteen; Carmel Kent; Lisa Licitra; Paolo Locatelli; Nicola Restifo; Ruty Rinott; Elena Sini; Michele Torresani; Zeev Waks

doi:10.3233/978-1-61499-101-4-604

Evicase: An evidence-based case structuring approach for personalized healthcare

Boaz Carmeli, Paolo Casali, Anna Goldbraich, Abigail Goldsteen, Carmel Kent, Lisa Licitra, Paolo Locatelli, Nicola Restifo, Ruty Rinott, Elena Sini, Michele Torresani, Zeev Waks

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

Abstract

The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.

Original language	English
Title of host publication	Quality of Life Through Quality of Information - Proceedings of MIE 2012
Publisher	IOS Press
Pages	604-608
Number of pages	5
ISBN (Print)	9781614991007
DOIs	https://doi.org/10.3233/978-1-61499-101-4-604
State	Published - 2012
Externally published	Yes
Event	24th Medical Informatics in Europe Conference, MIE 2012 - Pisa, Italy Duration: 26 Aug 2012 → 29 Aug 2012

Publication series

Name	Studies in Health Technology and Informatics
Volume	180
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	24th Medical Informatics in Europe Conference, MIE 2012
Country/Territory	Italy
City	Pisa
Period	26/08/12 → 29/08/12

Keywords

Clinical business intelligence
Clinical guidelines
Decision support
Machine-learning algorithms
Personalized medicine

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/978-1-61499-101-4-604

Cite this

Carmeli, B., Casali, P., Goldbraich, A., Goldsteen, A., Kent, C., Licitra, L., Locatelli, P., Restifo, N., Rinott, R., Sini, E., Torresani, M., & Waks, Z. (2012). Evicase: An evidence-based case structuring approach for personalized healthcare. In Quality of Life Through Quality of Information - Proceedings of MIE 2012 (pp. 604-608). (Studies in Health Technology and Informatics; Vol. 180). IOS Press. https://doi.org/10.3233/978-1-61499-101-4-604

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title = "Evicase: An evidence-based case structuring approach for personalized healthcare",

abstract = "The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.",

keywords = "Clinical business intelligence, Clinical guidelines, Decision support, Machine-learning algorithms, Personalized medicine",

author = "Boaz Carmeli and Paolo Casali and Anna Goldbraich and Abigail Goldsteen and Carmel Kent and Lisa Licitra and Paolo Locatelli and Nicola Restifo and Ruty Rinott and Elena Sini and Michele Torresani and Zeev Waks",

year = "2012",

doi = "10.3233/978-1-61499-101-4-604",

language = "English",

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series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

pages = "604--608",

booktitle = "Quality of Life Through Quality of Information - Proceedings of MIE 2012",

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note = "24th Medical Informatics in Europe Conference, MIE 2012 ; Conference date: 26-08-2012 Through 29-08-2012",

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Carmeli, B, Casali, P, Goldbraich, A, Goldsteen, A, Kent, C, Licitra, L, Locatelli, P, Restifo, N, Rinott, R, Sini, E, Torresani, M & Waks, Z 2012, Evicase: An evidence-based case structuring approach for personalized healthcare. in Quality of Life Through Quality of Information - Proceedings of MIE 2012. Studies in Health Technology and Informatics, vol. 180, IOS Press, pp. 604-608, 24th Medical Informatics in Europe Conference, MIE 2012, Pisa, Italy, 26/08/12. https://doi.org/10.3233/978-1-61499-101-4-604

Evicase: An evidence-based case structuring approach for personalized healthcare. / Carmeli, Boaz; Casali, Paolo; Goldbraich, Anna et al.
Quality of Life Through Quality of Information - Proceedings of MIE 2012. IOS Press, 2012. p. 604-608 (Studies in Health Technology and Informatics; Vol. 180).

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

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AU - Carmeli, Boaz

AU - Casali, Paolo

AU - Goldbraich, Anna

AU - Goldsteen, Abigail

AU - Kent, Carmel

AU - Licitra, Lisa

AU - Locatelli, Paolo

AU - Restifo, Nicola

AU - Rinott, Ruty

AU - Sini, Elena

AU - Torresani, Michele

AU - Waks, Zeev

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N2 - The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.

AB - The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.

KW - Clinical business intelligence

KW - Clinical guidelines

KW - Decision support

KW - Machine-learning algorithms

KW - Personalized medicine

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DO - 10.3233/978-1-61499-101-4-604

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AN - SCOPUS:84872549923

SN - 9781614991007

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BT - Quality of Life Through Quality of Information - Proceedings of MIE 2012

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ER -

Evicase: An evidence-based case structuring approach for personalized healthcare

Abstract

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Keywords

ASJC Scopus subject areas

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