Attributes for causal inference in electronic healthcare databases

Jenna Reps; Jonathan M. Garibaldi; Uwe Aickelin; Daniele Soria; Jack E. Gibson; Richard B. Hubbard

Attributes for causal inference in electronic healthcare databases

Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard

Research output: Journal Publication › Article › peer-review

Abstract

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria.

Original language	English
Article number	6627871
Pages (from-to)	548-549
Number of pages	2
Journal	Proceedings of the IEEE Symposium on Computer-Based Medical Systems
Publication status	Published - 2013
Externally published	Yes

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Computer Science Applications

UN SDGs

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

Cite this

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title = "Attributes for causal inference in electronic healthcare databases",

abstract = "Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria.",

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year = "2013",

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journal = "Proceedings of the IEEE Symposium on Computer-Based Medical Systems",

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T1 - Attributes for causal inference in electronic healthcare databases

AU - Reps, Jenna

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AU - Aickelin, Uwe

AU - Soria, Daniele

AU - Gibson, Jack E.

AU - Hubbard, Richard B.

PY - 2013

Y1 - 2013

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AB - Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria.

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M3 - Article

AN - SCOPUS:84897105240

SN - 1063-7125

SP - 548

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JO - Proceedings of the IEEE Symposium on Computer-Based Medical Systems

JF - Proceedings of the IEEE Symposium on Computer-Based Medical Systems

M1 - 6627871

ER -

Attributes for causal inference in electronic healthcare databases

Abstract

ASJC Scopus subject areas

UN SDGs

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