Attributes for causal inference in electronic healthcare databases

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

doi:10.1109/CBMS.2013.6627871

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

3 Citations (Scopus)

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
Title of host publication	Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems
Pages	548-549
Number of pages	2
DOIs	https://doi.org/10.1109/CBMS.2013.6627871
Publication status	Published - 2013
Externally published	Yes
Event	26th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2013 - Porto, Portugal Duration: 20 Jun 2013 → 22 Jun 2013

Publication series

Name	Proceedings of the IEEE Symposium on Computer-Based Medical Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISSN (Print)	1063-7125

Conference

Conference	26th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2013
Country/Territory	Portugal
City	Porto
Period	20/06/13 → 22/06/13

UN SDGs

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

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Computer Science Applications

Access to Document

10.1109/CBMS.2013.6627871

Cite this

Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. (2013). Attributes for causal inference in electronic healthcare databases. In Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems (pp. 548-549). Article 6627871 (Proceedings of the IEEE Symposium on Computer-Based Medical Systems). https://doi.org/10.1109/CBMS.2013.6627871

@inproceedings{214fcd38aa0544c4830d4901166d3287,

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.",

author = "Jenna Reps and Garibaldi, \{Jonathan M.\} and Uwe Aickelin and Daniele Soria and Gibson, \{Jack E.\} and Hubbard, \{Richard B.\}",

year = "2013",

doi = "10.1109/CBMS.2013.6627871",

language = "English",

isbn = "9781479910533",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "548--549",

booktitle = "Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems",

}

Reps, J, Garibaldi, JM, Aickelin, U, Soria, D, Gibson, JE & Hubbard, RB 2013, Attributes for causal inference in electronic healthcare databases. in Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems., 6627871, Proceedings of the IEEE Symposium on Computer-Based Medical Systems, pp. 548-549, 26th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2013, Porto, Portugal, 20/06/13. https://doi.org/10.1109/CBMS.2013.6627871

Attributes for causal inference in electronic healthcare databases. / Reps, Jenna; Garibaldi, Jonathan M.; Aickelin, Uwe et al.
Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems. 2013. p. 548-549 6627871 (Proceedings of the IEEE Symposium on Computer-Based Medical Systems).

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

TY - GEN

T1 - Attributes for causal inference in electronic healthcare databases

AU - Reps, Jenna

AU - Garibaldi, Jonathan M.

AU - Aickelin, Uwe

AU - Soria, Daniele

AU - Gibson, Jack E.

AU - Hubbard, Richard B.

PY - 2013

Y1 - 2013

N2 - 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.

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.

UR - https://www.scopus.com/pages/publications/84889030338

U2 - 10.1109/CBMS.2013.6627871

DO - 10.1109/CBMS.2013.6627871

M3 - Conference contribution

AN - SCOPUS:84889030338

SN - 9781479910533

T3 - Proceedings of the IEEE Symposium on Computer-Based Medical Systems

SP - 548

EP - 549

BT - Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems

T2 - 26th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2013

Y2 - 20 June 2013 through 22 June 2013

ER -

Attributes for causal inference in electronic healthcare databases

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

Access to Document

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