A validated logistic regression model to identify coronary heart disease patients within primary care databases in the United kingdom.

K. Thiru; P. Donnan; F. Sullivan

A validated logistic regression model to identify coronary heart disease patients within primary care databases in the United kingdom.

K. Thiru^*, P. Donnan, F. Sullivan

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

We established the optimal search strategy for identifying coronary heart disease (CHD) patients within the Electronic Patient Record (EPR) of 'paperless' family practices in the UK. Multiple logistic regression modelling (MLRM) and Receiver Operating Characteristic (ROC) curves were used to develop the query. The selected search strategy was validated at 2 additional paperless family practices.

Original language	English
Number of pages	1
Journal	AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Publication status	Published - 2003

Cite this

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abstract = "We established the optimal search strategy for identifying coronary heart disease (CHD) patients within the Electronic Patient Record (EPR) of 'paperless' family practices in the UK. Multiple logistic regression modelling (MLRM) and Receiver Operating Characteristic (ROC) curves were used to develop the query. The selected search strategy was validated at 2 additional paperless family practices.",

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AB - We established the optimal search strategy for identifying coronary heart disease (CHD) patients within the Electronic Patient Record (EPR) of 'paperless' family practices in the UK. Multiple logistic regression modelling (MLRM) and Receiver Operating Characteristic (ROC) curves were used to develop the query. The selected search strategy was validated at 2 additional paperless family practices.

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JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

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A validated logistic regression model to identify coronary heart disease patients within primary care databases in the United kingdom.

Abstract

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