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Pharmacoepidemiol Drug Saf. 2013 Aug;22(8):826-33. doi: 10.1002/pds.3438. Epub 2013 Apr 17.

Automatic generation of case-detection algorithms to identify children with asthma from large electronic health record databases.

Author information

1
Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands. m.afzal@erasmusmc.nl

Abstract

PURPOSE:

Most electronic health record databases contain unstructured free-text narratives, which cannot be easily analyzed. Case-detection algorithms are usually created manually and often rely only on using coded information such as International Classification of Diseases version 9 codes. We applied a machine-learning approach to generate and evaluate an automated case-detection algorithm that uses both free-text and coded information to identify asthma cases.

METHODS:

The Integrated Primary Care Information (IPCI) database was searched for potential asthma patients aged 5-18 years using a broad query on asthma-related codes, drugs, and free text. A training set of 5032 patients was created by manually annotating the potential patients as definite, probable, or doubtful asthma cases or non-asthma cases. The rule-learning program RIPPER was then used to generate algorithms to distinguish cases from non-cases. An over-sampling method was used to balance the performance of the automated algorithm to meet our study requirements. Performance of the automated algorithm was evaluated against the manually annotated set.

RESULTS:

The selected algorithm yielded a positive predictive value (PPV) of 0.66, sensitivity of 0.98, and specificity of 0.95 when identifying only definite asthma cases; a PPV of 0.82, sensitivity of 0.96, and specificity of 0.90 when identifying both definite and probable asthma cases; and a PPV of 0.57, sensitivity of 0.95, and specificity of 0.67 for the scenario identifying definite, probable, and doubtful asthma cases.

CONCLUSIONS:

The automated algorithm shows good performance in detecting cases of asthma utilizing both free-text and coded data. This algorithm will facilitate large-scale studies of asthma in the IPCI database.

KEYWORDS:

automated case definition; case-detection algorithms; electronic medical records; machine learning; pharmacoepidemiology

PMID:
23592573
DOI:
10.1002/pds.3438
[Indexed for MEDLINE]
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