Evaluation of matched control algorithms in EHR-based phenotyping studies: a case study of inflammatory bowel disease comorbidities.
The success of many population studies is determined by proper matching of cases to controls. Some of the confounding and bias that afflict electronic health record (EHR)-based observational studies may be reduced by creating effective methods for finding adequate controls. We implemented a method t...
| Publicado en: | Journal of Biomedical Informatics Vol. 52; pp. 105 - 112 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Dec2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109768436&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109768436 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Dec2014 vid: 52 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 109768436 NLM25196084 2012824389 10.1016/j.jbi.2014.08.012 NLM25196084 PMC4261034 109768436 ppf: 105 ppct: 7 formats: tig: atl: Evaluation of matched control algorithms in EHR-based phenotyping studies: a case study of inflammatory bowel disease comorbidities. aug: au: Castro, Victor M Apperson, W Kay Gainer, Vivian S Ananthakrishnan, Ashwin N Goodson, Alyssa P Wang, Taowei D Herrick, Christopher D Murphy, Shawn N sug: subj: Comorbidity Electronic Health Records Classification Inflammatory Bowel Diseases Epidemiology Medical Informatics Methods Algorithms Case Control Studies Human Patient Attitudes ab: The success of many population studies is determined by proper matching of cases to controls. Some of the confounding and bias that afflict electronic health record (EHR)-based observational studies may be reduced by creating effective methods for finding adequate controls. We implemented a method to match case and control populations to compensate for sparse and unequal data collection practices common in EHR data. We did this by matching the healthcare utilization of patients after observing that more complete data was collected on high healthcare utilization patients vs. low healthcare utilization patients. In our results, we show that many of the anomalous differences in population comparisons are mitigated using this matching method compared to other traditional age and gender-based matching. As an example, the comparison of the disease associations of ulcerative colitis and Crohn's disease show differences that are not present when the controls are chosen in a random or even a matched age/gender/race algorithm. In conclusion, the use of healthcare utilization-based matching algorithms to find adequate controls greatly enhanced the accuracy of results in EHR studies. Full source code and documentation of the control matching methods is available at https://community.i2b2.org/wiki/display/conmat/. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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