Evaluating robustness of a generalized linear model when applied to electronic health record data accessed using an Open API.
The Integrated Clinical and Environmental Exposures Service (ICEES) provides open regulatory-compliant access to clinical data, including electronic health record data, that have been integrated with environmental exposures data. While ICEES has been validated in the context of an asthma use case an...
| Publicado en: | Health Informatics Journal Vol. 29; no. 2; pp. 1 - 17 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
Apr-Jun2023
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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=191301799&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191301799 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14604582 EJK jtl: Health Informatics Journal issn: 14604582 maglogo: Y pubinfo: dt: Apr-Jun2023 vid: 29 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 191301799 10.1177/14604582231170892 191301799 ppf: 1 ppct: 16 formats: tig: atl: Evaluating robustness of a generalized linear model when applied to electronic health record data accessed using an Open API. aug: au: Sharma, Priya Haaland, Perry Krishnamurthy, Ashok Lan, Bo Schmitt, Patrick L Sinha, Meghamala Xu, Hao Fecho, Karamarie affil: Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA sug: ab: The Integrated Clinical and Environmental Exposures Service (ICEES) provides open regulatory-compliant access to clinical data, including electronic health record data, that have been integrated with environmental exposures data. While ICEES has been validated in the context of an asthma use case and several other use cases, the regulatory constraints on the ICEES open application programming interface (OpenAPI) result in data loss when using the service for multivariate analysis. In this study, we investigated the robustness of the ICEES OpenAPI through a comparative analysis, in which we applied a generalized linear model (GLM) to the OpenAPI data and the constraint-free source data to examine factors predictive of asthma exacerbations. Consistent with previous studies, we found that the main predictors identified by both analyses were sex, prednisone, race, obesity, and airborne particulate exposure. Comparison of GLM model fit revealed that data loss impacts model quality, but only with select interaction terms. We conclude that the ICEES OpenAPI supports multivariate analysis, albeit with potential data loss that users should be aware of. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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