Distributed learning from multiple EHR databases: Contextual embedding models for medical events.
Electronic health record (EHR) data provide promising opportunities to explore personalized treatment regimes and to make clinical predictions. Compared with regular clinical data, EHR data are known for their irregularity and complexity. In addition, analyzing EHR data involves privacy issues and s...
| Publicado en: | Journal of Biomedical Informatics Vol. 92; pp. 103138 - 103139 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Apr2019
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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=136179803&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136179803 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Apr2019 vid: 92 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 136179803 136179803 NLM30825539 136179803 10.1016/j.jbi.2019.103138 NLM30825539 136179803 ppf: 103138 ppct: 1 formats: tig: atl: Distributed learning from multiple EHR databases: Contextual embedding models for medical events. aug: au: Li, Ziyi Roberts, Kirk Jiang, Xiaoqian Long, Qi affil: Emory University, Department of Biostatistics and Bioinformatics, Atlanta, GA 30332, USA sug: subj: Computer Communication Networks Human Diagnosis, Computer Assisted Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales ab: Electronic health record (EHR) data provide promising opportunities to explore personalized treatment regimes and to make clinical predictions. Compared with regular clinical data, EHR data are known for their irregularity and complexity. In addition, analyzing EHR data involves privacy issues and sharing such data is often infeasible among multiple research sites due to regulatory and other hurdles. A recently published work uses contextual embedding models and successfully builds one predictive model for more than seventy common diagnoses. Despite of the high predictive power, the model cannot be generalized to other institutions without sharing data. In this work, a novel method is proposed to learn from multiple databases and build predictive models based on Distributed Noise Contrastive Estimation (Distributed NCE). We use differential privacy to safeguard the intermediary information sharing. The numerical study with a real dataset demonstrates that the proposed method not only can build predictive models in a distributed manner with privacy protection, but also preserve model structure well and achieve comparable prediction accuracy. The proposed methods have been implemented as a stand-alone Python library and the implementation is available on Github (https://github.com/ziyili20/DistributedLearningPredictor) with installation instructions and use-cases. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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