Temporal electronic phenotyping by mining careflows of breast cancer patients.
In this work we present a careflow mining approach designed to analyze heterogeneous longitudinal data and to identify phenotypes in a patient cohort. The main idea underlying our approach is to combine methods derived from sequential pattern mining and temporal data mining to derive frequent health...
| Publicado en: | Journal of Biomedical Informatics Vol. 66; pp. 136 - 148 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Feb2017
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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=121276674&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121276674 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Feb2017 vid: 66 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 121276674 121276674 NLM28057564 121276674 10.1016/j.jbi.2016.12.012 NLM28057564 121276674 ppf: 136 ppct: 12 formats: tig: atl: Temporal electronic phenotyping by mining careflows of breast cancer patients. aug: au: Dagliati, A. Sacchi, L. Bellazzi, R. Tibollo, V. Pavesi, L. Zambelli, A. Holmes, J.H. affil: Department of Electrical, Computer and Biomedical Engineering and Centre for Health Technologies, University of Pavia, Italy sug: subj: Patient Care Statistics and Numerical Data Breast Neoplasms Therapy Data Mining Electronics Female Breast Neoplasms Diagnosis Health Care Delivery Human Female ab: In this work we present a careflow mining approach designed to analyze heterogeneous longitudinal data and to identify phenotypes in a patient cohort. The main idea underlying our approach is to combine methods derived from sequential pattern mining and temporal data mining to derive frequent healthcare histories (careflows) in a population of patients. This approach was applied to an integrated data repository containing clinical and administrative data of more than 4000 breast cancer patients. We used the mined histories to identify sub-cohorts of patients grouped according to healthcare activities pathways, then we characterized these sub-cohorts with clinical data. In this way, we were able to perform temporal electronic phenotyping of electronic health records (EHR) data. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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