Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records.
Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 11 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
8/25/2015
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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=109322379&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109322379 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/25/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109322379 109322379 NLM26380290 10.1155/2015/636371 NLM26380290 PMC4561944 109322379 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records. aug: au: Jonnagaddala, Jitendra Liaw, Siaw-Teng Ray, Pradeep Kumar, Manish Dai, Hong-Jie Hsu, Chien-Yeh affil: School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW 2052, Australia sug: ab: Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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