Evaluation of an automated pressure ulcer risk assessment model.
The key to timely interventions and reducing avoidable incidence is the early identification of patients at risk for developing pressure ulcers. To enable the automatic detection of such patients and inform acute care interdisciplinary providers, a filter feature model using heuristic statistical me...
| Publicado en: | Home Health Care Management & Practice Vol. 19; no. 4; pp. 272 - 285 |
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| Autores principales: | , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Jun2007
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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=106160399&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106160399 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10848223 96C jtl: Home Health Care Management & Practice issn: 10848223 maglogo: Y pubinfo: dt: Jun2007 vid: 19 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 106160399 2009636655 10.1177/1084822307303566 106160399 ppf: 272 ppct: 13 formats: tig: atl: Evaluation of an automated pressure ulcer risk assessment model. aug: au: Borlawsky T Hripcsak G affil: Senior Systems Consultant, Ohio State University Medical Center Information Warehouse sug: subj: Decision Trees Pressure Ulcer Nursing Risk Assessment Chi Square Test Descriptive Statistics Female Male P-Value Predictive Value of Tests Pressure Ulcer Classification Pressure Ulcer Diagnosis Sensitivity and Specificity Human Female Male ab: The key to timely interventions and reducing avoidable incidence is the early identification of patients at risk for developing pressure ulcers. To enable the automatic detection of such patients and inform acute care interdisciplinary providers, a filter feature model using heuristic statistical methods was applied to a relational database of retrospective patient data including demographics, medications, and clinical visit details. These attributes served as input for the C4.5 decision tree induction algorithm, which was used to classify patient risk. The validity of the resulting classification model, Electronic Pressure Ulcer Prediction (ePUP), was assessed using a fourfold cross-validation. The current results show a limited application of such a naive classification algorithm for automating pressure ulcer risk assessments. Additional refinements will be necessary before the predictions of ePUP are sufficient for general clinical use and the improvement of patient safety in acute care settings, and during the transition from hospital to home. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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