An Interpretable Algorithm on Post-injury Health Service Utilization Patterns to Predict Injury Outcomes.
Purpose Post-injury health service utilization (HSU) contributes to injury outcomes, but limited studies investigated their relationship. This study aims to group injured patients in transport accidents based on minimal historical information of their HSU so that the groups are meaningfully associat...
| Published in: | Journal of Occupational Rehabilitation Vol. 30; no. 3; pp. 331 - 343 |
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| Main Authors: | , , |
| Format: | research tables/charts Journal Article |
| Published: |
Springer Nature
Sep2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144951457&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144951457 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10530487 JOR jtl: Journal of Occupational Rehabilitation issn: 10530487 maglogo: N pubinfo: dt: Sep2020 vid: 30 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 144951457 144951457 144951457 10.1007/s10926-019-09863-0 144951457 ppf: 331 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Interpretable Algorithm on Post-injury Health Service Utilization Patterns to Predict Injury Outcomes. aug: au: Akbarzadeh Khorshidi, Hadi Hassani-Mahmooei, Behrooz Haffari, Gholamreza affil: School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia sug: subj: Algorithms Occupational-Related Injuries Health Services Utilization Patient Classification Methods Transportation Accidents, Occupational Prediction Models Outcomes (Health Care) Human Funding Source Machine Learning Methods Accidental Injuries Decision Trees Cluster Analysis Descriptive Statistics Regression Health Care Costs Recovery Severity of Injury Adolescence Adult Middle Age Aged Male Female Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Purpose Post-injury health service utilization (HSU) contributes to injury outcomes, but limited studies investigated their relationship. This study aims to group injured patients in transport accidents based on minimal historical information of their HSU so that the groups are meaningfully associated with the outcome of interest. Methods The data include 20,692 injured patients who had compensation claims over 3 years. We propose a hybrid approach, combining unsupervised and supervised machine learning methods. Based on the first week post-injury data, we identify a proper clustering of patients best associated with total cost to recovery, as well as the discovery of HSU patterns. This allows developing models to accurately predict the outcome of interest using the discovered patterns. Furthermore, we propose to use decision tree classifiers to accurately classify future patients into the discovered clusters using their first week post-injury information. Results Our hybrid approach has identified eight patient groups. The compactness of the resulted clusters, assessed by Average Silhouette Width metric, is 0.71 indicating well-defined clusters. The resulted patient groups are highly predictive of injury outcomes. They improve the cost predictability more than twice in comparison with predictors such as gender, age and injury type. These groups also have substantial association with patients' recovery. The transparency and interpretability of decision trees allow integrating the resulting classification rules conveniently in operational processes. Conclusions This study provides a framework to discover knowledge and useful insights for health service providers and policy makers to control injury outcomes, and consequently to reduce the severity of transport accidents. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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