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An Interpretable Algorithm on Post-injury Health Service Utilization Patterns to Predict Injury Outcomes

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Abstract

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.

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Funding

This study is funded by Transport Accident Commission (TAC) through the Institute of Safety, Compensation and Recovery Research (ISCRR).

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Correspondence to Hadi Akbarzadeh Khorshidi.

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Hadi Akbarzadeh Khorshidi, Behrooz Hassani-Mahmooei and Gholamreza Haffari declare that they have no conflict of interest.

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This article does not contain any studies with human participants performed by any of the authors.

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Statement not required. This study was performed using a de-identified administrative dataset, with ethics approval granted by Monash University Human Research Ethics Committee (CF09/3150—2009001727).

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Akbarzadeh Khorshidi, H., Hassani-Mahmooei, B. & Haffari, G. An Interpretable Algorithm on Post-injury Health Service Utilization Patterns to Predict Injury Outcomes. J Occup Rehabil 30, 331–342 (2020). https://doi.org/10.1007/s10926-019-09863-0

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