Using Artificial Neural Networks to Predict Claim Duration in a Work Injury Compensation Environment

TL;DR

This paper presents an artificial neural network model based on Cox regression to predict claim durations in Canadian work injury compensation, utilizing injury codes and demographic data for early claim management.

Contribution

It introduces a neural network implementation of Cox regression tailored for predicting injury claim durations using initial claim data.

Findings

Neural network model effectively predicts claim durations.

Model handles missing covariate data.

Provides distributional predictions for claim durations.

Abstract

Currently, work injury compensation boards in Canada track injury information using a standard system of codes (under the National Work Injury Statistics Program (NWISP)). These codes capture the medical nature and original cause of the injury in some detail, hence they potentially contain information which may be used to predict the severity of an injury and the resulting time loss from work. Claim duration easurements and forecasts are central to the operation of a work injury compensation program. However, due to the complexity of the codes traditional statistical modelling techniques are of limited value. We will describe an artificial neural network implementation of Cox proportional hazards regression due to Ripley (1998 thesis) which is used as the basis for a model for the prediction of claim duration within a work injury compensation environment. The model accepts as input the injury codes, as well as basic demographic and workplace information. The output consists of a claim duration prediction in the form of a distribution. The input represents information available when a claim is first filed, and may therefore be used in a claims management setting. We will describe the model selection procedure, as well as a procedure for accepting inputs with missing covariates.