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

Anthony Almudevar

TL;DR

An artificial neural network implementation of Cox proportional hazards regression due to Ripley (1998 thesis) is used as the basis for a model for the prediction of claim duration within a work injury compensation environment.

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.

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

TL;DR

An artificial neural network implementation of Cox proportional hazards regression due to Ripley (1998 thesis) is used as the basis for a model for the prediction of claim duration within a work injury compensation environment.

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.
Paper Structure (13 sections, 5 equations, 9 figures, 6 tables)

This paper contains 13 sections, 5 equations, 9 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Structure of Data
  3. Codes
  4. Other predictors
  5. Statistical Modelling
  6. Long term duration trends
  7. Proportional hazards assumption
  8. Main effects model
  9. ANN model
  10. Model Selection
  11. Model Evaluation
  12. Predictions Based on Partial Inputs
  13. Conclusion

Figures (9)

  • Figure 1: Long term trend in claim duration. 1) and 2) Mean and median trends using piecewise linear Cox regression (solid) and quarterly summaries (dashed). 3) Quarterly summaries of censoring rates.
  • Figure 2: Log cumulative hazard functions partitioned by NOI, POB, OCC, PAY, AGE and SEX (by column, then by row).
  • Figure 3: Multiple boxplots of log duration given for each predictor.
  • Figure 4: Generalized $R^2$ by decay parameter, for reduced and full models. Separate lines corresponding to number of hidden nodes are superimposed.
  • Figure 5: Actual durations (for closed claims) partitioned into deciles on the basis of prediction terms calculated from selected model. Plots are given in original and logarithm scale, for clarity. The location of 1, 4 and 10 weeks is indicated.
  • ...and 4 more figures