# A machine learning framework to classify musculoskeletal injury risk groups in military service members

## TL;DR

This study uses machine learning to classify military service members into musculoskeletal injury risk groups based on self-reported data and electronic health records.

## Contribution

The novel contribution is the development of a machine learning framework using survival models to predict MSKI risk over multiple time horizons in military populations.

## Key findings

- The COX model outperformed other models in predicting MSKI risk with a time-dependent area under the curve of 0.73 to 0.70.
- The highest risk group had an MSKI incidence rate 4.74 times higher than the lowest risk group at 180 days.
- Factors like race, self-reported pain, gender, and prior injury were most predictive of MSKI risk.

## Abstract

Musculoskeletal injuries (MSKIs) are endemic in military populations. Thus, it is essential to identify and mitigate MSKI risks. Time-to-event machine learning models utilizing self-reported questionnaires or existing data (e.g., electronic health records) may aid in creating efficient risk screening tools.

A total of 4,222 U.S. Army Service members completed a self-report MSKI risk screen as part of their unit's standard in-processing. Additionally, participants' MSKI and demographic data were abstracted from electronic health record data. Survival machine learning models (Cox proportional hazard regression (COX), COX with splines, conditional inference trees, and random forest) were deployed to develop a predictive model on the training data (75%; n = 2,963) for MSKI risk over varying time horizons (30, 90, 180, and 365 days) and were evaluated on the testing data (25%; n = 987). Probability of predicted risk (0.00–1.00) from the final model stratified Service members into quartiles based on MSKI risk.

The COX model demonstrated the best model performance over the time horizons. The time-dependent area under the curve ranged from 0.73 to 0.70 at 30 and 180 days. The index prediction accuracy (IPA) was 12% better at 180 days than the IPA of the null model (0 variables). Within the COX model, “other” race, more self-reported pain items during the movement screens, female gender, and prior MSKI demonstrated the largest hazard ratios. When predicted probability was binned into quartiles, at 180 days, the highest risk bin had an MSKI incidence rate of 2,130.82 ± 171.15 per 1,000 person-years and incidence rate ratio of 4.74 (95% confidence interval: 3.44, 6.54) compared to the lowest risk bin.

Self-reported questionnaires and existing data can be used to create a machine learning algorithm to identify Service members' MSKI risk profiles. Further research should develop more granular Service member-specific MSKI screening tools and create MSKI risk mitigation strategies based on these screenings.