# Temporal machine learning framework for diabetic foot ulcer healing trajectory prediction

## TL;DR

A machine learning framework predicts diabetic foot ulcer healing phases using clinical data, enabling proactive treatment planning.

## Contribution

A novel machine learning framework that predicts healing transitions using clinical metadata and provides treatment recommendations.

## Key findings

- The framework achieved 78% ± 4% accuracy in predicting healing phase transitions.
- The treatment recommendation system showed 88.7% agreement for offloading prescriptions.
- Dressing recommendations varied by wound chronicity, with lower match rates for very chronic wounds.

## Abstract

Diabetic foot ulcer management relies predominantly on reactive treatment adjustments based on current wound status. This study developed an accessible machine learning framework using routinely collected clinical metadata (no imaging required) to predict healing phase transitions at the next clinical appointment, enabling proactive treatment planning with an integrated recommendation system.

Longitudinal data from 268 patients with 329 distinct ulcers across 890 appointments were analyzed. Features (n \documentclass[12pt]{minimal}
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Feature selection identified 30 essential predictors, achieving 70.9% dimensionality reduction. The optimized classifier demonstrated 78% ± 4% accuracy with balanced category performance (per-class F1 scores: 0.72–0.84) and average AUC of 0.90. Historical phase features dominated predictive importance. The integrated treatment recommendation system achieved 88.7% within-category agreement for offloading prescriptions across all chronicity levels. Dressing recommendations demonstrated chronicity-stratified performance, with match rates declining from 83.7% for acute wounds to 5.6% for very chronic wounds, appropriately reflecting clinical reality that treatment-resistant wounds require individualized therapeutic experimentation.

This framework demonstrates potential for next-appointment trajectory prediction using accessible clinical metadata without specialized imaging, pending prospective validation. The chronicity-dependent recommendation performance appropriately distinguishes wounds amenable to standardized protocols from treatment-resistant cases requiring iterative experimentation.