Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation

TL;DR

This paper applies a collaborative filtering recommender system to impute missing medical data and predict outcomes for Achilles Tendon Rupture patients, demonstrating feasibility with real patient data.

Contribution

It introduces a novel application of the MatchBox recommender system framework for medical data imputation and prediction in ATR rehabilitation.

Findings

Feasibility demonstrated on real ATR patient data

Personalized data imputation achieved

Initial qualitative evaluation conducted

Abstract

Imputing incomplete medical tests and predicting patient outcomes are crucial for guiding the decision making for therapy, such as after an Achilles Tendon Rupture (ATR). We formulate the problem of data imputation and prediction for ATR relevant medical measurements into a recommender system framework. By applying MatchBox, which is a collaborative filtering approach, on a real dataset collected from 374 ATR patients, we aim at offering personalized medical data imputation and prediction. In this work, we show the feasibility of this approach and discuss potential research directions by conducting initial qualitative evaluations.