Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation
This work addresses personalized therapy decision-making for ATR patients, but it is incremental as it applies an existing method to a new medical dataset.
The authors tackled the problem of imputing incomplete medical tests and predicting patient outcomes for Achilles Tendon Rupture rehabilitation by applying a collaborative filtering method (MatchBox) to a dataset of 374 patients, demonstrating feasibility through initial qualitative evaluations.
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.