  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.
