Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation
This work addresses a domain-specific problem for clinicians and patients in musculoskeletal injury rehabilitation, offering an incremental improvement by integrating imputation and prediction into a single framework.
The authors tackled the problem of predicting Achilles Tendon Rupture rehabilitation outcomes from clinical data with extensive missing entries by developing an end-to-end probabilistic framework that simultaneously imputes missing data and predicts outcomes, demonstrating clear superiority over traditional two-stage methods.
Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic method for predicting the ATR rehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes. In this work, we design an end-to-end probabilistic framework to impute missing data entries and predict rehabilitation outcomes simultaneously. We evaluate our model on a real-life ATR clinical cohort, comparing with various baselines. The proposed method demonstrates its clear superiority over traditional methods which typically perform imputation and prediction in two separate stages.