跟腱断裂康复治疗的同步测量插补与预后预测
Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation
摘要：
跟腱断裂是一种典型的软组织损伤。这种肌肉骨骼损伤后的康复仍然是一个长期的过程，结果非常多变。准确预测康复结果对于治疗决策支持至关重要。然而，由于从ATR患者记录的数据中的大量缺失条目以及测量和结果之间的复杂非线性关系，训练用于从治疗数据预测ATR康复结果的自动方法是具有挑战性的。在这项工作中，我们设计了一个端到端的概率框架，同时估算缺失的数据条目和预测康复结果。我们在现实生活中的ATR临床队列中评估我们的模型，并与各种基线进行比较。所提出的方法表明其明显优于传统的方法，通常在两个单独的阶段进行插补和预测。
英文摘要：
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.
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