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  • 1.
    Hamesse, Charles
    et al.
    KTH.
    Ackermann, P.
    Kjellström, Hedvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Zhang, C.
    Simultaneous measurement imputation and outcome prediction for achilles tendon rupture rehabilitation2018In: CEUR Workshop Proceedings, CEUR-WS , 2018, Vol. 2142, p. 82-86Conference paper (Refereed)
    Abstract [en]

    Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Accurately predicting the rehabilitation outcome of ATR using noisy measurements with missing entries is crucial for treatment decision support. In this work, we design a probabilistic model that simultaneously predicts the missing measurements and the rehabilitation outcome in an end-to-end manner. We evaluate our model and compare it with multiple baselines including multi-stage methods using an ATR clinical cohort. Experimental results demonstrate the superiority of our model for ATR rehabilitation outcome prediction.

  • 2.
    Hamesse, Charles
    et al.
    KTH. Royal Military Academy, Brussels, Belgium.
    Tu, Ruibo
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Ackermann, Paul
    Karolinska University Hospital, Stockholm, Sweden.
    Kjellström, Hedvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Zhang, Cheng
    Microsoft Research, Cambridge, UK.
    Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation2019In: Proceedings of Machine Learning Research 106, 2019Conference paper (Refereed)
    Abstract [en]

    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 AT Rrehabilitation 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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