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Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation
KTH. Royal Military Academy, Brussels, Belgium.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.ORCID iD: 0000-0003-1356-9653
Karolinska University Hospital, Stockholm, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-5750-9655
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2019 (English)In: Proceedings of Machine Learning Research 106, 2019Conference paper, Published 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.

Place, publisher, year, edition, pages
2019.
National Category
Engineering and Technology Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-258070OAI: oai:DiVA.org:kth-258070DiVA, id: diva2:1349749
Conference
Machine Learning for Healthcare 2019, University of Michigan, Ann Arbor, MI August 8-10, 2019
Note

QC 20190912

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-17Bibliographically approved

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fulltext(683 kB)2 downloads
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CiteExportLink to record
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