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Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation
KTH. Royal Military Academy, Brussels, Belgium.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.ORCID-id: 0000-0003-1356-9653
Karolinska University Hospital, Stockholm, Sweden.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.ORCID-id: 0000-0002-5750-9655
Vise andre og tillknytning
2019 (engelsk)Inngår i: Proceedings of Machine Learning Research 106, 2019Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2019.
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-258070OAI: oai:DiVA.org:kth-258070DiVA, id: diva2:1349749
Konferanse
Machine Learning for Healthcare 2019, University of Michigan, Ann Arbor, MI August 8-10, 2019
Merknad

QC 20190912

Tilgjengelig fra: 2019-09-09 Laget: 2019-09-09 Sist oppdatert: 2019-09-17bibliografisk kontrollert

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