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Simultaneous Measur...
Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation
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- Hamesse, Charles (författare)
- KTH,Royal Military Academy, Brussels, Belgium
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- Tu, Ruibo (författare)
- KTH,Robotik, perception och lärande, RPL
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- Ackermann, Paul (författare)
- Karolinska University Hospital, Stockholm, Sweden
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- Kjellström, Hedvig, 1973- (författare)
- KTH,Robotik, perception och lärande, RPL
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- Zhang, Cheng (författare)
- Microsoft Research, Cambridge, UK
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(creator_code:org_t)
- 2019
- 2019
- Engelska.
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Ingår i: Proceedings of Machine Learning Research 106.
- Relaterad länk:
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https://www.mlforhc....
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https://kth.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)