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Simultaneous measurement imputation and outcome prediction for achilles tendon rupture rehabilitation
KTH.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.ORCID-id: 0000-0002-5750-9655
2018 (engelsk)Inngår i: CEUR Workshop Proceedings, CEUR-WS , 2018, Vol. 2142, s. 82-86Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
CEUR-WS , 2018. Vol. 2142, s. 82-86
Serie
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2142
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-238396Scopus ID: 2-s2.0-85050917241OAI: oai:DiVA.org:kth-238396DiVA, id: diva2:1261619
Konferanse
1st Joint Workshop on AI in Health, AIH 2018, Stockholm, Sweden, 13 July 2018 through 14 July 2018
Merknad

QC 20181108

Tilgjengelig fra: 2018-11-08 Laget: 2018-11-08 Sist oppdatert: 2018-11-08bibliografisk kontrollert

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Kjellström, Hedvig

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