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Simultaneous Measurement Imputation and Rehabilitation Outcome Prediction for Achilles Tendon Rupture
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [sv]

Achilles tendonbrott (Achilles Tendon Rupture, ATR) är en av de typiska mjukvävnadsskadorna. Rehabilitering efter sådana muskuloskeletala skador förblir en långvarig process med ett mycket variet resultat. Att kunna förutsäga rehabiliteringsresultat exakt är avgörande för beslutsfattande stöduppdrag. I detta arbete designar vi en probabilistisk modell för att förutse rehabiliteringsresultat för ATR med hjälp av en klinisk kohort med många saknade poster. Vår modell är tränad från början till slutet för att samtidigt förutsäga de saknade inmatningarna och rehabiliteringsresultat. Vi utvärderar vår modell och jämför med flera baslinjer, inklusive flerstegsmetoder. Experimentella resultat visar överlägsenheten hos vår modell över dessa flerstadiga tillvägagångssätt med olika dataimuleringsmetoder för ATR rehabiliterings utfalls prognos.

Abstract [en]

Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such musculoskeletal injuries remains a prolonged process with a very variable outcome. Being able to predict the rehabilitation outcome accurately is crucial for treatment decision support. In this work, we design a probabilistic model to predict the rehabilitation outcome for ATR using a clinical cohort with numerous missing entries. Our model is trained end-to-end in order to simultaneously predict the missing entries and the rehabilitation outcome. We evaluate our model and compare with multiple baselines, including multi-stage methods. Experimental results demonstrate the superiority of our model over these baseline multi-stage approaches with various data imputation methods for ATR rehabilitation outcome prediction.

Place, publisher, year, edition, pages
2018. , p. 85
Series
TRITA-EECS-EX ; 2018:318
Keywords [en]
Machine learning, Probabilistic graphical models, Healthcare
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-231485OAI: oai:DiVA.org:kth-231485DiVA, id: diva2:1228796
Subject / course
Computer Technology, Program- and System Development
Educational program
Master of Science - Computer Science
Supervisors
Examiners
Available from: 2018-09-19 Created: 2018-06-28 Last updated: 2018-09-19Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • en-GB
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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