O 046 – A flexible omnibus matching algorithm (FOMA) to support treatment decisions for children with cerebral palsy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Treatment outcomes among children with cerebral palsy are mediocre, unpredictable, and stagnant over several decades. We use a nearest-neighbors matching algorithm to predict outcomes from individuals. The algorithm allows clinician input regarding the relevant matching parameters, treatment of choice, and outcome of interest. The algorithm was tested on 1092 limbs that underwent single-event multi-level surgery. Predictions compared favorably to previous regression-based approaches, producing smaller root mean squared errors across the spectrum of kinematics.

Original languageEnglish (US)
Title of host publication Gait and Posture
DOIs
StateAccepted/In press - Jan 1 2018

Publication series

NameGait and Posture
PublisherElsevier
ISSN (Print)0966-6362

Keywords

  • Cerebral palsy
  • Machine learning
  • Nearest neighbor
  • Outcome
  • Prediction

PubMed: MeSH publication types

  • Journal Article

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