Machine Learning Algorithms For Prediction of Ambulation and Wheelchair Transfer Ability in Spina Bifida.

Details

• Alternative Title:
  Archives of Physical Medicine and Rehabilitation, 2025, v. 106, no. 5: Machine Learning Algorithms For Prediction of Ambulation and Wheelchair Transfer Ability in Spina Bifida.
• Journal Article:
  Archives of Physical Medicine and Rehabilitation
• Personal Author:
  McKernan, Gina ; Mesoros, Matt ; Dicianno, Brad E.
• Description:
  To determine which statistical techniques enhance our ability to predict ambulation and transfer ability in people with spina bifida (SB).
  
  Retrospective cohort study.
  
  Thirty-five US outpatient SB clinic sites.
  
  Individuals (n=4589) with SB aged 5-73 years (median age=13.59y).
  
  Not applicable.
  
  Ambulation ability, which consisted of the following categories: community ambulators, household ambulators, therapeutic ambulators, and nonambulators.
  
  Wheelchair transfer ability, as defined by the ability to transfer in and out of a wheelchair unassisted.
  
  A recurrent neural network (RNN) using a multilayer perceptron discarded 76 cases during case processing, resulting in 4513 that were run through the RNN. The predictions in the resulting testing dataset were 83.22% accurate. Recall was 93.21% for community ambulators, 10.00% for household ambulators, 23.96% for therapeutic ambulators, and 76.70% for nonambulators. Precision was 85.34% for community ambulators, 16.05% for household ambulators, 16.67% for therapeutic ambulators, and 93.47% for nonambulators. Total predictions included 68.39% for community ambulators, 2.25% for household ambulators, 3.83% for therapeutic ambulators, and 25.53% for nonambulators. Correspondingly, the model accurately classified 70% of wheelchair transfers while correctly identifying 97.3% of those able to transfer unassisted.
  
  RNN models hold promise for the prediction of functional outcomes such as ambulation and transfer ability in people with SB, particularly for community ambulators and nonambulators. Compared with the previous work using traditional logistic regression approaches which misclassified 16% of cases, the RNN resulted in greater prediction accuracy with fewer than 7% of cases misclassified.
• Content Notes:
  Author manuscript
• Subjects:
  • Humans
  • Spinal Dysraphism
  • Wheelchairs
  • Middle Aged
  • Female
  • Child
  • Retrospective Studies
  • Male
  • Adolescent
  • Adult
  More +
• Keywords:
  • Ambulation
  • Machine learning
  • Myelomeningocele
  • Population outcomes
  • Registries
  • Rehabilitation
  • Spina bifida
  • Spinal dysraphism
  • Transfer
  • Walking
  More +
• Source:
  Archives of Physical Medicine and Rehabilitation, 2025, v. 106, no. 5
• DOI:
  https://doi.org/10.1016/j.apmr.2024.11.013
• ISSN:
  0003-9993 ; 1532-821X
• Pubmed ID:
  39631515
• Pubmed Central ID:
  PMC12048235
• Document Type:
  Text
• Funding:
  U01 DD001280/DD/NCBDD CDC HHSUnited States/
• Genre:
  Journal Article
• Pages in Document:
  682-687 (13 pdf pages)
• Volume:
  106
• Issue:
  5
• Download URL:
  https://stacks.cdc.gov/view/cdc/261884/cdc_261884_DS1.pdf
• File Type:
  [PDF - 269.00 KB]
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:25a7013757df0f7c24f56b4fd42147789b2773ea4091dc88e1a39451089c874a6580941af75d7fb9557398407c9df1f5596a8573ac1e0a3db00b9c440c1ba10e