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Related Experiment Video

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Gross motor function prediction using natural language processing in cerebral palsy.

Kelly Greve1,2, Yizhao Ni3, Amy F Bailes1,2

  • 1Division of Occupational Therapy and Physical Therapy, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.

Developmental Medicine and Child Neurology
|June 6, 2022
PubMed
Summary
This summary is machine-generated.

Natural language processing (NLP) accurately predicts ambulatory status in children with cerebral palsy (CP). NLP also effectively differentiates Gross Motor Function Classification System (GMFCS) levels using electronic health records (EHRs).

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Area of Science:

  • Medical Informatics
  • Rehabilitation Medicine
  • Pediatric Neurology

Background:

  • Cerebral palsy (CP) significantly impacts motor function and ambulatory status.
  • Accurate classification of motor function, such as Gross Motor Function Classification System (GMFCS) levels, is crucial for patient management.
  • Electronic health records (EHRs) contain rich clinical information that is often unstructured.

Purpose of the Study:

  • To evaluate the efficacy of natural language processing (NLP) in predicting ambulatory status and GMFCS levels from clinical notes in EHRs for patients with CP.
  • To assess the performance of NLP models in classifying different GMFCS levels.

Main Methods:

  • A retrospective cohort study included 2483 patients (aged 8-26 years) with CP diagnosed between 2009 and 2020.
  • NLP techniques were applied to unstructured clinical notes within EHRs.
  • Performance was measured using sensitivity, specificity, positive predictive value, and AUC for predicting ambulatory status and GMFCS levels.

Main Results:

  • NLP achieved 81% positive predictive value and 0.89 AUC for predicting ambulatory status.
  • The model showed good differentiation for GMFCS levels IV and V (0.75 AUC) and between levels I-II and III (0.71 AUC).
  • High specificity (96%) was observed for differentiating GMFCS levels I-II from III.

Conclusions:

  • NLP applied to EHRs is a powerful tool for predicting ambulatory status in children with CP.
  • NLP demonstrates good potential for classifying GMFCS levels, aiding in personalized functional characterization and management.
  • Integration of these NLP methods into EHR systems could provide real-time clinical insights.