Predicting future falls in older people using natural language processing of general practitioners' clinical notes.

Journal: Age and ageing
Published Date: Apr 1, 2023

Abstract

BACKGROUND: Falls in older people are common and morbid. Prediction models can help identifying individuals at higher fall risk. Electronic health records (EHR) offer an opportunity to develop automated prediction tools that may help to identify fall-prone individuals and lower clinical workload. However, existing models primarily utilise structured EHR data and neglect information in unstructured data. Using machine learning and natural language processing (NLP), we aimed to examine the predictive performance provided by unstructured clinical notes, and their incremental performance over structured data to predict falls.

Authors

  • Noman Dormosh
    Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands.
  • Martijn C Schut
    Department of Medical Informatics, Amsterdam UMC (location AMC), Amsterdam, The Netherlands.
  • Martijn W Heymans
    Department of Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
  • Otto Maarsingh
    Department of General practice, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
  • Jonathan Bouman
    Department of General Practice, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands.
  • Nathalie van der Velde
    Department of Internal Medicine, Section of Geriatric Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands.
  • Ameen Abu-Hanna
    Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands.

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