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Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol

Author

Listed:
  • Joanna F Dipnall
  • Richard Page
  • Lan Du
  • Matthew Costa
  • Ronan A Lyons
  • Peter Cameron
  • Richard de Steiger
  • Raphael Hau
  • Andrew Bucknill
  • Andrew Oppy
  • Elton Edwards
  • Dinesh Varma
  • Myong Chol Jung
  • Belinda J Gabbe

Abstract

Background: Distal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The “Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)” study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data. Methods and design: Adult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS. Discussion: The PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture.

Suggested Citation

  • Joanna F Dipnall & Richard Page & Lan Du & Matthew Costa & Ronan A Lyons & Peter Cameron & Richard de Steiger & Raphael Hau & Andrew Bucknill & Andrew Oppy & Elton Edwards & Dinesh Varma & Myong Chol , 2021. "Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-12, September.
    • Handle: RePEc:plo:pone00:0257361
    DOI: 10.1371/journal.pone.0257361
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    References listed on IDEAS

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    1. Luts, Jan & Molenberghs, Geert & Verbeke, Geert & Van Huffel, Sabine & Suykens, Johan A.K., 2012. "A mixed effects least squares support vector machine model for classification of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 611-628.
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    Cited by:

    1. Matthew Coleman & Joanna F. Dipnall & Myong Chol Jung & Lan Du, 2022. "PreRadE: Pretraining Tasks on Radiology Images and Reports Evaluation Framework," Mathematics, MDPI, vol. 10(24), pages 1-14, December.

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