This paper is in the following e-collection/theme issue:

Non-randomized Protocols and Methods (ehealth) (568) Electronic Health Records (1064) Machine Learning (1480) Natural Language Processing (696) Spinal Cord Injury Rehabilitation (62) Pressure Ulcers and Decubitus Wounds (25)

Published on in Vol 6, No 1 (2017): January

Leveraging Electronic Health Care Record Information to Measure Pressure Ulcer Risk in Veterans With Spinal Cord Injury: A Longitudinal Study Protocol

Leveraging Electronic Health Care Record Information to Measure Pressure Ulcer Risk in Veterans With Spinal Cord Injury: A Longitudinal Study Protocol

Leveraging Electronic Health Care Record Information to Measure Pressure Ulcer Risk in Veterans With Spinal Cord Injury: A Longitudinal Study Protocol

Authors of this article:

Stephen L Luther1, 2 Author Orcid Image ;   Susan S Thomason1, 3 Author Orcid Image ;   Sunil Sabharwal4 Author Orcid Image ;   Dezon K Finch1 Author Orcid Image ;   James McCart1, 5 Author Orcid Image ;   Peter Toyinbo1 Author Orcid Image ;   Lina Bouayad1 Author Orcid Image ;   Michael E Matheny6, 7, 8, 9 Author Orcid Image ;   Glenn T Gobbel7, 10 Author Orcid Image ;   Gail Powell-Cope1, 2, 11 Author Orcid Image
Article Authors Cited by (5) Tweetations (6) Metrics

Journals

  1. Gould L, Bohn G, Bryant R, Paine T, Couch K, Cowan L, McFarland F, Simman R. Pressure ulcer summit 2018: An interdisciplinary approach to improve our understanding of the risk of pressure‐induced tissue damage. Wound Repair and Regeneration 2019;27(5):497 View
  2. Bogie K, Roggenkamp S, Zeng N, Seton J, Schwartz K, Henzel M, Richmond M, Sun J, Zhang G. Development of Predictive Informatics Tool Using Electronic Health Records to Inform Personalized Evidence-Based Pressure Injury Management for Veterans with Spinal Cord Injury. Military Medicine 2021;186(Supplement_1):651 View
  3. Bazoge A, Morin E, Daille B, Gourraud P. Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review. JMIR Medical Informatics 2023;11:e42477 View
  4. Luther S, Thomason S, Sabharwal S, Finch D, McCart J, Toyinbo P, Bouayad L, Lapcevic W, Hahm B, Hauser R, Matheny M, Powell-Cope G. Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury. Spinal Cord 2023;61(9):513 View
  5. Ma Y, He X, Yang T, Yang Y, Yang Z, Gao T, Yan F, Yan B, Wang J, Han L. Evaluation of the risk prediction model of pressure injuries in hospitalized patient: A systematic review and meta‐analysis. Journal of Clinical Nursing 2024 View