Development of Machine Learning Algorithms for Identifying Patients With Limited Health Literacy
Elastic-Net Penalized Logistic Regression had the best performance when compared with other ML algorithms with a c-statistic of 0.766, calibration slope/intercept of 1.044/-0.037, and a Brier score of 0.179. Over one-third of patients presenting to an outpatient spine center were found to have limited HL. While this algorithm is far from being used in clinical practice, ML algorithms offer a potential opportunity for identifying patients at risk for limited HL without administering in-person HL assessments. This could possibly enable screening and early intervention to mitigate the potential negative consequences of limited HL without taxing the existing clinical workflow.
Authors
- Jorrit‐Jan Verlaan (ORCID: https://orcid.org/0000-0001-8105-6660)
- Dylan Koole (ORCID: https://orcid.org/0000-0002-2770-5655)
- Oscar Shen (ORCID: https://orcid.org/0000-0003-3854-1470)
- Tom M. de Groot (ORCID: https://orcid.org/0000-0002-8980-5707)
- Amanda Lans (ORCID: https://orcid.org/0000-0002-4959-6517)
- Joseph H. Schwab (ORCID: https://orcid.org/0000-0003-1156-3864)
Institutions
- University Medical Center Groningen (NL)
- Harvard University (US)
- University of Groningen (NL)
- Utrecht University (NL)
- Leiden University Medical Center (NL)
- University Medical Center Utrecht (NL)
- Massachusetts General Hospital (US)
Publication Details
- Journal
- Journal of Evaluation in Clinical Practice
- Published
- 2024-11-22
- DOI
- https://doi.org/10.1111/jep.14248
- Citations
- 3
- Primary Topic
- Health Literacy and Information Accessibility
- Type
- article
- Field-Weighted Citation Impact
- 2.55