Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29120, first published .
Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

Journals

  1. Hoekstra O, Hurst W, Tummers J. Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review. Healthcare Analytics 2022;2:100107 View
  2. Chen M, Tan X, Padman R. A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study. Journal of Medical Internet Research 2023;25:e36477 View
  3. De Rosario H, Pitarch-Corresa S, Pedrosa I, Vidal-Pedrós M, de Otto-López B, García-Mieres H, Álvarez-Rodríguez L. Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review. JMIR Medical Informatics 2023;11:e48693 View
  4. Hung L, Su Y, Sun J, Huang W, Sung S. Clinical narratives as a predictor for prognosticating functional outcomes after intracerebral hemorrhage. Journal of the Neurological Sciences 2023;453:120807 View