Published on in Vol 10, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29806, first published .
Early Prediction of Functional Outcomes After Acute Ischemic Stroke Using Unstructured Clinical Text: Retrospective Cohort Study

Early Prediction of Functional Outcomes After Acute Ischemic Stroke Using Unstructured Clinical Text: Retrospective Cohort Study

Early Prediction of Functional Outcomes After Acute Ischemic Stroke Using Unstructured Clinical Text: Retrospective Cohort Study

Journals

  1. Sung S, Sung K, Pan R, Lee P, Hu Y. Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing. Frontiers in Cardiovascular Medicine 2022;9 View
  2. Suh H, Tully J, Meineke M, Waterman R, Gabriel R. Identification of Preanesthetic History Elements by a Natural Language Processing Engine. Anesthesia & Analgesia 2022;135(6):1162 View
  3. Tsai H, Hsieh C, Sung S. Application of machine learning and natural language processing for predicting stroke-associated pneumonia. Frontiers in Public Health 2022;10 View
  4. 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
  5. 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