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

Machine Learning (1400) Natural Language Processing (650) Decision Support for Health Professionals (1145) Electronic Health Records (971) Stroke (150) Clinical Information and Decision Making (1339)

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

Authors of this article:

Sheng-Feng Sung1, 2 Author Orcid Image ;   Cheng-Yang Hsieh3 Author Orcid Image ;   Ya-Han Hu4 Author Orcid Image
Article Authors Cited by (8) Tweetations (7) Metrics

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
  6. Lee H, Schwamm L, Sansing L, Kamel H, de Havenon A, Turner A, Sheth K, Krishnaswamy S, Brandt C, Zhao H, Krumholz H, Sharma R. StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. npj Digital Medicine 2024;7(1) View
  7. Chidera Egegamuka N, Ekedebe N, Kingsley Kelechi A, Raymond Patrick O, Chidinma C O. Development of Random Forest Model for Stroke Prediction. International Journal of Innovative Science and Research Technology (IJISRT) 2024:2783 View
  8. Lefkovitz I, Walsh S, Blank L, Jetté N, Kummer B. Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review. JMIR Neurotechnology 2024;3:e51822 View