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

Artificial Intelligence (1371) Natural Language Processing (692) Machine Learning (1465) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Electronic Health Records (1053) Stroke (163)

Published on in Vol 23, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22951, first published .
Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation

Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation

Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation

Authors of this article:

Yiqing Zhao1 Author Orcid Image ;   Sunyang Fu1 Author Orcid Image ;   Suzette J Bielinski1 Author Orcid Image ;   Paul A Decker1 Author Orcid Image ;   Alanna M Chamberlain1 Author Orcid Image ;   Veronique L Roger1 Author Orcid Image ;   Hongfang Liu1 Author Orcid Image ;   Nicholas B Larson1 Author Orcid Image
Article Authors Cited by (17) Tweetations (8) Metrics

Journals

  1. Stroganov O, Fedarovich A, Wong E, Skovpen Y, Pakhomova E, Grishagin I, Fedarovich D, Khasanova T, Merberg D, Szalma S, Bryant J, Bentley B. Mapping of UK Biobank clinical codes: Challenges and possible solutions. PLOS ONE 2022;17(12):e0275816 View
  2. Zhu C, Xu Z, Gu Y, Zheng S, Sun X, Cao J, Song B, Jin J, Liu Y, Wen X, Cheng S, Li J, Wu X. Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study. Journal of Hospital Infection 2022;122:96 View
  3. Gkantzios A, Kokkotis C, Tsiptsios D, Moustakidis S, Gkartzonika E, Avramidis T, Aggelousis N, Vadikolias K. Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors’ Functional Outcome upon Discharge Utilizing Explainable Machine Learning. Diagnostics 2023;13(3):532 View
  4. Zhao K, Zhao Q, Zhou P, Liu B, Zhang Q, Yang M, Mansour A. Can Artificial Intelligence Be Applied to Diagnose Intracerebral Hemorrhage under the Background of the Fourth Industrial Revolution? A Novel Systemic Review and Meta-Analysis. International Journal of Clinical Practice 2022;2022:1 View
  5. Reynolds M, Bunch T, Steinberg B, Ronk C, Kim H, Wieloch M, Lip G. Novel methodology for the evaluation of symptoms reported by patients with newly diagnosed atrial fibrillation: Application of natural language processing to electronic medical records data. Journal of Cardiovascular Electrophysiology 2023;34(4):790 View
  6. Kokkotis C, Giarmatzis G, Giannakou E, Moustakidis S, Tsatalas T, Tsiptsios D, Vadikolias K, Aggelousis N. An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data. Diagnostics 2022;12(10):2392 View
  7. 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
  8. Jin Z, Zhang H, Tai M, Yang Y, Yao Y, Guo Y. Natural Language Processing in a Clinical Decision Support System for the Identification of Venous Thromboembolism: Algorithm Development and Validation. Journal of Medical Internet Research 2023;25:e43153 View
  9. Mridha K, Ghimire S, Shin J, Aran A, Uddin M, Mridha M. Automated Stroke Prediction Using Machine Learning: An Explainable and Exploratory Study With a Web Application for Early Intervention. IEEE Access 2023;11:52288 View
  10. Ivey L, Rodriguez F, Shi H, Chong C, Chen J, Raskind‐Hood C, Downing K, Farr S, Book W. Positive Predictive Value of International Classification of Diseases, Ninth Revision, Clinical Modification , and International Classification of Diseases, Tenth Revision, Clinical Modification , Codes for Identification of Congenital Heart Defects. Journal of the American Heart Association 2023;12(16) View
  11. 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
  12. Sivarajkumar S, Gao F, Denny P, Aldhahwani B, Visweswaran S, Bove A, Wang Y. Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study. JMIR Medical Informatics 2024;12:e52289 View
  13. Seehusen K, Remaley A, Sampson M, Meeusen J, Larson N, Decker P, Killian J, Takahashi P, Roger V, Manemann S, Lam R, Bielinski S. Discordance Between Very Low‐Density Lipoprotein Cholesterol and Low‐Density Lipoprotein Cholesterol Increases Cardiovascular Disease Risk in a Geographically Defined Cohort. Journal of the American Heart Association 2024;13(8) View
  14. Alrowais F, Jamjoom A, Karamti H, Umer M, Alsubai S, Abate A, Ashraf I. Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network. Multimedia Tools and Applications 2024 View
  15. 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
  16. Burford K, Itzkowitz N, Crowe R, Wang H, Lo A, Rundle A. Clinical trauma severity of indoor and outdoor injurious falls requiring emergency medical service response. Injury Epidemiology 2024;11(1) View
  17. J M S, P S. Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: a predicting framework. Scientific Reports 2024;14(1) View