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

Machine Learning (1501) Decision Support for Health Professionals (1210) Clinical Information and Decision Making (1434)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32508, first published .
Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study

Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study

Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study

Authors of this article:

Po-Yuan Su1 Author Orcid Image ;   Yi-Chia Wei2, 3, 4 Author Orcid Image ;   Hao Luo1 Author Orcid Image ;   Chi-Hung Liu5, 6 Author Orcid Image ;   Wen-Yi Huang2, 6 Author Orcid Image ;   Kuan-Fu Chen7, 8 Author Orcid Image ;   Ching-Po Lin3 Author Orcid Image ;   Hung-Yu Wei1 Author Orcid Image ;   Tsong-Hai Lee5, 6 Author Orcid Image
Article Authors Cited by (16) Tweetations (2) Metrics

Journals

  1. Chung C, Su E, Chen J, Chen Y, Kuo C. XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke. Diagnostics 2023;13(5):842 View
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  3. Lai H, Kuo E, Li R, Chuang T, Huang Y, Hsieh J, Chang T, Huang Y, Hsieh W, Su Y, Liu M. Using an Interpretable Machine Learning Model to Predict Corifollitropin Alfa Protocol. Fertility & Reproduction 2023;05(01):50 View
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  5. Gebeye L, Dessie E, Yimam J. Predictors of micronutrient deficiency among children aged 6–23 months in Ethiopia: a machine learning approach. Frontiers in Nutrition 2024;10 View
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  9. Huang Q, Shou G, Shi B, Li M, Zhang S, Han M, Hu F. Machine learning is an effective method to predict the 3-month prognosis of patients with acute ischemic stroke. Frontiers in Neurology 2024;15 View
  10. Pucar Đ, Šimović V. Predictive modeling of stroke occurrence using Python for improved risk assessment. Journal of Process Management and New Technologies 2024;12(1-2):110 View
  11. Vu T, Kokubo Y, Inoue M, Yamamoto M, Mohsen A, Martin-Morales A, Inoué T, Dawadi R, Araki M. Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study. Journal of Cardiovascular Development and Disease 2024;11(7):207 View
  12. Cai G, Xu J, Zhang C, Jiang J, Chen G, Chen J, Liu Q, Xu G, Lan Y. Identifying biomarkers related to motor function in chronic stroke: A fNIRS and TMS study. CNS Neuroscience & Therapeutics 2024;30(7) View
  13. Horváth L, Zsemla G. A neurovascularis és mentális betegségek regisztereinek integrált rendszere. Orvosi Hetilap 2024;165(24-25):955 View
  14. Rodríguez Mallma M, Zuloaga-Rotta L, Borja-Rosales R, Rodríguez Mallma J, Vilca-Aguilar M, Salas-Ojeda M, Mauricio D. Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review. Neurology International 2024;16(6):1285 View
  15. Sorayaie Azar A, Samimi T, Tavassoli G, Naemi A, Rahimi B, Hadianfard Z, Wiil U, Nazarbaghi S, Bagherzadeh Mohasefi J, Lotfnezhad Afshar H. Predicting stroke severity of patients using interpretable machine learning algorithms. European Journal of Medical Research 2024;29(1) View