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

Clinical Information and Decision Making (1339) Decision Support for Health Professionals (1145) Artificial Intelligence (1214) Machine Learning (1400) Clinical Informatics (918)

Published on in Vol 24, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41819, first published .
A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study

A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study

A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study

Authors of this article:

Cheng Fang1 Author Orcid Image ;   Yifeng Pan2 Author Orcid Image ;   Luotong Zhao1 Author Orcid Image ;   Zhaoyi Niu1 Author Orcid Image ;   Qingguo Guo1 Author Orcid Image ;   Bing Zhao1 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

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  2. Tsuyuki C, Hiraga H, Sudo M, Ueda T, Seo K, Minatozaki M, Fukuda Y, Okuda Y, Iwasaki H, Naito H, Lu D. Estimability study on the age of toddlers’ gait development based on gait parameters. Scientific Reports 2023;13(1) View
  3. Kuo H, Liu T, Huang Y, Chin S, Ro L, Kuo H. Differential Diagnostic Value of Machine Learning–Based Models for Embolic Stroke. Clinical and Applied Thrombosis/Hemostasis 2023;29 View
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  6. Pan Y, Fang C, Zhu X, Wan J. Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study. DIGITAL HEALTH 2023;9 View
  7. Schonfeld E, Mordekai N, Berg A, Johnstone T, Shah A, Shah V, Haider G, Marianayagam N, Veeravagu A. Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations. Cureus 2024 View
  8. Zhu J, Shan Y, Li Y, Wu X, Gao G. Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms. World Neurosurgery 2024;185:e1348 View
  9. Fang C, Pan Y, Ji X, Li S, Xie G, Zhang H, Wan J. Combining clinical-radiomics features with machine learning methods for building models to predict postoperative recurrence in patients with chronic subdural hematoma: Retrospective Cohort Study (Preprint). Journal of Medical Internet Research 2023 View
  10. Zhu J, Shan Y, Li Y, Xu X, Wu X, Xue Y, Gao G. Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury. Intensive Care Medicine Experimental 2024;12(1) View