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

Journals

  1. Zou H, Yang W, Wang M, Zhu Q, Liang H, Wu H, Tang L. Predicting length of stay ranges by using novel deep neural networks. Heliyon 2023;9(2):e13573 View
  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
  4. Zhu G, Ozkara B, Chen H, Zhou B, Jiang B, Ding V, Wintermark M. Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study. The Neuroradiology Journal 2024;37(1):74 View
  5. Li X, Dai A, Tran R, Wang J. Identifying miRNA biomarkers for breast cancer and ovarian cancer: a text mining perspective. Breast Cancer Research and Treatment 2023;201(1):5 View
  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, Ji X, Pan Y, Xie G, Zhang H, Li S, 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. Journal of Medical Internet Research 2024;26:e54944 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
  11. Park Y, Kim D, Jeon J, Kim K. Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data. Journal of Medical Internet Research 2024;26:e46608 View
  12. Zhou H, Fang C, Pan Y. Development of a System for Predicting Hospitalization Time for Patients With Traumatic Brain Injury Based on Machine Learning Algorithms: User-Centered Design Case Study. JMIR Human Factors 2024;11:e62866 View