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

Artificial Intelligence (1216) Machine Learning (1401) Clinical Information and Decision Making (1340) Decision Support for Health Professionals (1147)

Published on in Vol 23, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25090, first published .
An Artificial Neural Network Prediction Model for Posttraumatic Epilepsy: Retrospective Cohort Study

An Artificial Neural Network Prediction Model for Posttraumatic Epilepsy: Retrospective Cohort Study

An Artificial Neural Network Prediction Model for Posttraumatic Epilepsy: Retrospective Cohort Study

Authors of this article:

Xueping Wang1 Author Orcid Image ;   Jie Zhong2 Author Orcid Image ;   Ting Lei3 Author Orcid Image ;   Deng Chen1 Author Orcid Image ;   Haijiao Wang1 Author Orcid Image ;   Lina Zhu1 Author Orcid Image ;   Shanshan Chu1 Author Orcid Image ;   Ling Liu1 Author Orcid Image
Article Authors Cited by (6) Tweetations (62) Metrics

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  2. Fang C, Pan Y, Zhao L, Niu Z, Guo Q, Zhao B. A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study. Journal of Medical Internet Research 2022;24(12):e41819 View
  3. Zhu C, Li J, Wei D, Wu L, Zhang Y, Huang H, Lin W. A nomogram to predict the treatment benefit of perampanel in drug-resistant epilepsy patients. Frontiers in Neurology 2023;14 View
  4. Zhao X, Li J, Xie X, Fang Z, Feng Y, Zhong Y, Chen C, Huang K, Ge C, Shi H, Si Y, Zou J. Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study. Journal of Psychosomatic Research 2024;176:111553 View
  5. 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
  6. 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