ArticleNPJ digital medicine2021
A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Neuroprognostication After Cardiac Arrest: From Test-Centered Prediction to Ethically Constrained, Longitudinal Decision-Making.Neurocritical care · 2026Article
- Explainable deep learning framework incorporating medical knowledge for insulin titration in diabetes.Communications medicine · 2026Article
- Development and validation of an automatic machine learning-based myocardial contusion prediction model in rib fracture patients: a retrospective multicenter cohort study.Frontiers in medicine · 2026Article
- A reliable and explainable deep learning framework for clinical-grade endocrine disorder risk prediction and decision support.Frontiers in artificial intelligence · 2026Article
- Soft-voting classifier for outcome prediction in traumatic brain injury patients with clear consciousness: model development and error pattern analysis.Neurosurgical review · 2025Article
- Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review.NPJ digital medicine · 2025Article
- A Hands-On Introduction to Data Analytics for Biomedical Research.Function (Oxford, England) · 2025Review
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- Machine Learning Approaches to Prognostication in Traumatic Brain Injury.Current neurology and neuroscience reports · 2025Review
- Predicting clinical pathways of traumatic brain injuries (TBIs) through process mining.NPJ digital medicine · 2025Article
- Machine learning-enhanced causal inference of surgical decisions and rehabilitation strategies in traumatic brain injury.Frontiers in neurology · 2025Article
- Artificial intelligence in financial market prediction: advancements in machine learning for stock price forecasting.Frontiers in artificial intelligence · 2025Review
- Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.Journal of Korean Neurosurgical Society · 2024Article
- Discovery of generalizable TBI phenotypes using multivariate time-series clustering.Computers in biology and medicine · 2024Article
- Article
- Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study.The neuroradiology journal · 2024Article
- Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department.Diagnostics (Basel, Switzerland) · 2023Article
- A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping.Journal of biomedical informatics · 2023Article
- Predicting posttraumatic epilepsy using admission electroencephalography after severe traumatic brain injury.Epilepsia · 2023Article
- Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models.Nature biomedical engineering · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Prognosis of the long-term functional outcome of traumatic brain injury is essential for personalized management of that injury. Nonetheless, accurate prediction remains unavailable. Although machine learning has shown promise in many fields, including medical diagnosis and prognosis, such models are rarely deployed in real-world settings due to a lack of transparency and trustworthiness. To address these drawbacks, we propose a machine learning-based framework that is explainable and aligns with clinical domain knowledge. To build such a framework, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score's trustworthiness. Using 831 patients with moderate or severe traumatic brain injury to build a model using the proposed framework, an area under the receiver operating characteristic curve (AUC) and accuracy of 0.8085 and 0.7488 were achieved, respectively, in determining which patients will experience poor functional outcomes. The performance of the machine learning classifier is not adversely affected by the imposition of statistical and domain knowledge "checks and balances". Finally, through a case study, we demonstrate how the decision made by a model might be biased if it is not audited carefully.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.