Machine Learning Approaches to Prognostication in Traumatic Brain Injury
Purpose of Review This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data—including clinical, imaging, and physiological inputs—to identify...
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| Published in | Current neurology and neuroscience reports Vol. 25; no. 1; p. 19 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.12.2025
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1528-4042 1534-6293 1534-6293 |
| DOI | 10.1007/s11910-025-01405-x |
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| Abstract | Purpose of Review
This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data—including clinical, imaging, and physiological inputs—to identify intricate non-linear relationships that traditional methods might overlook.
Recent Findings
ML algorithms of clinical features, neuroimaging, and metrics from the autonomic nervous system enhance the early detection of clinical deterioration and improve outcome prediction. Challenges persist, including issues of data variability, model interpretability, and overfitting. However, advancements in model standardization and validation are key to enhancing their clinical applicability.
Summary
ML-based, multimodal approaches offer transformative potential for personalized treatment planning and patient management. Future directions include integrating digital twins and real-time continuous data analysis, reinforcing the idea that comprehensive data amalgamation is essential for precise, adaptive prognostication and decision-making in neurocritical care, ultimately leading to better patient outcomes. |
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| AbstractList | This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data-including clinical, imaging, and physiological inputs-to identify intricate non-linear relationships that traditional methods might overlook.
ML algorithms of clinical features, neuroimaging, and metrics from the autonomic nervous system enhance the early detection of clinical deterioration and improve outcome prediction. Challenges persist, including issues of data variability, model interpretability, and overfitting. However, advancements in model standardization and validation are key to enhancing their clinical applicability. ML-based, multimodal approaches offer transformative potential for personalized treatment planning and patient management. Future directions include integrating digital twins and real-time continuous data analysis, reinforcing the idea that comprehensive data amalgamation is essential for precise, adaptive prognostication and decision-making in neurocritical care, ultimately leading to better patient outcomes. Purpose of ReviewThis review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data—including clinical, imaging, and physiological inputs—to identify intricate non-linear relationships that traditional methods might overlook.Recent FindingsML algorithms of clinical features, neuroimaging, and metrics from the autonomic nervous system enhance the early detection of clinical deterioration and improve outcome prediction. Challenges persist, including issues of data variability, model interpretability, and overfitting. However, advancements in model standardization and validation are key to enhancing their clinical applicability.SummaryML-based, multimodal approaches offer transformative potential for personalized treatment planning and patient management. Future directions include integrating digital twins and real-time continuous data analysis, reinforcing the idea that comprehensive data amalgamation is essential for precise, adaptive prognostication and decision-making in neurocritical care, ultimately leading to better patient outcomes. This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data-including clinical, imaging, and physiological inputs-to identify intricate non-linear relationships that traditional methods might overlook.PURPOSE OF REVIEWThis review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data-including clinical, imaging, and physiological inputs-to identify intricate non-linear relationships that traditional methods might overlook.ML algorithms of clinical features, neuroimaging, and metrics from the autonomic nervous system enhance the early detection of clinical deterioration and improve outcome prediction. Challenges persist, including issues of data variability, model interpretability, and overfitting. However, advancements in model standardization and validation are key to enhancing their clinical applicability. ML-based, multimodal approaches offer transformative potential for personalized treatment planning and patient management. Future directions include integrating digital twins and real-time continuous data analysis, reinforcing the idea that comprehensive data amalgamation is essential for precise, adaptive prognostication and decision-making in neurocritical care, ultimately leading to better patient outcomes.RECENT FINDINGSML algorithms of clinical features, neuroimaging, and metrics from the autonomic nervous system enhance the early detection of clinical deterioration and improve outcome prediction. Challenges persist, including issues of data variability, model interpretability, and overfitting. However, advancements in model standardization and validation are key to enhancing their clinical applicability. ML-based, multimodal approaches offer transformative potential for personalized treatment planning and patient management. Future directions include integrating digital twins and real-time continuous data analysis, reinforcing the idea that comprehensive data amalgamation is essential for precise, adaptive prognostication and decision-making in neurocritical care, ultimately leading to better patient outcomes. Purpose of Review This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data—including clinical, imaging, and physiological inputs—to identify intricate non-linear relationships that traditional methods might overlook. Recent Findings ML algorithms of clinical features, neuroimaging, and metrics from the autonomic nervous system enhance the early detection of clinical deterioration and improve outcome prediction. Challenges persist, including issues of data variability, model interpretability, and overfitting. However, advancements in model standardization and validation are key to enhancing their clinical applicability. Summary ML-based, multimodal approaches offer transformative potential for personalized treatment planning and patient management. Future directions include integrating digital twins and real-time continuous data analysis, reinforcing the idea that comprehensive data amalgamation is essential for precise, adaptive prognostication and decision-making in neurocritical care, ultimately leading to better patient outcomes. |
| ArticleNumber | 19 |
| Author | Dalton, Kenneth Chen, Lujie Karen Badjatia, Neeraj Wang, Tina I. Felix, Ryan B. Hu, Peter Yang, Shiming Podell, Jamie |
| Author_xml | – sequence: 1 givenname: Neeraj orcidid: 0000-0003-1509-9034 surname: Badjatia fullname: Badjatia, Neeraj email: nbadjatia@som.umaryland.edu organization: Program in Trauma, University of Maryland School of Medicine, Department of Neurology, University of Maryland School of Medicine, Department of Neurosurgery, University of Maryland School of Medicine – sequence: 2 givenname: Jamie surname: Podell fullname: Podell, Jamie organization: Program in Trauma, University of Maryland School of Medicine, Department of Neurology, University of Maryland School of Medicine – sequence: 3 givenname: Ryan B. orcidid: 0000-0002-5375-1313 surname: Felix fullname: Felix, Ryan B. organization: Program in Trauma, University of Maryland School of Medicine, Fischell Department of Bioengineering, University of Maryland – sequence: 4 givenname: Lujie Karen orcidid: 0000-0002-7185-8405 surname: Chen fullname: Chen, Lujie Karen organization: Department of Information Systems, University of Maryland – sequence: 5 givenname: Kenneth surname: Dalton fullname: Dalton, Kenneth organization: Program in Trauma, University of Maryland School of Medicine, Department of Neurology, University of Maryland School of Medicine – sequence: 6 givenname: Tina I. orcidid: 0000-0003-3237-1693 surname: Wang fullname: Wang, Tina I. organization: Department of Neurosurgery, University of Maryland School of Medicine – sequence: 7 givenname: Shiming orcidid: 0000-0003-0338-4268 surname: Yang fullname: Yang, Shiming organization: Program in Trauma, University of Maryland School of Medicine, University of Maryland Institute for Health Computing (UM-IHC) – sequence: 8 givenname: Peter orcidid: 0000-0001-7332-758X surname: Hu fullname: Hu, Peter organization: Program in Trauma, University of Maryland School of Medicine, University of Maryland Institute for Health Computing (UM-IHC), Department of Epidemiology & Public Health, University of Maryland School of Medicine |
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This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the... This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML... Purpose of ReviewThis review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the... |
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| SubjectTerms | Accuracy Algorithms Anesthesia Autonomic nervous system Biomarkers Brain damage Brain Injuries, Traumatic - diagnosis Brain Injuries, Traumatic - diagnostic imaging Brain Injuries, Traumatic - physiopathology Clinical outcomes Consciousness Decision making Disability FDA approval Humans Learning algorithms Machine Learning Magnetic resonance imaging Medicine Medicine & Public Health Mortality Neuroimaging Neuroimaging - methods Neurology Neurosciences Patients Physiology Prognosis Public health Review Topical Collection on Neurotrauma Traumatic brain injury |
| Title | Machine Learning Approaches to Prognostication in Traumatic Brain Injury |
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