ReviewBiomedicines2022
Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics.
Review in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Integrating Focused Shockwave Therapy into Rehabilitation for Groin Pain Syndrome: A Prospective Study in Soccer Players.Life (Basel, Switzerland) · 2026Article
- Article
- Integration of Shear-Wave Elastography and Inertial Motion Sensing for Quantitative Monitoring of Tendon Remodeling After Shockwave Therapy in Greater Trochanteric Pain Syndrome.Bioengineering (Basel, Switzerland) · 2026Article
- Development and validation of an interpretable machine learning model integrating baseline multimodal CT perfusion and clinical data for predicting 9-month functional outcomes in acute ischemic stroke.Frontiers in neurology · 2026Article
- Development and validation of a clinical nomogram for predicting 30-day in-hospital mortality in children with moderate-to-severe traumatic brain injury.Frontiers in pediatrics · 2026Article
- Soft-voting classifier for outcome prediction in traumatic brain injury patients with clear consciousness: model development and error pattern analysis.Neurosurgical review · 2025Article
- Prediction of Clinically Significant Improvements During the Interdisciplinary Intensive Outpatient Program for Traumatic Brain Injury Using Machine Learning.Annals of biomedical engineering · 2025Article
- Evidence and unknowns for the relevancy of applying current parenteral nutrition support recommendations among infants born less than 750 g or younger than 25 weeks' gestation: A narrative review.JPEN. Journal of parenteral and enteral nutrition · 2025Review
- MetaRehabVerse: The Great Opportunity to Put the Person's Functioning and Participation at the Center of Healthcare.Neurorehabilitation and neural repair · 2025Review
- Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Development and validation of a model to predict cognitive impairment in traumatic brain injury patients: a prospective observational study.EClinicalMedicine · 2025Article
- Comparison of Machine Learning Models in Predicting Mental Health Sequelae Following Concussion in Youth.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm.PeerJ. Computer science · 2025Article
- Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training.Journal of neuroengineering and rehabilitation · 2024Article
- The Role of Psychological Variables in Predicting Rehabilitation Outcomes After Spinal Cord Injury: An Artificial Neural Networks Study.Journal of clinical medicine · 2024Article
- Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach.JMIR biomedical engineering · 2024Article
- Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach.Journal of functional morphology and kinesiology · 2024Article
- Prediction of poststroke independent walking using machine learning: a retrospective study.BMC neurology · 2024Article
- Artificial neural network analysis of factors affecting functional independence recovery in patients with lumbar stenosis after neurosurgery treatment: An observational cohort study.Journal of orthopaedics · 2024Article
- Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Defining reliable tools for early prediction of outcome is the main target for physicians to guide care decisions in patients with brain injury. The application of machine learning (ML) is rapidly increasing in this field of study, but with a poor translation to clinical practice. This is basically dependent on the uncertainty about the advantages of this novel technique with respect to traditional approaches. In this review we address the main differences between ML techniques and traditional statistics (such as logistic regression, LR) applied for predicting outcome in patients with stroke and traumatic brain injury (TBI). Thirteen papers directly addressing the different performance among ML and LR methods were included in this review. Basically, ML algorithms do not outperform traditional regression approaches for outcome prediction in brain injury. Better performance of specific ML algorithms (such as Artificial neural networks) was mainly described in the stroke domain, but the high heterogeneity in features extracted from low-dimensional clinical data reduces the enthusiasm for applying this powerful method in clinical practice. To better capture and predict the dynamic changes in patients with brain injury during intensive care courses ML algorithms should be extended to high-dimensional data extracted from neuroimaging (structural and fMRI), EEG and genetics.
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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.