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 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.

Toxin Induced Parkinsonism and Hospitalization Related Adverse Outcome Mitigation for Parkinson's Disease: A Comprehensive Review.

Patients with Parkinson's disease admitted to the hospital have unique presentations. This unique subset of patients requires a multidisciplinary approach with a knowledge-based care team that can demonstrate awareness of complications specific to Parkinson's disease to reduce critical care admissions, morbidity, and mortality. Early recognition of toxic exposures, medication withdrawals, or medication-induced symptoms can reduce morbidity and mortality.