Gold standard prognostic models for long-term outcome in patients with severe traumatic
brain injury (TBI) use admission characteristics and are considered useful in some areas but …

Background Timely and accurate outcome prediction plays a critical role in guiding clinical
decisions for hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU …

Introduction The aim of this study was to evaluate the usefulness of the APACHE II (Acute
Physiology and Chronic Health Evaluation II), SAPS II (Simplified Acute Physiology Score II) …

Abstract Machine learning can predict outcomes and determine variables contributing to
precise prediction, and can thus classify patients with different risk factors of outcomes. This …

Abstract Background and Objective Prediction of medical outcomes may potentially benefit
from using modern statistical modeling techniques. We aimed to externally validate …

Background Precise models are necessary to estimate mortality risk following traumatic
injury to inform clinical decision making or quantify hospital performance. The Trauma and …

Background Machine learning (ML) model is increasingly used to predict short-term
outcome in critically ill patients, but the study for long-term outcome is sparse. We used …

This study validates risk prediction models for acute traumatic brain injury (TBI) in critical
care units in the United Kingdom and recalibrates the models to this population. The Risk …

Background Traumatic brain injury (TBI) has substantial physical, psychological, social and
economic impacts, with high rates of morbidity and mortality. Considering its high incidence …

Objective: Predicting prognosis of in-hospital patients is critical. However, it is challenging to
accurately predict the life and death of certain patients at certain period. To determine …