Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug
discovery and development, clinical trials, personalized medicine, pharmacogenomics …

Aim Artificial intelligence (AI) and machine learning (ML) are important areas of computer
science that have recently attracted attention for their application to medicine. However, as …

Rationale: Crisis standards of care (CSCs) guide critical care resource allocation during
crises. Most recommend ranking patients on the basis of their expected in-hospital mortality …

Objective To identify factors influencing implementation of machine learning algorithms
(MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a …

ABSTRACT The National Early Warning Score 2 (NEWS2) is the established track and
trigger system to assess illness severity and risk of deterioration for patients in acute …

Research-based AI-driven systems to predict clinical deterioration are increasingly being
developed, but few are being implemented into clinical workflows. Escobar et al.(AAM) …

Objective To develop and validate a questionnaire to assess the usability of clinical decision
support systems (CDSS) and to assist in the early identification of usability issues that may …

Background Predicting outcomes of critically ill intensive care unit (ICU) patients with
coronavirus-19 disease (COVID-19) is a major challenge to avoid futile, and prolonged ICU …

Background: Sepsis is a leading cause of death in patients with trauma, and the risk of
mortality increases significantly for each hour of delay in treatment. A hypermetabolic …

The twenty-first century saw the rise of “systems biology,” an interdisciplinary approach in
biomedical research integrating computational modeling, cell biology, proteomics, and …