Chronic diseases are becoming more widespread. Treatment and monitoring of these
diseases require going to hospitals frequently, which increases the burdens of hospitals and …

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients
susceptible to many complications affecting morbidity and mortality. ICU settings require a …

Introduction Early detection and intervention of atrial fibrillation (AF) is a cornerstone for
effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have …

Importance Postoperative complications can significantly impact perioperative care
management and planning. Objectives To assess machine learning (ML) models for …

Background Patient-specific dose prediction improves the efficiency and quality of radiation
treatment planning and reduces the time required to find the optimal plan. In this study, a …

We aimed to build up multiple machine learning models to predict 30-days mortality, and 3
complications including septic shock, thrombocytopenia, and liver dysfunction after open …

Abstract Background and Aim This study aims to construct a strategy that uses assistance
from artificial intelligence (AI) to assist radiologists in the identification of malignant versus …

Purpose: The objective of this study was to evaluate the efficacy of machine learning
algorithms in predicting risks of complications and poor glycemic control in nonadherent …

Every critically ill patient requires care by a critical care pharmacist (CCP) for best possible
outcomes. Indeed, these highly trained professionals generate benefit through direct patient …

Objective To determine if natural language processing (NLP) with machine learning of
unstructured full text documents (a preoperative CT scan) improves the ability to predict …