Latest AI and machine learning research in infection control for healthcare professionals.
Predictive modeling of healthcare needs to strike a balance between performance and interpretability-especially when used to guide decisions regarding resource allocation and patient risk. Here is a full machine learning workflow for predicting 30-day hospital readmission among diabetic patients based on the Diabetes 130-US Hospitals data. Following aggressive preprocessing, feature engineering, a...
There is a growing literature on the prediction of risk of deterioration in hospital settings, including by leveraging artificial intelligence (AI) models. However, this literature has focused on acute-care hospitals, rather than post-acute facilities, where the risk of deterioration remains high. Post-acute facilities tend to have lower digital maturity and poorer data foundations, as well as les...
BACKGROUND: Diabetes has reached epidemic proportions in Pakistan. This study applied machine learning (ML) techniques to identify comorbidity-based a...
Predictive modelling in healthcare has advanced rapidly, yet social care systems, despite their central role in supporting vulnerable populations, rem...
BACKGROUND: Falls are among the most common adverse events in hospitalized patients, with about 30% leading to injury. We developed a machine learning...
BACKGROUND: This multicenter study developed and validated an interpretable machine learning model integrating granular nursing and emergency departme...
BACKGROUND: With population aging and the growing burden of chronic diseases, the number of patients with cardiovascular disease (CVD) in China contin...
OBJECTIVES: To evaluate the suitability of three major open-access ICU databases (Medical Information Mart for Intensive Care IV [MIMIC-IV], eICU Coll...
INTRODUCTION: A 3D interactive report is a state-of-the-art artificial intelligence (AI) tool that integrates a patient's imaging history into an intu...
RATIONALE AND OBJECTIVES: Hospital-radiology joint ventures (JVs) are forming at an accelerating pace as health systems seek to recapture outpatient i...
Maintaining the quality and consistency of radiology reports has become increasingly challenging with the growing volume of imaging examinations. This...
OBJECTIVE: Post-infarction ventricular septal rupture (PIVSR) is a fatal mechanical complication of acute myocardial infarction. We aimed to develop a...
BACKGROUND: Rapid identification of large vessel occlusion (LVO) in acute ischemic stroke (AIS) is essential for reperfusion therapy. Screening tools,...
OBJECTIVES: Patients with substance misuse are at high risk for clinical deterioration, and pre-hospital encounters constitute important risk factors....
BACKGROUND: Gastrointestinal stromal tumors (GISTs) are tumors with malignant potential. This research aims to develop an artificial intelligence (AI)...
Klebsiella pneumoniae (KP) has emerged as a formidable nosocomial pathogen in the era of antimicrobial resistance, with mortality from pneumonia cause...
OBJECTIVES: Inappropriate and broad-spectrum antibiotic use contributes to antimicrobial resistance (AMR) and higher healthcare costs. In Saudi Arabia...
BACKGROUND: Implementing AI into real-world health care settings is known to be challenging, particularly regarding how well AI is embedded into the e...
BACKGROUND: The medical burden caused by stroke is increasingly severe, and a small minority of high-cost patients consume the majority of medical exp...
OBJECTIVES: There is limited data demonstrating the benefit of artificial intelligence technology in the diagnosis and triage of pulmonary embolism. O...