Related papers: Using machine learning techniques to predict hospi…

This study investigates how machine learning (ML) models can predict hospital readmissions for diabetic patients fairly and accurately across different demographics (age, gender, race). We compared models like Deep Learning, Generalized…

Significant advancements have been made in recent years to optimize patient recruitment for clinical trials, however, improved methods for patient recruitment prediction are needed to support trial site selection and to estimate appropriate…

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine…

AI-powered stethoscopes offer a promising alternative for screening rheumatic heart disease (RHD), particularly in regions with limited diagnostic infrastructure. Early detection is vital, yet echocardiography, the gold standard tool,…

This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random…

Early recognition of clinical deterioration is one of the main steps for reducing inpatient morbidity and mortality. The challenging task of clinical deterioration identification in hospitals lies in the intense daily routines of healthcare…

Machine learning (ML) has recently shown promising results in medical predictions using electronic health records (EHRs). However, since ML models typically have a limited capability in terms of input sizes, selecting specific medical…

Coronary Artery Disease (CAD) remains a leading cause of morbidity and mortality worldwide. Early detection is critical to recover patient outcomes and decrease healthcare costs. In recent years, machine learning (ML) advancements have…

Recently, there has been a growing interest in applying machine learning methods to problems in engineering mechanics. In particular, there has been significant interest in applying deep learning techniques to predicting the mechanical…

Leukemia, the cancer of blood cells, originates in the blood-forming cells of the bone marrow. In Chronic Myeloid Leukemia (CML) conditions, the cells partially become mature that look like normal white blood cells but do not resist…

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical…

Hospital readmissions remain a challenge for healthcare systems, especially among patients with chronic conditions such as diabetes. Unplanned readmissions within 30 days are costly, strain hospital resources, and can indicate poor care…

Hospitalizations that follow closely on the heels of one or more emergency department visits are often symptoms of missed opportunities to form a proper diagnosis. These diagnostic errors imply a failure to recognize the need for…

Objective: Blood transfusions, crucial in managing anemia and coagulopathy in ICU settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have…

Persistent demographic disparities have been identified in the treatment of patients seeking care in the emergency department (ED). These may be driven in part by subconscious biases, which providers themselves may struggle to identify. To…

Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. Materials and Methods: The PubMed and MEDLINE databases were searched…

A task of vital clinical importance, within Diabetes management, is the prevention of hypo/hyperglycemic events. Increasingly adopted Continuous Glucose Monitoring (CGM) devices offer detailed, non-intrusive and real time insights into a…

Background: The rapid advancement of Machine Learning (ML) represents novel opportunities to enhance public health research, surveillance, and decision-making. However, there is a lack of comprehensive understanding of algorithmic bias,…

Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential patient outcomes. Despite active research on…

The objective of this work is to develop an Electronic Medical Record (EMR) data processing tool that confers clinical context to Machine Learning (ML) algorithms for error handling, bias mitigation and interpretability. We present…