Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients.

Early detection of sepsis is crucial and can save lives. However, identifying sepsis early and accurately remains a difficult task in the medical field. This study aims to investigate a new machine-learning approach.

The Use of Machine Learning to Predict Late Arrivals at the Adult Outpatient Department.

Introduction: Patient unpunctuality leads to delays in the delivery of care and increased waiting times, resulting in crowdedness. Late arrivals for adult outpatient appointments are a challenge for healthcare, contributing to negative effects on the efficiency of health services as well as wasted time, budget, and resources. This study aims to identify factors and characteristics associated with tardy arrivals at adult outpatient appointments using machine learning and artificial intelligence.

Prediction of ICU Patients' Deterioration Using Machine Learning Techniques.

Introduction: Assessing vital sign measurements within hospital settings presents a valuable opportunity for data analysis and knowledge extraction. By generating adaptable, personalized prediction models of patient vital signs, these models can yield clinically relevant insights not achievable through population-based models. This study aims to compare several statistical forecasting models to determine their real-life applicability.

A Machine Learning Model for Predicting the Risk of Readmission in Community-Acquired Pneumonia.

Background Pneumonia is a common respiratory infection that affects all ages, with a higher rate anticipated as age increases. It is a disease that impacts patient health and the economy of the healthcare institution. Therefore, machine learning methods have been used to guide clinical judgment in disease conditions and can recognize patterns based on patient data.

Leveraging Advanced Data Analytics to Predict the Risk of All-Cause Seven-Day Emergency Readmissions.

Introduction Emergency readmissions have been a long-time, multifaceted, unsolved problem. Developing a predictive model calibrated with hospital-specific Electronic Health Record (EHR) data could give higher prediction accuracy and insights into high-risk patients for readmission. Thus, we need to proactively introduce the necessary interventions.

The Role of Artificial Intelligence and Machine Learning During the Covid-19 Pandemic: A Review.

Covid-19 is one of the most significant infectious diseases that have faced humanity in the past century from clinical, economic, and social perspectives. Although the role of infectious diseases in human history has been vicious and is well known to humanity, Covid-19 is a special case since it is the first worldwide outbreak in the era of advanced computing and telecommunications. For this reason, it was only logical to see Artificial Intelligence (AI) and Machine Learning (ML) on the top of the list of controls to compact the spread of Covid-19.