Predictive analytics using electronic health record (EHR) data have rapidly advanced over
the last decade. While model performance metrics have improved considerably, best …

Deep learning models (aka Deep Neural Networks) have revolutionized many fields
including computer vision, natural language processing, speech recognition, and is being …

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

Background Mortality prediction of hospitalized patients is an important problem. Over the
past few decades, several severity scoring systems and machine learning mortality …

Background Unplanned readmission of a hospitalized patient is an indicator of patients'
exposure to risk and an avoidable waste of medical resources. In addition to hospital …

Mortality prediction of intensive care unit (ICU) patients facilitates hospital benchmarking
and has the opportunity to provide caregivers with useful summaries of patient health at the …

When clinicians assess the prognosis of patients in intensive care, they take imaging and
non-imaging data into account. In contrast, many traditional machine learning models rely …

Summary Background Intensive-care units (ICUs) treat the most critically ill patients, which is
complicated by the heterogeneity of the diseases that they encounter. Severity scores based …

Current mortality prediction models and scoring systems for intensive care unit patients are
generally usable only after at least 24 or 48 h of admission, as some parameters are unclear …

Patient similarity analysis is a precondition to apply machine learning technology on medical
data. In this sense, patient similarity analysis harnesses the information wealth of electronic …