Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging ED real-world production data provides a unique opportunity to comprehend this multifaceted problem holistically. This paper introduces a novel approach to analyse healthcare production data, treating the length of stay of patients, and the follow up ...
This study addresses the challenge of leveraging free-text descriptions in Electronic Health Records (EHR) for clinical research and healthcare improvement. Despite the potential of this data, its direct interpretation by computers is limited. Semantic annotation emerges as a method to make EHR free text machine-interpretable but struggles with specific domain ontologies and faces heightened diffi...
Electronic health data concerning implantable medical devices (IMD) opens opportunities for dynamic real-world monitoring to assess associated risks r...
Forecasting the need for Renal Replacement Therapy (RRT) in intensive care units (ICUs) at an early stage can enhance patient outcomes and optimize re...
Coding according to the International Classification of Diseases (ICD)-10 and its clinical modifications (CM) is inherently complex and expensive. Nat...
This chapter explores the essential role of Binding Corporate Rules (BCRs) in managing and facilitating secure health data transfers within corporat...
Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the mo...
As the aging process accelerates, the incidence of chronic diseases in the elderly is rising. As a result, it is crucial to optimize health education ...
To construct a robot intelligent discharge follow-up platform and explore its application effects in clinical discharge follow-up scenarios Applying i...
Symptom phenotypes are one of the key types of manifestations for diagnosis and treatment of various disease conditions. However, the diversity of s...
AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals,...
The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a ser...
Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the i...
Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the interv...
Healthcare-associated infections resulting from cross-contamination, particularly from the hands of multidisciplinary staff, significantly impact pati...
Interictal epileptic discharge (IED) detection from electroencephalography (EEG) is an important but difficult step in the epilepsy diagnosis. To redu...
During a patient's hospitalization, extensive information is documented in clinical notes. The efficient summarization of this information is vital fo...
Interictal epileptiform discharges (IEDs) are electrophysiological events that intermittently occur in between seizures in Epilepsy patients. Automate...
The ongoing growth of artificial intelligence (AI) involves virtually every aspect of oncologic care in medicine. Although AI is in its infancy, it ha...