Resources

Retrospective medical data collection is essential for advancing patient care, offering insights and supporting the development of health technology. The Medical Information Mart for Intensive Care (MIMIC)-III database has been instrumental in provi…

MIMIC-IV-Ext-CLIF is a derived dataset of MIMIC-IV v3.1, transformed into the Common Longitudinal ICU data Format (CLIF). CLIF is an open-source critical care data model developed by a consortium of more than 10 US academic medical centers to standa…

Immunosuppression due to underlying conditions or immunosuppressive medication use increases the risk of morbidity and mortality in the context of infectious disease. Identifying patients with immunosuppression is important for better studying and u…

This resource shares processed frontal electroencephalography (EEG) data collected from adult patients undergoing non-emergency, non-cardiac surgery with general anaesthesia at four hospitals. The dataset was generated to study neurophysiological si…

The International Cardiac Arrest REsearch consortium (I-CARE) Database includes baseline clinical information and continuous electroencephalogram (EEG) and electrocardiogram (ECG) recordings from comatose patients following cardiac arrest. The patie…

This newly published dataset aims to facilitate research in atrial fibrillation (AF) detection in the intensive care unit (ICU). The ICU is a unique environment with noisy signals due to patient movement and prevalent alarms that can lead to alarm f…

MIMIC-II documents a diverse and large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a uni…

Dysglycaemia is common in hospitalised and critically ill patients, and non-invasive estimation of blood glucose from the electrocardiogram (ECG) is an active research area. However, large public resources that link ECGs to contemporaneous glucose m…

Patient’s unique information needs about their hospitalization can be addressed using clinical evidence from electronic health records (EHRs) and artificial intelligence (AI). However, robust datasets to assess the factuality and relevance of AI-gen…

False arrhythmia alarms are a persistent problem in intensive care units despite considerable effort from industrial and academic algorithm developers. Among the various arrhythmias, ventricular tachycardia (VT) is particularly challenging to detect…