An emphasis on overly broad notions of generalisability as it pertains to applications of
machine learning in health care can overlook situations in which machine learning might …

Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links
embedded systems (hardware and software) and machine learning, with the purpose of …

Acute critical illness is often preceded by deterioration of routinely measured clinical
parameters, eg, blood pressure and heart rate. Early clinical prediction is typically based on …

Background Continuous monitoring of vital signs by using wearable wireless devices may
allow for timely detection of clinical deterioration in patients in general wards in comparison …

Machine learning is increasingly integrated into clinical practice, with applications ranging
from pre-clinical data processing, bedside diagnosis assistance, patient stratification …

Background Continuous vital signs monitoring on general hospital wards may allow earlier
detection of patient deterioration and improve patient outcomes. This systematic review will …

Aims To understand current experiences of vital signs monitoring of patients and clinical staff
on a surgical ward, and views on the introduction of wearable ambulatory monitoring into the …

Background Alongside the promise of improving clinical work, advances in healthcare
artificial intelligence (AI) raise concerns about the risk of deskilling clinicians. This purpose …

Background Vital signs monitoring is a universal tool for the detection of postoperative
complications; however, unwell patients can be missed between traditional observation …

OBJECTIVES: Results of pre-post intervention studies of sepsis early warning systems have
been mixed, and randomized clinical trials showing efficacy in the emergency department …