Background: Artificial intelligence (AI) provides opportunities to identify the health risks of
patients and thus influence patient safety outcomes. Objective: The purpose of this …

The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …

Background: Healthcare systems represent complex organizations within which multiple
factors (physical environment, human factor, technological devices, quality of care) …

Objective This study aims to improve the classification of the fall incident severity level by
considering data imbalance issues and structured features through machine learning …

Falls are a common problem associated with significant morbidity, mortality, and economic
costs. Current fall prevention policies in local healthcare settings are often guided by …

Incident reporting systems have been widely adopted to collect information about patient
safety incidents. Much of the value of incident reports lies in the free-text section. Computer …

Background. An injurious fall is one of the main indicators of care quality in healthcare
facilities. Despite several fall screen tools being widely used to evaluate a patient's fall risk …

Background Identifying patients at high risk of falling is crucial in implementing effective fall
prevention programs. While the integration of information systems is becoming more …

Objective The study sought to evaluate the feasibility of using Unified Medical Language
System (UMLS) semantic features for automated identification of reports about patient safety …

Patient safety incident reporting is currently considered a cornerstone of efforts to improve
patient safety. For incidents related to high-maturity electronic health record systems (EHRs) …