Latest AI and machine learning research in information technology for healthcare professionals.
AIMS: Electronic health records (EHR) can be used to target atrial fibrillation (AF) screening. We evaluated the performance of risk prediction models scalable across nationwide EHRs. METHODS: Retrospective cohort study individuals aged ≥30 years without diagnosed AF in the Clalit Health Services (Israel) EHR dataset between January 1 2019 and June 30, 2019. The primary outcome was a diagnosis of ...
Botnet detection remains a perennial and critical challenge in cybersecurity. As long as the internet exists, threat actors will devise new ways to create and disguise these malicious networks, making the development of robust detection methods a task that will never be obsolete. Traditional approaches, relying on rigid signatures and manual feature engineering, are often locked in a reactive cycl...
Deep learning-based models have been widely used to predict electronic health record (EHR) events by exploiting diagnostic characteristics. Despite si...
OBJECTIVES: To develop recommendations to inform development and integration of predictive digital health and artificial intelligence tools in primary...
BACKGROUND: Stroke is a disease with extremely high mortality and disability rates worldwide. Hemorrhagic stroke and ischemic stroke require completel...
The introduction of foundational models, specifically large language models, has promised a health care transformation. However, the field is rapidly ...
The transition to a circular economy (CE) is a critical strategy for improving the sustainability and resilience of the global food system. Industry 4...
Applications of data science and artificial intelligence (AI) in global health are expanding, yet research remains fragmented and often misaligned wit...
BACKGROUND: Chronic diseases pose a heavy global burden, with challenges in utilizing unstructured data for continuous care. Natural language intellig...
OBJECTIVES: Unwarranted clinical variation (UCV), defined as care provided to a patient that is not proportional to the patient's needs, clinical char...
The IoT has posed novel cyber-physical vulnerabilities due to the fast proliferation of Internet of Things (IoT) systems. Old network-based intrusion ...
Interpretable, automated Artificial Intelligence (AI) solutions are essential for accurate 12-lead electrocardiogram (ECG) arrhythmia classification b...
BACKGROUND: Deep learning has shown promise in diabetes management but faces challenges in real-world application due to its "black-box" nature, chara...
BACKGROUND AND PURPOSE: Cardiovascular disease (CVD) is the leading cause of death globally [1] as well as the leading cause of death among cancer sur...
A growing number of data-driven clinical decision support (CDS) tools are incorporated into tele-critical care, but the clinician perceptions of their...
Digital twins-virtual representations dynamically linked to physical systems-have the potential to transform biomedical engineering by enabling real-t...
BACKGROUND: The modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary outcome in clinical trials and observat...
Patient's unique information needs about their hospitalization can be addressed using clinical evidence from electronic health records (EHRs) and arti...
Objective: AI is rapidly transforming healthcare, yet its integration into clinical neuropsychology remains limited and uneven. This paper explores th...
OBJECTIVE: Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record...