AIMC Topic: Decision Support Systems, Clinical

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A knowledge-based decision support system for inferring supportive treatment recommendations for diabetes mellitus.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist healthcare practitioners in their daily effort a...

Health, Digital Health and Decision Support: Sisyphus and Pandora.

Studies in health technology and informatics
The history of medicine is punctuated by conquests, discoveries and revolutions. It is also marked by questioning. It is made of doubts and certainties. In this thousand years old history, certain recent battles bear witness to these questionings, su...

A smart, practical, deep learning-based clinical decision support tool for patients in the prostate-specific antigen gray zone: model development and validation.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Despite efforts to improve screening and early detection of prostate cancer (PC), no available biomarker has shown acceptable performance in patients with prostate-specific antigen (PSA) gray zones. We aimed to develop a deep learning-base...

Overcoming Major Barriers to Build Efficient Decision Support Systems in Pharmacovigilance.

Studies in health technology and informatics
Many decision support methods and systems in pharmacovigilance are built without explicitly addressing specific challenges that jeopardize their eventual success. We describe two sets of challenges and appropriate strategies to address them. The firs...

Clinical Informatics and Quality Improvement in the Pediatric Intensive Care Unit.

Pediatric clinics of North America
Clinical informatics can support quality improvement and patient safety in the pediatric intensive care unit (PICU) in several ways including data extraction, analysis, and decision support enabled by electronic health records (EHRs), and databases a...

Using Explainable Artificial Intelligence Models (ML) to Predict Suspected Diagnoses as Clinical Decision Support.

Studies in health technology and informatics
The complexity of emergency cases and the number of emergency patients have increased dramatically. Due to a reduced or even missing specialist medical staff in the emergency departments (EDs), medical knowledge is often used without professional sup...

Connecting artificial intelligence and primary care challenges: findings from a multi stakeholder collaborative consultation.

BMJ health & care informatics
UNLABELLED: Despite widespread advancements in and envisioned uses for artificial intelligence (AI), few examples of successfully implemented AI innovations exist in primary care (PC) settings.

Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond.

Critical care clinics
Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care un...

Clinical decision support for severe trauma patients: Machine learning based definition of a bundle of care for hemorrhagic shock and traumatic brain injury.

The journal of trauma and acute care surgery
BACKGROUND: Deviation from guidelines is frequent in emergency situations, and this may lead to increased mortality. Probably because of time constraints, 55% is the greatest reported guidelines compliance rate in severe trauma patients. This study a...