AI Medical Compendium Topic

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Decision Support Systems, Clinical

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Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis.

Medical hypotheses
The present study developed a feature selection (FS)-based decision support system using the electroencephalography (EEG) signals recorded from neonates with and without seizures. The study employed 10 different FS algorithms to reduce the classifica...

PARS, a system combining semantic technologies with multiple criteria decision aiding for supporting antibiotic prescriptions.

Journal of biomedical informatics
OBJECTIVE: Motivated by the well documented worldwide spread of adverse drug events, as well as the increased danger of antibiotic resistance (caused mainly by inappropriate prescribing and overuse), we propose a novel recommendation system for antib...

The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Breast (Edinburgh, Scotland)
Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calculation, prognostication and clinical decision-support, management planning, and precision medicine. W...

Application of Artificial Intelligence to Gastroenterology and Hepatology.

Gastroenterology
Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammat...

KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services.

PloS one
BACKGROUND AND OBJECTIVE: To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in...

Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces.

International journal of medical informatics
OBJECTIVES: To determine the effect of a domain-specific ontology and machine learning-driven user interfaces on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED).

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
BACKGROUND: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers.

IEEE journal of biomedical and health informatics
In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate insulin pump delivery, such as low-glucose insulin suspension and the artificial pancreas, are star...