AI Medical Compendium Topic:
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Data science and machine learning in anesthesiology.

Korean journal of anesthesiology
Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods that are largely inference-based, ML is geared more towards making accurate predictions. ML is a field of artificial intelligence concerned with devel...

Predicting Optimal Hypertension Treatment Pathways Using Recurrent Neural Networks.

International journal of medical informatics
BACKGROUND: In ambulatory care settings, physicians largely rely on clinical guidelines and guideline-based clinical decision support (CDS) systems to make decisions on hypertension treatment. However, current clinical evidence, which is the knowledg...

Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN).

IEEE journal of biomedical and health informatics
The medical and machine learning communities are relying on the promise of artificial intelligence (AI) to transform medicine through enabling more accurate decisions and personalized treatment. However, progress is slow. Legal and ethical issues aro...

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial.

IEEE journal of biomedical and health informatics
Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when relying on manual review of clinical notes, particularly in critical care settings where the time win...

A Neuro-ontology for the neurological examination.

BMC medical informatics and decision making
BACKGROUND: The use of clinical data in electronic health records for machine-learning or data analytics depends on the conversion of free text into machine-readable codes. We have examined the feasibility of capturing the neurological examination as...

Using FHIR to Construct a Corpus of Clinical Questions Annotated with Logical Forms and Answers.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This paper describes a novel technique for annotating logical forms and answers for clinical questions by utilizing Fast Healthcare Interoperability Resources (FHIR). Such annotations are widely used in building the semantic parsing models (which aim...

Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment outcomes and quality of life. Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive mea...