AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Programming Languages

Showing 71 to 80 of 100 articles

Clear Filters

DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis.

Bioinformatics (Oxford, England)
SUMMARY: Disease ontology (DO) annotates human genes in the context of disease. DO is important annotation in translating molecular findings from high-throughput data to clinical relevance. DOSE is an R package providing semantic similarity computati...

Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness.

A change language for ontologies and knowledge graphs.

Database : the journal of biological databases and curation
Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new ...

FHIR-Based Arden Syntax Compiler for Clinical Decision Support.

Studies in health technology and informatics
The Arden Syntax is a language designed for the encoding of medical knowledge into clinical decision support systems. Its evolution is overseen by Health Level 7. A significant enhancement in its new version 3.0 is the incorporation of FHIR for data ...

A Novel Method and Python Library for ECG Signal Quality Assessment.

Studies in health technology and informatics
Electrocardiogram (ECG) is one of the reference cardiovascular diagnostic exams. However, the ECG signal is very prone to being distorted through different sources of artifacts that can later interfere with the diagnostic. For this reason, signal qua...

Statistical and Machine Learning Methods for Discovering Prognostic Biomarkers for Survival Outcomes.

Methods in molecular biology (Clifton, N.J.)
Discovering molecular biomarkers for predicting patient survival outcomes is an essential step toward improving prognosis and therapeutic decision-making in the treatment of severe diseases such as cancer. Due to the high-dimensionality nature of omi...

dsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: In multi-cohort machine learning studies, it is critical to differentiate between effects that are reproducible across cohorts and those that are cohort-specific. Multi-task learning (MTL) is a machine learning approach that facilitates t...