Article

Leveraging Generative AI to Accelerate Biocuration of Medical Actions for Rare Disease

2024-08-22

Abstract excerpt

<h4>Background: </h4> Structured representations of clinical data can support computational analysis of individuals and cohorts, and ontologies representing disease entities and phenotypic abnormalities are now commonly used for translational research. The Medical Action Ontology (MAxO) provides a computational representation of treatments and other actions taken for the clinical management of patients. Currently,...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 322fc5cf-d494-5dbe-bbe6-1f5db35ad20f
DOI: 10.1101/2024.08.22.24310814

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Leveraging Generative AI to Accelerate Biocuration of Medical Actions for Rare DiseaseDOI 10.1101/2024.08.22.24310814Read

The Medical Action Ontology: A Tool for Annotating and Analyzing Treatments and Clinical Management of Human DiseaseSemantic proximity Read

The Evidence Aggregator: AI reasoning applied to rare disease diagnosticsSemantic proximity Read

MSeqDR PMD-VR: An Expert-Curated Virtual Registry of 11,000 Mitochondrial Disease Cases Established Through Literature Mining and Generative AI Augmentation.Semantic proximity Read

Harnessing generative AI to annotate the severity of all phenotypic abnormalities within the Human Phenotype OntologySemantic proximity Read

Reframing AI for Rare Disease RecognitionSemantic proximity Read

Can large language models reliably extract human disease genes from full-text scientific literature?Semantic proximity Read

Consistent Performance of GPT-4o in Rare Disease Diagnosis Across Nine Languages and 4967 CasesSemantic proximity Read

Genosolver: Rare Disease Diagnosis through Holistic Integration of Unstructured Clinical Narratives Using Large Language and Reasoning ModelsSemantic proximity Read

The Evidence Aggregator: AI reasoning applied to rare disease diagnostics.Semantic proximity Read

Assessing the utility of large language models for phenotype-driven gene prioritization in the diagnosis of rare genetic disease.Semantic proximity Read

Performance Characteristics of Reasoning Large Language Models for Evidence Extraction from Clinical Genomics LiteratureSemantic proximity Read

GEN-KnowRD: Reframing AI for Rare Disease RecognitionSemantic proximity Read