Article

Leveraging EMR Data with AI to Forecast Genetic Testing Results in Neonatal Care using LUMINA

2025-09-25

Abstract excerpt

<title>Abstract</title> <p>Accurate identification of patients likely to benefit from genetic testing in the neonatal intensive care unit (NICU) remains a clinical challenge. We introduce LUMINA (Learning from Unstructured Medical Information for Neonatal Assessment), a computational framework that leverages electronic medical record notes and Human Phenotype Ontology (HPO) embeddings to predict genetic testing o...

Topics

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

Identifiers and source

Literature Corpus work: bbe356a8-2f3c-55a8-a336-8d5ac9862c29
DOI: 10.21203/rs.3.rs-7632718/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Leveraging EMR Data with AI to Forecast Genetic Testing Results in Neonatal Care using LUMINADOI 10.21203/rs.3.rs-7632718/v1Read

RarePathAI: Predictive Analytics to Identify Rare Diseases in Real-World DataSemantic proximity Read

InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records.Semantic proximity Read

Building Population Phenotypic Journeys from Laboratory Tests in Electronic Health Records for Translational ResearchSemantic proximity Read

MPSE provides fast, flexible, and efficient means to identify newborns who will benefit from whole genome sequencing within the first 48 hours of NICU admissionSemantic proximity Read

Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real World Data Towards a Personalized Medicine ApproachSemantic proximity Read

InfEHR: Resolving Clinical Uncertainty through Deep Geometric Learning on Electronic Health RecordsSemantic proximity Read

Early Detection of Rare Disease Using Hierarchical Set-to-Sequence Modeling of Structured Electronic Health RecordsSemantic proximity Read

InfEHR: Resolving Clinical Uncertainty through Deep Geometric Learning on Electronic Health RecordsSemantic proximity Read

NeoGx: Machine Learning to Predict Genetic Evaluation Need in the Level IV NICUSemantic proximity Read

Reframing AI for Rare Disease RecognitionSemantic proximity Read

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

RESCUE: An end-to-end multi-agent LLM system for proactive rare-disease patient screening in the EHRSemantic proximity Read