ArticleGenome medicine2023
Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning.
Article in Genome medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 28 citations in OpenAlex.
- Generative AI at the Bedside: An Integrative Review of Applications and Implications in Clinical Nursing Practice.Journal of clinical nursing · 2026Review
- Rapid whole-genome sequencing in the pediatric intensive care unit: from etiologic diagnosis to precision management-a narrative review.Translational pediatrics · 2026Review
- Natural language processing-based model to predict radiation pneumonitis in patients with locally advanced non-small cell lung cancer undergoing chemoradiotherapy: a retrospective cohort study.EClinicalMedicine · 2026Article
- Prediction of human pathogenic start loss variants based on self-supervised contrastive learning.BMC biology · 2025Article
- MPSE identifies newborns for whole genome sequencing within 48 h of NICU admission.NPJ genomic medicine · 2025Article
- The Utah NeoSeq Project: a collaborative multidisciplinary program to facilitate genomic diagnostics in the neonatal intensive care unit.NPJ genomic medicine · 2025Article
- Breaking barriers: fostering equitable access to pediatric genomics through innovative care models and technologies.Pediatric research · 2025Review
- A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit.NPJ digital medicine · 2025Article
- Childhood outcomes of fetal genomic copy-number variants: The prenatal microarray cohort study.Genetics in medicine open · 2025Article
- Rapid whole genome sequencing in newborn screening for metabolic diseases.Frontiers in pediatrics · 2025Review
- Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study.BMC medical informatics and decision making · 2024Article
- Diagnostic Utility of Whole Genome Sequencing After Negative Karyotyping/Chromosomal Microarray in Infants Born With Multiple Congenital Anomalies.Journal of Korean medical science · 2024Article
- Sodium Deoxycholate-Propidium Monoazide Droplet Digital PCR for Rapid and Quantitative Detection of ViableMicroorganisms · 2024Article
- A Machine Learning Decision Support Tool Optimizes Whole Genome Sequencing Utilization in a Neonatal Intensive Care Unit.medRxiv : the preprint server for health sciences · 2024Article
- NeoGx: Machine-Recommended Rapid Genome Sequencing for Neonates.medRxiv : the preprint server for health sciences · 2024Article
- PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.Genome medicine · 2024Article
- Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review.NPJ genomic medicine · 2024Review
- A Panel-Agnostic Strategy 'HiPPo' Improves Diagnostic Efficiency in the UK Genomic Medicine Service.Healthcare (Basel, Switzerland) · 2023Article
- A Review of Disparities and Unmet Newborn Screening Needs over 33 Years in a Cohort of Mexican Patients with Inborn Errors of Intermediary Metabolism.International journal of neonatal screening · 2023Article
- Open-Source Artificial Intelligence System Supports Diagnosis of Mendelian Diseases in Acutely Ill Infants.Children (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundRapidly and efficiently identifying critically ill infants for whole genome sequencing (WGS) is a costly and challenging task currently performed by scarce, highly trained experts and is a major bottleneck for application of WGS in the NICU. There is a dire need for automated means to prioritize patients for WGS.
methodsInstitutional databases of electronic health records (EHRs) are logical starting points for identifying patients with undiagnosed Mendelian diseases. We have developed automated means to prioritize patients for rapid and whole genome sequencing (rWGS and WGS) directly from clinical notes. Our approach combines a clinical natural language processing (CNLP) workflow with a machine learning-based prioritization tool named Mendelian Phenotype Search Engine (MPSE).
resultsMPSE accurately and robustly identified NICU patients selected for WGS by clinical experts from Rady Children's Hospital in San Diego (AUC 0.86) and the University of Utah (AUC 0.85). In addition to effectively identifying patients for WGS, MPSE scores also strongly prioritize diagnostic cases over non-diagnostic cases, with projected diagnostic yields exceeding 50% throughout the first and second quartiles of score-ranked patients.
conclusionsOur results indicate that an automated pipeline for selecting acutely ill infants in neonatal intensive care units (NICU) for WGS can meet or exceed diagnostic yields obtained through current selection procedures, which require time-consuming manual review of clinical notes and histories by specialized personnel.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.