Dataset Information

Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning.

ABSTRACT:

Background

Rapidly 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.

Methods

Institutional 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).

Results

MPSE 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.

Conclusions

Our 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.

SUBMITTER: Peterson B

PROVIDER: S-EPMC10018992 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning.

Peterson Bennet B Hernandez Edgar Javier EJ Hobbs Charlotte C Malone Jenkins Sabrina S Moore Barry B Rosales Edwin E Zoucha Samuel S Sanford Erica E Bainbridge Matthew N MN Frise Erwin E Oriol Albert A Brunelli Luca L Kingsmore Stephen F SF Yandell Mark M

Genome medicine 20230316 1

<h4>Background</h4>Rapidly 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.<h4>Methods</h4>Institutional databases of electronic health records (EHRs) are logical starting points for identifying patients with undiagnosed Mendelian diseases. W ...[more]

PMID: 36927505

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Dataset Information

Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning.

Background

Methods

Results

Conclusions

Publications

Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning.

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