A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit
Abstract The Mendelian Phenotype Search Engine (MPSE), a clinical decision support tool using Natural Language Processing and Machine Learning, helped neonatologists expedite decisions to whole genome sequencing (WGS) to diagnose patients in the neonatal intensive care unit. After the MPSE was intro...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01458-9 |
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| author | Edwin F. Juarez Bennet Peterson Erica Sanford Kobayashi Sheldon Gilmer Laura E. Tobin Brandan Schultz Jerica Lenberg Jeanne Carroll Shiyu Bai-Tong Nathaly M. Sweeney Curtis Beebe Lawrence Stewart Lauren Olsen Julie Reinke Elizabeth A. Kiernan Rebecca Reimers Kristen Wigby Chris Tackaberry Mark Yandell Charlotte Hobbs Matthew N. Bainbridge |
| author_facet | Edwin F. Juarez Bennet Peterson Erica Sanford Kobayashi Sheldon Gilmer Laura E. Tobin Brandan Schultz Jerica Lenberg Jeanne Carroll Shiyu Bai-Tong Nathaly M. Sweeney Curtis Beebe Lawrence Stewart Lauren Olsen Julie Reinke Elizabeth A. Kiernan Rebecca Reimers Kristen Wigby Chris Tackaberry Mark Yandell Charlotte Hobbs Matthew N. Bainbridge |
| author_sort | Edwin F. Juarez |
| collection | DOAJ |
| description | Abstract The Mendelian Phenotype Search Engine (MPSE), a clinical decision support tool using Natural Language Processing and Machine Learning, helped neonatologists expedite decisions to whole genome sequencing (WGS) to diagnose patients in the neonatal intensive care unit. After the MPSE was introduced, utilization of WGS increased, time to ordering WGS decreased, and WGS diagnostic yield increased. |
| format | Article |
| id | doaj-art-b4dc20653c5944d6a9839aed1de65d34 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-b4dc20653c5944d6a9839aed1de65d342025-02-02T12:43:49ZengNature Portfolionpj Digital Medicine2398-63522025-01-01811410.1038/s41746-025-01458-9A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unitEdwin F. Juarez0Bennet Peterson1Erica Sanford Kobayashi2Sheldon Gilmer3Laura E. Tobin4Brandan Schultz5Jerica Lenberg6Jeanne Carroll7Shiyu Bai-Tong8Nathaly M. Sweeney9Curtis Beebe10Lawrence Stewart11Lauren Olsen12Julie Reinke13Elizabeth A. Kiernan14Rebecca Reimers15Kristen Wigby16Chris Tackaberry17Mark Yandell18Charlotte Hobbs19Matthew N. Bainbridge20Rady Children’s Institute for Genomic MedicineDepartment of Biomedical Informatics, University of UtahRady Children’s Institute for Genomic MedicineRady Children’s Hospital San DiegoRady Children’s Institute for Genomic MedicineRady Children’s Institute for Genomic MedicineRady Children’s Institute for Genomic MedicineRady Children’s Hospital San DiegoRady Children’s Hospital San DiegoRady Children’s Hospital San DiegoRady Children’s Hospital San DiegoRady Children’s Hospital San DiegoRady Children’s Hospital San DiegoRady Children’s Hospital San DiegoRady Children’s Hospital San DiegoRady Children’s Institute for Genomic MedicineRady Children’s Institute for Genomic MedicineClinithinkDepartment of Human Genetics, Utah Center for Genetic Discovery, University of UtahRady Children’s Institute for Genomic MedicineRady Children’s Institute for Genomic MedicineAbstract The Mendelian Phenotype Search Engine (MPSE), a clinical decision support tool using Natural Language Processing and Machine Learning, helped neonatologists expedite decisions to whole genome sequencing (WGS) to diagnose patients in the neonatal intensive care unit. After the MPSE was introduced, utilization of WGS increased, time to ordering WGS decreased, and WGS diagnostic yield increased.https://doi.org/10.1038/s41746-025-01458-9 |
| spellingShingle | Edwin F. Juarez Bennet Peterson Erica Sanford Kobayashi Sheldon Gilmer Laura E. Tobin Brandan Schultz Jerica Lenberg Jeanne Carroll Shiyu Bai-Tong Nathaly M. Sweeney Curtis Beebe Lawrence Stewart Lauren Olsen Julie Reinke Elizabeth A. Kiernan Rebecca Reimers Kristen Wigby Chris Tackaberry Mark Yandell Charlotte Hobbs Matthew N. Bainbridge A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit npj Digital Medicine |
| title | A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit |
| title_full | A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit |
| title_fullStr | A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit |
| title_full_unstemmed | A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit |
| title_short | A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit |
| title_sort | machine learning decision support tool optimizes wgs utilization in a neonatal intensive care unit |
| url | https://doi.org/10.1038/s41746-025-01458-9 |
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