Evidence map›Paper›PMID 41419365›Full record

ArticleThe Lancet. Digital health2025

Development and validation of a pre-trained language model for neonatal morbidities: a retrospective, multicentre, prognostic study.

Feng Xie, Philip Chung, Jonathan D Reiss, Erico Tjoa, Davide De Francesco, Thanaphong Phongpreecha, William Haberkorn, Dipro Chakraborty, Alan Lee Chang, Tomin James and 19 more

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

29 authors.

Feng XieDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA; Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
Philip ChungDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA.
Jonathan D ReissDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Erico TjoaDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Davide De FrancescoDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Thanaphong PhongpreechaDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
William HaberkornDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Dipro ChakrabortyDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Alan Lee ChangDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Tomin JamesDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Yeasul KimDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Samson MatarasoDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Camilo EspinosaDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Liu YangDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Chi-Hung ShuDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Lei XueDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Eloïse BersonDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Neshat MohammadiDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Sayane ShomeDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
S Momsen ReinckeDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Marc GhanemDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Ivana MaricDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Brice GaudilliereDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Martin S AngstDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA.
Karl SylvesterDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Surgery, Stanford University, Stanford, CA, USA.
Gary M ShawDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Lawrence S PrinceDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
David K StevensonDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Nima AghaeepourDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. Electronic address: naghaeep@stanford.edu.

Funding

Machine Learning for Integrative Modeling of the Immune System in Clinical SettingsR35GM138353 · NIGMS · STANFORD UNIVERSITY · PI AGHAEEPOUR, NIMA · 2020 to 2024
$2.2M
NIGMS NIH HHS R35 GM138353
6 · The paper itself

Abstract

backgroundEarly identification and monitoring of neonatal morbidities are critical for timely interventions that can prevent complications, optimise resource use, and support families. Although traditional tools based on tabular data and biomarkers are beneficial, they are restricted in assessing the risk of morbidities in newborns. In this study, we developed NeonatalBERT, a pre-trained large language model (LLM) that estimates the risk of neonatal morbidities from clinical notes.

methodsThis prognostic study investigated retrospective primary and external cohorts from two different quaternary-care academic medical centres in the USA: Stanford Health Care and Beth Israel Deaconess Medical Center. NeonatalBERT was initially pre-trained on clinical notes from the primary cohort and then fine-tuned separately for both cohorts. NeonatalBERT was also compared against other existing LLMs, such as BioBERT and Bio-ClinicalBERT, as well as traditional machine learning and logistic regression models using tabular features. NeonatalBERT was evaluated on 19 neonatal morbidities (respiratory distress syndrome, bronchopulmonary dysplasia, pulmonary haemorrhage, pulmonary hypertension, atelectasis, aspiration syndrome, intraventricular haemorrhage, periventricular leukomalacia, neonatal seizures, other CNS disorders, patent ductus arteriosus, cardiovascular instability, sepsis, candidiasis, anaemia, jaundice, necrotising enterocolitis, retinopathy of prematurity, and death) for the primary cohort and ten for the external cohort (respiratory distress syndrome, bronchopulmonary dysplasia, pulmonary haemorrhage, intraventricular haemorrhage, patent ductus arteriosus, sepsis, jaundice, necrotising enterocolitis, retinopathy of prematurity, and death). For each outcome, the area under the receiver operating characteristic curve, area under the precision-recall curve (AUPRC), and F1 scores were evaluated.

findings32 321 newborns were included in the primary cohort, including 27 411 in the primary training set (mean gestational age 38·64 weeks [SD 2·30]; 13 056 [47·6%] female and 14 355 [52·4%] male newborns) and 4910 in the primary testing set (mean gestational age 38·64 [2·13] weeks; 2336 [47·6%] female and 2574 [52·4%] male newborns). Additionally, 7061 newborns were selected into the external cohort, including 5653 in the external training set (1567 [27·7%] premature and 4086 [72·3%] term births; 2614 [46·2%] female and 3039 [53·8%] male newborns) and 1408 in the external testing set (383 [27·2%] premature and 1025 [72·8%] term births; 624 [44·3%] female and 784 [55·7%] male newborns). In the primary cohort, the mean AUPRC over 19 outcomes was 0·291 (95% CI 0·268-0·314) for NeonatalBERT, 0·238 (0·217-0·259) for Bio-ClinicalBERT, 0·217 (0·197-0·236) for BioBERT, and 0·194 (0·177-0·211) for the traditional model using tabular data. In the external cohort, NeonatalBERT had a mean AUPRC of 0·360 (0·328-0·393), outperforming other models with the range of 0·224-0·333.

interpretationBased on validation using two large-scale US datasets, NeonatalBERT effectively estimates the risk of neonatal morbidities from unstructured clinical notes of newborns. The promising results from this study show the potential of NeonatalBERT to enhance neonatal care and streamline hospital operations.

fundingNational Institutes of Health, Burroughs Wellcome Fund, March of Dimes Foundation, Alfred E Mann Foundation, Gates Foundation, Christopher Hess Research Fund, Roberts Foundation Research Fund, Prematurity Research Center, and Stanford Maternal & Child Health Research Institute Postdoctoral Support funds.

Indexed as

Infant, Newborn, DiseasesLarge Language ModelsFemaleHumansInfant, NewbornMaleMorbidityPrognosisRetrospective StudiesRisk Assessment

Identifiers

PMID41419365
PMCPMC12748047

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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.