Predicting interval from diagnosis to delivery in preeclampsia using electronic health records

Xiaotong Yang; Hailey K Ballard; Aditya D Mahadevan; Ke Xu; David Garmire; Elizabeth Langen; Dominick J. Lemas; Lana X. Garmire

doi:10.1038/s41467-025-58437-7

JOURNAL ARTICLE

Predicting interval from diagnosis to delivery in preeclampsia using electronic health records

Xiaotong Yang Hailey K Ballard Aditya D Mahadevan Ke Xu David Garmire Elizabeth Langen Dominick J. Lemas Lana X. Garmire

Year: 2025 Journal: Nature Communications Vol: 16 (1)Pages: 3496-3496 Publisher: Nature Portfolio

DOI: 10.1038/s41467-025-58437-7

Get Full-Text PDF Get Analytical Report

Keywords:

Preeclampsia Medicine Health records Medical record Confidence interval Pregnancy Obstetrics Pediatrics Computer science Internal medicine Health care Biology

Metrics

Cited By

15.22

FWCI (Field Weighted Citation Impact)

Refs

0.94

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Pregnancy and preeclampsia studies

Health Sciences → Medicine → Obstetrics and Gynecology

Machine Learning in Healthcare

Physical Sciences → Computer Science → Artificial Intelligence

Gestational Diabetes Research and Management

Health Sciences → Medicine → Obstetrics and Gynecology

Predicting interval from diagnosis to delivery in preeclampsia using electronic health records

Metrics

Citation History

Topics

Related Documents

Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-Based Neural Networks

Learning Diagnosis from Electronic Health Records

Predicting Prediabetes Risk From Electronic Health Records Using Machine Learning

Predicting Prediabetes Risk From Electronic Health Records Using Machine Learning

Predicting Cancer Disease from Electronic Health Records