ArticleBriefings in bioinformatics2024
Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence-guided nutritional therapy in the ICU.Current opinion in clinical nutrition and metabolic care · 2026Review
- AI-guided precision parenteral nutrition for neonatal intensive care units.Nature medicine · 2025Article
- Advancing neonatal health: the promise and challenges of universal genome sequencing in newborn screening.Pediatric research · 2025Article
- A machine learning approach to leveraging electronic health records for enhanced omics analysis.Nature machine intelligence · 2025Article
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Authors and funding
18 authors.
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Abstract
Translational biology posits a strong bi-directional link between clinical phenotypes and a patient's biological profile. By leveraging this bi-directional link, we can efficiently deconvolute pre-existing clinical information into biological profiles. However, traditional computational tools are limited in their ability to resolve this link because of the relatively small sizes of paired clinical-biological datasets for training and the high dimensionality/sparsity of tabular clinical data. Here, we use state-of-the-art foundation models (FMs) for electronic health record (EHR) data to generate proteomics profiles of pregnant patients, thereby deconvoluting pre-existing clinical information into biological profiles without the cost and effort of running large-scale traditional omics studies. We show that FM-derived representations of a patient's EHR data coupled with a fully connected neural network prediction head can generate 206 blood protein expression levels. Interestingly, these proteins were enriched for developmental pathways, while proteins not able to be generated from EHR data were enriched for metabolic pathways. Finally, we show a proteomic signature of gestational diabetes that includes proteins with established and novel links to gestational diabetes. These results showcase the power of FM-derived EHR representations in efficiently generating biological states of pregnant patients. This capability can revolutionize disease understanding and therapeutic development, offering a cost-effective, time-efficient, and less invasive alternative to traditional methods of generating proteomics.
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Registered trials
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