ArticleiScience2022
MedML: Fusing medical knowledge and machine learning models for early pediatric COVID-19 hospitalization and severity prediction.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 21 citations in OpenAlex.
- Privacy-Preserving Federated Learning Framework for Multi-Source Electronic Health Records Prognosis Prediction.Sensors (Basel, Switzerland) · 2025Article
- Comparative performance of twelve machine learning models in predicting COVID-19 mortality risk in children: a population-based retrospective cohort study in Brazil.PeerJ. Computer science · 2025Article
- OligoM-Cancer: A multidimensional information platform for deep phenotyping of heterogenous oligometastatic cancer.Computational and structural biotechnology journal · 2024Article
- A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care.Patterns (New York, N.Y.) · 2024Article
- Multi-faceted analysis and prediction for the outbreak of pediatric respiratory syncytial virus.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics.Nature communications · 2023Article
- FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network.Scientific data · 2023Article
- Biases associated with database structure for COVID-19 detection in X-ray images.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
The COVID-19 pandemic has caused devastating economic and social disruption. This has led to a nationwide call for models to predict hospitalization and severe illness in patients with COVID-19 to inform the distribution of limited healthcare resources. To address this challenge, we propose a machine learning model, MedML, to conduct the hospitalization and severity prediction for the pediatric population using electronic health records. MedML extracts the most predictive features based on medical knowledge and propensity scores from over 6 million medical concepts and incorporates the inter-feature relationships in medical knowledge graphs via graph neural networks. We evaluate MedML on the National Cohort Collaborative (N3C) dataset. MedML achieves up to a 7% higher AUROC and 14% higher AUPRC compared to the best baseline machine learning models. MedML is a new machine learnig framework to incorporate clinical domain knowledge and is more predictive and explainable than current data-driven methods.
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Registered trials
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.