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Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19
Open accessPublished version of a preprint
Abstract
Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This study used federated learning to predict 7-day mortality among 4,029 hospitalized COVID-19 patients across five Mount Sinai Health System hospitals, comparing local, pooled, and federated LASSO and MLP models. Federated models generally improved on local models, and federated MLP often performed best, while pooled LASSO remained stronger than federated LASSO.
Key findings
- In 4,029 COVID-19-positive patients, 7-day mortality prevalence differed significantly across hospitals (p<0.001), ranging from 5.6% to 24.2%.
- Federated LASSO outperformed local LASSO at all sites except MSQ, with AUC-ROC 0.693–0.805; pooled LASSO outperformed federated LASSO at all five sites, with AUC-ROC 0.736–0.822.
- Federated MLP surpassed federated LASSO at all sites and consistently outperformed all other tested models at all hospitals except MSB; local MLP outperformed local LASSO at all hospitals.
- Adding Gaussian noise to federated MLP decreased performance at all sites except MSQ; AUC-ROC ranged 0.796–0.834 without noise and 0.767–0.830 with noise.
Methods
- Data came from Epic EHRs of five MSHS hospitals in NYC: MSB (n=611), MSH (n=1644), MSM (n=749), MSQ (n=540), and MSW (n=485); primary outcome was mortality within seven days of admission.
- LASSO and MLP models were trained locally at each site, pooled across all sites, and federated via parameter sharing with a central aggregator using federated averaging; no raw data were shared.
- Models were evaluated with 10-fold stratified cross-validation, using AUC-ROC averaged across folds; Gaussian noise experiments simulated federated learning in practice.
Limitations
- Stated: data were limited to MSHS hospitals in NYC, which may limit generalizability; the work focused on principle rather than an operational deployment and did not address load balancing, convergence, or scaling; only clinical data and two classifiers were used; identical MLP architectures were used across strategies for comparison but could have been further optimized.
- Evident from the text: there was no external validation outside the same health system, and some sites had small samples and marked differences in mortality prevalence (e.g., MSQ n=540, 23.0%; MSW n=485, 5.6%), which may affect site-level comparisons.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found significant differences across hospitals in the proportion of outcome, specifically mortality within seven days (p<0.001), which ranged from 5.6% to 24.2% (Table 1).”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > RESULTS > Intercohort Comparisons
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There were also significant differences in gender (p=0.004), age (p<0.001), ethnicity (p<0.001), and race (p<0.001).”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > RESULTS > Intercohort Comparisons
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found significant differences in the majority of key clinical features such as past medical history like asthma (p<0.001) and relevant lab tests including white blood cell counts (p<0.001; Supplementary Table 1).”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > RESULTS > Intercohort Comparisons
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“MSQ was the only hospital where federated LASSO performed slightly worse than the local model with a difference of 0.04 in AUC-ROC, which may be attributed to a low sample size of 540 patients and a high mortality prevalence of 23.0% compared to other sites.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > DISCUSSION
Strength:plain· not yet calibratedn = 540Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Federated MLP also outperformed pooled MLP at all sites which might be due to the experimental condition where the same underlying architecture was used for all MLP models.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Training metrics were similar to federated MLP without noise insertion (Supplemental Figure 2).”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > DISCUSSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Gaussian noise introduced into federated MLP led to decreased performance at all sites except MSQ, where both had AUC-ROCs of 0.822.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > RESULTS > Effect of Gaussian Noise
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated MLP outperformed local MLP and federated LASSO at all hospitals.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An average difference of 0.013 in AUC-ROC across all hospitals in these two models demonstrates that noise may be inserted to increase data security without severely compromising federated model performance.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > DISCUSSION
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Local MLP outperformed local LASSO at all hospitals.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > RESULTS > Cross-model Comparisons
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
- It may state two separate claims as one. 6
- Probably not a substantial claim. 8
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals.”
- Also stated in: § DISCUSSION · plain
“The primary findings of this study demonstrate that federated MLP and LASSO models outperformed their respective local models at most sites.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § DISCUSSION · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated LASSO outperformed the site’s local LASSO at all sites except MSQ, with AUC-ROC ranging from 0.693 to 0.805.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > RESULTS > Learning Framework Comparisons
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“LASSO pooled outperformed federated LASSO at all five sites and had AUC-ROCs ranging from 0.736 to 0.822.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > RESULTS > Learning Framework Comparisons
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“MSW had the lowest sample size of all hospitals at 485 and the lowest COVID-19 mortality prevalence of 5.6%.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > DISCUSSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All models were trained and evaluated using 10-fold stratified cross validation.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > MATERIALS AND METHODS > Experimental Evaluation
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To simulate federated learning in practice, we also performed experiments with the addition of Gaussian noise (see Supplementary Materials).”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > MATERIALS AND METHODS > Study Design
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, data collection was limited to MSHS hospitals in NYC. This may limit model generalizability to hospitals in other regions.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > DISCUSSION
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To promote replicability, we placed our study within the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines (Supplementary Table 3) and released our code under the GPLv3 license (Supplementary Materials).”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > MATERIALS AND METHODS > Study Design
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated learning is an effective method to share ML models without jeopardizing patient privacy.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > CONCLUSION
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Through this technique, federated models did not receive any raw data.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > MATERIALS AND METHODS > Model Development and Selection
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For consistency and to enable direct comparisons, each MLP model was built with the same architecture.”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > MATERIALS AND METHODS > Model Development and Selection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to varying prevalence across hospitals, we assessed multiple class balancing techniques (Supplementary Table 2).”
§ Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 > MATERIALS AND METHODS > Study Design
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Adding Gaussian noise to federated model parameters increases data security.
“An average difference of 0.013 in AUC-ROC across all hospitals in these two models demonstrates that noise may be inserted to increase data security without severely compromising federated model performance.”
The paper relies on this without support.
The pooled model, trained on combined data from all sites, represents an ideal framework for comparison.
“Although the pooled model is not practically feasible, it represents an ideal framework.”
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The MLP federated model performed better than the MLP local model at all five hospitals.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [92]
In the citing paper
“The LASSO federated model outperformed the LASSO local model at three hospitals.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [92]
In the citing paper
“The MLP federated model performed better than the MLP local model at all five hospitals.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [91]
In the citing paper
“In this case, FL devised a privacy-preserving approach that does not require the transfer of sensitive data from the source, instead of training local models with their own data and then sharing results with the global network [209].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [209]
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Clinical support system for triage based on federated learning for the Korea triage and acuity scale
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Federated Learning for Privacy Preservation in Healthcare
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A hybrid federated kernel regularized least squares algorithm
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Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review
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Graph-based Fusion Modeling and Explanation for Disease Trajectory PredictionPreprint
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The promise of artificial intelligence for kidney pathophysiology
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Tariq, A., Tang, S., Sakhi, H., Celi, L. A., Newsome, J. M., Rubin, D. L., Trivedi, H., Gichoya, J. W., & Banerjee, I. (2023) · Journal of medical imaging · doi:10.1117/1.jmi.10.3.034004 · Cited by 6
“Efforts have been made to identify mortality risk factors by building simple predictive models with a small number of curated features including comorbidities such as diabetes and coronary heart disease.(1,2) Vaid et al.”
BackgroundThe Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment
Nanevski, I., Jäger, S., Schulte‐Althoff, M., Behnke, E.-M., Fürstenau, D., & Bießmann, F. (2025) · Journal of Medical Internet Research · doi:10.2196/71034 · Cited by 5
Exploring Uncertainty in Medical Federated Learning: A Survey
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Adaptable graph neural networks design to support generalizability for clinical event prediction
Tariq, A., Kaur, G., Su, L., Gichoya, J. W., Patel, B. N., & Banerjee, I. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104794 · Cited by 5
Artificial Intelligence in Medicine and Privacy Preservation
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Prediction of COVID-19 Patients’ Survival by Deep Learning Approaches
Taheriyan, M., Ayyoubzadeh, S. M., Ebrahimi, M., Kalhori, S. R. N., Abooei, A. H., Gholamzadeh, M., & Ayyoubzadeh, S. M. (2022) · Medical Journal of the Islamic Republic of Iran · doi:10.47176/mjiri.36.144 · Cited by 5
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Prediction of Lung Cancer from Electronic Health Records Using CNN Supported NLP
Jabir, K., & Raja, A. T. (2023) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-23683-9_40 · Cited by 5
Liu, L., Song, W., Patil, N., Sainlaire, M., Jasuja, R., & Dykes, P. C. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105210 · Cited by 5
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Rifat, R. H., Shruti, A. C., Kamal, M., & Alam, M. G. R. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441001 · Cited by 5
Gayathri Hegde, M., Shrishti Bekal, M., Shenoy, P. D., & Venugopal, K. R. (2023) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc57504.2023.10461823 · Cited by 5
Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks
Nopour, R., Shanbehzadeh, M., & Kazemi-Arpanahi, H. (2023) · Journal of Education and Health Promotion · doi:10.4103/jehp.jehp_20_22 · Cited by 5
Predicting COVID-19 prognosis in hospitalized patients based on early status
Natanov, D., Avihai, B., McDonnell, E. P., Lee, E., Cook, B., Altomare, N., Ko, T. M., Chaia, A. I., Munoz, C., Ouellette, S., et al. (2023) · mBio · doi:10.1128/mbio.01508-23 · Cited by 5
Kim, T. H., Yu, J. Y., Jang, W. S., Heo, S. C., Sung, M., Hong, J., Chung, K. S., & Park, Y. R. (2024) · iScience · doi:10.1016/j.isci.2024.110943 · Cited by 6
Yao, L., Chen, Q., Yang, K., Zheng, Z., Chen, Z., Wang, D., Xia, Y., Chen, D., & Chen, L. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1585732 · Cited by 5
A federated learning framework for arbitrary spatio-temporal graph neural networks
Yoon, H., Chon, K.-W., & Kim, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112801 · Cited by 5
Ullah, F., Mohammad, N., Mostarda, L., Cacciagrano, D., Ullah, S., & Zhao, Y. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101605 · Cited by 4
Al-Dailami, A., Kuang, H., & Wang, J. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3567321 · Cited by 7
Kim, J., Kim, J., Hur, K., & Choi, E. (2026) · Scientific Reports · doi:10.1038/s41598-026-38299-9 · Cited by 4
Fed-DNN-Debugger: Automatically Debugging Deep Neural Network Models in Federated Learning
Duan, S., Liu, C., Han, P., Jin, X., Zhang, X., Xiang, X., & Pan, H. (2023) · Security and Communication Networks · doi:10.1155/2023/5968168 · Cited by 6
Sheth, V., Priyal, A., Mehta, K., Desai, N., & Shah, M. (2024) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.04.003 · Cited by 5
Internet of Things and Machine Learning for Smart Healthcare
da Costa, C. A., Zeiser, F. A., Righi, R. D. R., Antunes, R. S., Alegretti, A. P., Bertoni, A. P. S., Ramos, G. D. O., de Mello, B. H., Vanin, F. N. D. S., Bertoletti, O. A., et al. (2024) · Studies in computational intelligence · doi:10.1007/978-981-97-5624-7_3 · Cited by 6
Nariman, G. S., & Hamarashid, H. K. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00756-5 · Cited by 4
Dynamic modeling of hospitalized COVID-19 patients reveals disease state–dependent risk factors
Soper, B., Cadena, J., Nguyen, S., Chan, K. H. R., Kiszka, P., Womack, L., Work, M., Duggan, J. M., Haller, S. T., Hanrahan, J., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac012 · Cited by 4
Learning Multimedia Management Strategy at Home During Learning from Home
Nugroho, B., Syakhrani, A. W., Hardiansyah, A., Pattiasina, P. J., & Pratiwi, E. Y. R. (2021) · Nidhomul Haq Jurnal Manajemen Pendidikan Islam · doi:10.31538/ndh.v6i3.1806 · Cited by 4
Graph-based Fusion Modeling and Explanation forDisease Trajectory Prediction.
Tariq, A., Tang, S., Sakhi, H., Celi, L. A., Newsome, J. M., Rubin, D. L., Trivedi, H., Gichoya, J. W., Patel, B. N., & Banerjee, I. (2022) · PubMed Central · Cited by 4
Deep Learning Methods to Predict Mortality in COVID-19 Patients: A Rapid Scoping Review
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“To overcome data size issues, Vaid et al [6] proposed a federated learning framework model trained at local sites, and then model parameters are exchanged without sharing sensitive patient data.”
BackgroundA survey: Federated learning in healthcare
Gayen, A., Das, R., Mondal, A., Singha, S., & Jana, A. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111195 · Cited by 3
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Federated Learning Performance on Early ICU Mortality Prediction with Extreme Data Distributions
Georgoutsos, A., Kerasiotis, P., & Kantere, V. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7254-8_37 · Cited by 3
A Bibliometric Analysis on Federated Learning
Algorabi, Ö., Türkan, Y. S., Ulu, M., & Namlı, E. (2024) · Journal of Advanced Research in Natural and Applied Sciences · doi:10.28979/jarnas.1555351 · Cited by 4
Specifications for the Routine Implementation of Federated Learning in Hospitals Networks
Lamer, A., Filiot, A., Bouillard, Y., Mangold, P., Andrey, P., & Schiro, J. (2021) · Studies in health technology and informatics · doi:10.3233/shti210134 · Cited by 3
“Other studies implemented Support-Vector More recently, FL has been beneficial for predicting seven-days mortality in hospitalized COVID-19 patients from five local hospitals [5].”
BackgroundFederated causal inference from observational data
Vo, T. V., Lee, Y., & Leong, T.-Y. (2025) · Machine Learning · doi:10.1007/s10994-025-06819-9 · Cited by 2
Detection of COVID-19 Disease Using Federated Learning
Dixit, S., & Gupta, C. L. P. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-53085-2_4 · Cited by 2
Medical applications of artificial intelligence
Pradhan, B. K., Biswas, D., Neelapu, B. C., Sivaraman, J., & Pal, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-19073-5.00012-4 · Cited by 2
Machine Learning-based Risk Prediction and Safety Management for Outdoor Sports Activities
Lu, Y. (2024) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v25i5.3145 · Cited by 2
Bibliometric and Content Analysis of Articles on Artificial Intelligence in Healthcare
TÜRKMEN, İ., Söyler, A., Aliyev, S., & Semiz, T. (2024) · Journal of International Health Sciences and Management · doi:10.48121/jihsam.1533583 · Cited by 2
Incentive Analysis for Agent Participation in Federated Learning
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FLPanGuard a Conceptual FL-Based Framework to Detect and Fight New Pandemics
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A systematic review of the foundations, applications, and challenges of federated learning
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Leveraging Machine Unlearning for Better Medical Care and Data Protection in Healthcare 6.0
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Secure and Efficient Logistic Regression With Secret-Sharing MPC and Differential Privacy
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Future Trends in Federated Learning: Enabling Secure and Personalized Healthcare Solutions
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Kempter, S., Brunner, J. O., Hanses, F., Spinner, C. D., Zabel, L. T., Römmele, C., Borgmann, S., Vehreschild, J. J., & Bartenschlager, C. C. (2026) · Health Care Management Science · doi:10.1007/s10729-025-09752-4 · Cited by 1
Federated Learning Applications in Healthcare Informatics: A Comprehensive Review
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Federated Computing in Orthopaedic Surgery
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Artificial Intelligence in Medicine and Privacy Preservation
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FLCMC: Federated Learning Approach for Chinese Medicinal Text Classification
Hu, G., & Fang, X. (2024) · Entropy · doi:10.3390/e26100871 · Cited by 1
“In 2020, Vaid et al. [32] combined COVID-19 datasets from five medical institutions and utilized federated learning to improve mortality predictions for hospitalized COVID-19 patients.”
BackgroundFederated Learning-based Healthcare Services for COVID-19 Pandemic
Shymala Gowri, S., Hema Priya, N., Suliman, W., Ravi, V., & Maria, A. P. (2024) · International Symposium on Advanced Electrical and Communication Technologies (ISAECT) · doi:10.1109/isaect64333.2024.10799917 · Cited by 1
Nguyen, T. T., Bekrar, A., Le, T. M., Artiba, A., Chargui, T., & Trinh, T. T. H. (2024) · International Conference of the African Federation of Operational Research Societies (AFROS) · doi:10.1109/afros62115.2024.11036880 · Cited by 1
“The healthcare sector also attracts researchers’ attention, such as [24] used FML to predict mortality and [18] reviewed potential applications of blockchain and FML for collaborative care improvement.”
Betti, M., Bertolotti, M., Bolgeo, T., Bottrighi, A., Cassinari, A., Maconi, A., Massarino, C., Pennisi, M. A., Rava, E., & Roveta, A. (2021) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins51742.2021.9524121 · Cited by 1
Metadata
- Authors
- Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., Freitas, J. K. D., Wanyan, T., Johnson, K. W., Bicak, M., Klang, E., Kwon, Y. J., Costa, A., Zhao, S., Miotto, R., Charney, A. W., Böttinger, E., Fayad, Z. A., Nadkarni, G. N., Wang, F., & Glicksberg, B. S.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Weill Cornell Medicine (US)
- Indiana University Bloomington (US)
- Hasso Plattner Institute (DE)
- Corresponding author
- Glicksberg, Benjamin S
- ORCID
- Vaid, Akhil
- Jaladanki, Suraj K
- Xu, Jie
- Teng, Shelly
- Kumar, Arvind
- Lee, Samuel
- Somani, Sulaiman
- Paranjpe, Ishan
- Freitas, Jessica K De
- Wanyan, Tingyi
- Johnson, Kipp W
- Bicak, Mesude
- Klang, Eyal
- Kwon, Young Joon
- Costa, Anthony
- Zhao, Shan
- Miotto, Riccardo
- Charney, Alexander W
- Böttinger, Erwin
- Fayad, Zahi A
- Nadkarni, Girish N
- Wang, Fei
- Glicksberg, Benjamin S
- Venue
- JMIR Medical Informatics
- Publisher
- openRxiv
- Published
- 2020-08-14
- Volume
- 9
- Issue
- 1
- Pages
- e24207-e24207
- ISSN
- 2291-9694 (ISSN-L)
- PMC
- PMC7430624
- OpenAlex
- W3118996476
- Cited by
- 253
- References
- 20
- Citations by year
- 2026: 33 · 2025: 40 · 2024: 43 · 2023: 54 · 2022: 47 · 2021: 32 · 2020: 4
- FWCI
- 15.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Informatics (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- publish.UP (University of Potsdam) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (UL1TR001433)
- National Center for Advancing Translational Sciences (U54 TR001433-05)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Keywords
- federated learning, COVID-19 pandemic, mortality prediction, hospitalized patients, electronic health records, data privacy, model generalization, LASSO, multilayer perceptron
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