The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index; none of the 1 sources its claims cite is checked for support yet.
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.
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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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“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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“Multiple initiatives now exist to physically or virtually aggregate EHR and related data for COVID19[11,12], and single system data have been used to support clinical investigations of a few health conditions[13].”
Self-citationFedSL: Federated split learning on distributed sequential data in recurrent neural networks
Abedi, A., & Khan, S. S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15184-5 · Cited by 101
Federated Learning for Electronic Health Records
Dang, T. K., Xiang, L., Weng, J., & Feng, M. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3514500 · Cited by 98
Patel, V. A., Bhattacharya, P., Tanwar, S., Gupta, R., Sharma, G., Bokoro, P. N., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3201876 · Cited by 88
“[50], the central server initialized the main model by using random parameters.”
MethodVarikasuvu, S. R., Varshney, S., Dutt, N., Munikumar, M., Asfahan, S., Kulkarni, P. P., & Gupta, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-01462-5 · Cited by 80
Federated Learning in Health care Using Structured Medical Data
Oh, W., & Nadkarni, G. N. (2023) · Advances in Kidney Disease and Health · doi:10.1053/j.akdh.2022.11.007 · Cited by 88
Self-citationA federated machine learning approach for order-level risk prediction in Supply Chain Financing
Kong, L., Ge, Z., & Brintrup, A. M. (2023) · International Journal of Production Economics · doi:10.1016/j.ijpe.2023.109095 · Cited by 78
A survey on federated learning in data mining
Yu, B., Mao, W., Lv, Y., Zhang, C. B. C., & Xie, Y. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1443 · Cited by 78
“Vaid et al. (2020) used federated learning to predict mortality within 7 days of hospitalized new COVID-19 patients.”
MethodOn the role of artificial intelligence in medical imaging of COVID-19
Born, J., Beymer, D., Rajan, D., Coy, A., Mukherjee, V. V., Manica, M., Prasanna, P. M., Ballah, D., Guindy, M., Shaham, D., et al. (2021) · Patterns · doi:10.1016/j.patter.2021.100269 · Cited by 73
Wang, E., Li, Y., Ming, R., Wei, J., Du, P., Zhou, P., Zong, S., & Xiao, H. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.718974 · Cited by 63
COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal
Bottino, F., Tagliente, E., Pasquini, L., Napoli, A. D., Lucignani, M., Figà-Talamanca, L., & Napolitano, A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090893 · Cited by 61
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Multimodal Melanoma Detection with Federated Learning
Agbley, B. L. Y., Li, J., Haq, A. U., Bankas, E. K., Ahmad, S., Agyemang, I. O., Kulevome, D. K. B., Ndiaye, W. D., Cobbinah, B. M., & Latipova, S. (2021) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip53232.2021.9674116 · Cited by 53
Federated Learning in Ocular Imaging: Current Progress and Future Direction
Nguyen, T. X., Ran, A. R., Hu, X., Yang, D., Jiang, M., Dou, Q., & Cheung, C. Y.-L. (2022) · Diagnostics · doi:10.3390/diagnostics12112835 · Cited by 53
FedLGA: Toward System-Heterogeneity of Federated Learning via Local Gradient Approximation
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Alamoodi, A. H., Zaidan, B. B., Albahri, O. S., Garfan, S., Ahmaro, I. Y. Y., Mohammed, R. T., Zaidan, A. A., Ismail, A. R., Albahri, A. S., Momani, F., et al. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-00972-1 · Cited by 52
Managing Distributed Machine Learning Lifecycle for Healthcare Data in the Cloud
Zeydan, E., Arslan, Ş. S., & Liyanage, M. (2024) · IEEE Access · doi:10.1109/access.2024.3443520 · Cited by 56
“FL has shown promise in developing robust predictive models for COVID-19 EHR data while maintaining patient privacy, as discussed in [137].”
MethodMadathil, N. T., Dankar, F. K., Gergely, M., Belkacem, A. N., & Alrabaee, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.009 · Cited by 58
“In contrast, other studies using FedAvg (e.g., [42,43], and [45] consistently demonstrate modest accuracy and AUC values (e.g., 84% and 0.789 AUC), especially in the presence of DP, which typically introduces a trade-off between privacy and performance.”
InfluentialRajendran, S., Xu, Z., Pan, W., Ghosh, A., & Wang, F. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000117 · Cited by 46
Self-citationPrivacy-preserving federated machine learning on FAIR health data: A real-world application
Sınacı, A. A., Gençtürk, M., Álvarez-Romero, C., Ertürkmen, G. B. L., Martínez-García, A., Escalona-Cuaresma, M. J., & Parra-Calderón, C. L. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.02.014 · Cited by 47
Federated Learning for Privacy-Preserved Medical Internet of Things
Thilakarathne, N. N., Muneeswari, G., Parthasarathy, V., Alassery, F., Hamam, H., Mahendran, R. K., & Shafiq, M. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.023763 · Cited by 48
Privacy-Preserving Federated Learning in Healthcare
Moon, S., & Lee, W. H. (2023) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic57457.2023.10049966 · Cited by 42
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A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method
Varzaneh, Z. A., Orooji, A., Erfannia, L., & Shanbehzadeh, M. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100825 · Cited by 40
A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications
Upreti, D., Yang, E., Kim, H., & Seo, C. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.048932 · Cited by 46
Threats and Defenses in the Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Li, Y., Guo, Z., Yang, N., Chen, H., Yuan, D., & Ding, W. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3563537 · Cited by 38
Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease
Ducange, P., Marcelloni, F., Renda, A., & Ruffini, F. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10332-x · Cited by 42
Federated Learning in Smart Healthcare: A Survey of Applications, Challenges, and Future Directions
Nasajpour, M., Pouriyeh, S., Parizi, R. M., Han, M., Mosaiyebzadeh, F., Liu, L., Xie, Y., & Batista, D. M. (2025) · Electronics · doi:10.3390/electronics14091750 · Cited by 48
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Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data
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Privacy-Enhancing Technologies in Biomedical Data Science
Cho, H., Froelicher, D., Dokmai, N., Nandi, A., Sadhuka, S., Hong, M. M., & Berger, B. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-120423-120107 · Cited by 39
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Kolobkov, D. S., Sharma, S. M., Медведев, А. В., Lebedev, M., Kosaretskiy, E., & Vakhitov, R. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1266031 · Cited by 40
AlSereidi, A., Qahtan, S., Mohammed, R. T., Zaidan, A. A., Albayati, H., Pamucar, D. S., Albahri, A. S., Zaidan, B. B., Shaalan, K. F., Al‐Obaidi, J. R., et al. (2022) · International Journal of Information Technology & Decision Making · doi:10.1142/s021962202250050x · Cited by 35
Majeed, A., Zhang, X., & Hwang, S. O. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040127 · Cited by 37
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Review on application progress of federated learning model and security hazard protection
Yang, A., Ma, Z., Zhang, C., Han, Y., Hu, Z., Zhang, W., Huang, X., & Wu, Y. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.11.006 · Cited by 35
Blockchain and Machine Learning in EHR Security: A Systematic Review
Zukaib, U., Cui, X., Hassan, M., Harris, S., Hadi, H. J., & Zheng, C. (2023) · IEEE Access · doi:10.1109/access.2023.3333229 · Cited by 36
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Self-citationA primer on the use of machine learning to distil knowledge from data in biological psychiatry
Quinn, T. P., Hess, J., Marshe, V. S., Barnett, M. M., Hauschild, A.-C., Maciukiewicz, M., Elsheikh, S. S. M., Men, X., Schwarz, E., Trakadis, Y. J., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-023-02334-2 · Cited by 38
Survey of Medical Applications of Federated Learning
Choi, G., Cha, W. C., Lee, S. U., & Shin, S.-Y. (2024) · Healthcare Informatics Research · doi:10.4258/hir.2024.30.1.3 · Cited by 33
Self-citationPoulain, R., Bin Tarek, M. F., & Beheshti, R. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594102 · Cited by 63
“Another work investigated MLP and LASSO logistic regression in an FL setting to predict 7-day mortality for hospitalized COVID-19 patients with EHR data [45].”
BackgroundLycopene: a therapeutic strategy against coronavirus disease 19 (COVID- 19)
Khongthaw, B., Dulta, K., Chauhan, P. K., Kumar, V., & Ighalo, J. O. (2022) · Inflammopharmacology · doi:10.1007/s10787-022-01061-4 · Cited by 29
Personalized Federated Learning for In-Hospital Mortality Prediction of Multi-Center ICU
Deng, T., Hamdan, H., Yaakob, R., & Kasmiran, K. A. (2023) · IEEE Access · doi:10.1109/access.2023.3241488 · Cited by 26
Federated learning as a smart tool for research on infectious diseases
Zwiers, L. C., Grobbee, D. E., Uijl, A., & Ong, D. S. Y. (2024) · BMC Infectious Diseases · doi:10.1186/s12879-024-10230-5 · Cited by 24
“While FL makes it possible to use data from a wide variety of sources, researchers often resort to only using a selective pool of databases in their analyses (e.g. only data from New York hospitals in the study by Vaid et al. [38]), which limits generalizability.”
Moe, S. J. S., Kim, B. W., Khan, A. N., Xu, R., Tuan, N. A., Kim, K., & Kim, D. H. (2023) · IEEE Access · doi:10.1109/access.2023.3320716 · Cited by 22
Chai, Y., Liu, H., Zhu, H., Pan, Y., Zhou, A., Liu, H., Liu, J., & Qian, Y. (2024) · Information & Management · doi:10.1016/j.im.2024.103922 · Cited by 24
Lydia, E. L., Anupama, C. S. S., Beno, A., Elhoseny, M., Alshehri, M. D., & Selim, M. M. (2021) · Soft Computing · doi:10.1007/s00500-021-06514-6 · Cited by 22
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The value of federated learning during and post-COVID-19
Qian, F., & Zhang, J. A. (2021) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzab010 · Cited by 22
“The latest paper by Vaid et al found that using electronic health records (EHRs) from five hospitals within the Mount Sinai Health System in New York to predict 7-day mortality in hospitalized patients with COVID-19, FL-based models tended to performed better than locally trained predictive models.(6) This is the firs…”
MethodFederated learning for rare disease detection: a survey
Wang, J., & Ma, F. (2023) · Rare Disease and Orphan Drugs Journal · doi:10.20517/rdodj.2023.16 · Cited by 21
Shin, J., Yoon, H. S., Lee, S., Park, S., Liu, Y., Choi, J. D., & Lee, S.-J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.734 · Cited by 33
Self-citationDesign of an artificial neural network to predict mortality among COVID-19 patients
Shanbehzadeh, M., Nopour, R., & Kazemi-Arpanahi, H. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100983 · Cited by 21
“Their results showed that the MLP-ANN classifier gained the best performance to predict COVID-19-related mortality [73].”
Decentralised Federated Learning for Hospital Networks With Application to COVID-19 Detection
Giuseppi, A., Manfredi, S., Menegatti, D., Poli, C., & Pietrabissa, A. (2022) · IEEE Access · doi:10.1109/access.2022.3202922 · Cited by 23
Federated Learning for Medical Imaging Segmentation via Dynamic Aggregation on Non-IID Data Silos
Yang, L., He, J., Fu, Y., & Luo, Z. (2023) · Electronics · doi:10.3390/electronics12071687 · Cited by 24
Federated Learning for Diabetic Retinopathy Detection in a Multi-center Fundus Screening Network
Matta, S. C., Hassine, M. B., Lecat, C., Borderie, L., Guilcher, A. L., Massin, P., Cochener, B., Lamard, M., & Quellec, G. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340772 · Cited by 23
A Federated Learning Paradigm for Heart Sound Classification
Qiu, W., Qian, K., Wang, Z., Chang, Y., Bao, Z., Hu, B., Schuller, B. W., & Yamamoto, Y. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871319 · Cited by 19
Can we share models if sharing data is not an option?
Li, Z., Mao, F., & Wu, C. (2022) · Patterns · doi:10.1016/j.patter.2022.100603 · Cited by 20
Digital public health leadership in the global fight for health security
Al-Knawy, B., Kozlakidis, Z., Tarkoma, S., Bates, D. W., Honkela, A., Crooks, G., Rhee, K. B. L., & McKillop, M. M. (2023) · BMJ Global Health · doi:10.1136/bmjgh-2022-011454 · Cited by 24
Malik, H., & Anees, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18065-z · Cited by 20
“We offer a few statistical measures of performance such as accuracy (ACCU) [75], sensitivity (SEN) [76], precision (PRE), F1-score [73], and specificity (SPF) [15], that are extensively utilized in the disciplines of medicine and epidemiology [76].”
Li, W., Tong, J., Anjum, M. M., Mohammed, N., Chen, Y., & Jiang, X. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02014-1 · Cited by 19
Arunachalam, A., Ravi, V., Acharya, V., & Pham, T. D. (2021) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2021.3094544 · Cited by 18
Optimality and Stability in Federated Learning: A Game-theoretic ApproachPreprint
Donahue, K., & Kleinberg, J. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09580 · Cited by 21
Pan, J., Fan, Z., Smith, G. E., Guo, Y., Bian, J., & Xu, J. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70128 · Cited by 19
Self-citationPredicting Readmission Charges Billed by Hospitals: Machine Learning Approach
Gopukumar, D., Ghoshal, A., & Zhao, H. (2022) · JMIR Medical Informatics · doi:10.2196/37578 · Cited by 17
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Fair Federated Learning with Opposite GAN
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Application of privacy protection technology to healthcare big data
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Self-citationBlockchain-based Trustworthy Federated Learning ArchitecturePreprint
Lo, S. K., Liu, Y., Lu, Q., Wang, C., Xu, X., Paik, H.-Y., & Zhu, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.06912 · Cited by 16
“[21] used federated learning for mortality prediction in hospitalised COVID-19 patients.”
MethodTan, M. J. T., Lichlyter, D. A., Maravilla, N. M. A. T., Schrock, W. J., Ting, F. I. L., Choa-Go, J. M., Francisco, K. K. Y., Byers, M. C., Karim, H. B. A., & AlDahoul, N. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1535018 · Cited by 19
Cross-Device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection
Xia, T., Han, J., Ghosh, A., & Mascolo, C. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10096427 · Cited by 14
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FedRAD: Heterogeneous Federated Learning via Relational Adaptive Distillation
Tang, J., Ding, X., Hu, D., Guo, B., Shen, Y., Ma, P., & Jiang, Y. (2023) · Sensors · doi:10.3390/s23146518 · Cited by 16
FedScore: A privacy-preserving framework for federated scoring system development
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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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