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.
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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Cites
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Claims the paper makes
0 of 1 cited sources checked · 0 supported · 0 flagged
- Findingown data
There were significant differences across hospitals in the proportion of mortality within seven days, which ranged from 5.6% to 24.2%.
“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).”
Quote in the paperNot a substantial claim - Findingown data
There were significant differences across hospitals in gender, age, ethnicity, and race.
“There were also significant differences in gender (p=0.004), age (p<0.001), ethnicity (p<0.001), and race (p<0.001).”
Quote in the paperNot a substantial claim - Findingown data
There were significant differences across hospitals in the majority of key clinical features, such as asthma and white blood cell counts.
“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).”
Quote in the paperNot a substantial claim - Findingreasoning
MSQ was the only hospital where federated LASSO performed slightly worse than the local model, with a difference of 0.04 in AUC-ROC.
“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.”
Quote in the paperNot a substantial claim - Findingreasoning
Federated MLP outperformed pooled MLP at all sites.
“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.”
Quote in the paperSubstantial claim
- Findingown data
Training metrics were similar between federated MLP with noise and federated MLP without noise insertion.
“Training metrics were similar to federated MLP without noise insertion (Supplemental Figure 2).”
Quote in the paperSubstantial claim - Findingown data
Gaussian noise introduced into federated MLP led to decreased performance at all sites except MSQ, where both had AUC-ROCs of 0.822.
“Gaussian noise introduced into federated MLP led to decreased performance at all sites except MSQ, where both had AUC-ROCs of 0.822.”
Quote in the paperNot a substantial claim - Findingown data
Federated MLP outperformed local MLP at all hospitals.
“Federated MLP outperformed local MLP and federated LASSO at all hospitals.”
Quote in the paperSubstantial claim - Findingown data
The average difference in AUC-ROC across all hospitals between federated MLP with noise and without noise was 0.013.
“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.”
Quote in the paperSubstantial claim - Findingnone given
Local MLP outperformed local LASSO at all hospitals.
“Local MLP outperformed local LASSO at all hospitals.”
Quote in the paperNot a substantial claim - Findingown data
Federated MLP consistently outperformed all other tested models at all hospitals except MSB.
“While performance varied slightly among both classifiers in different experiments, federated MLP consistently outperformed all other tested models at all hospitals except MSB.”
Quote in the paperSubstantial claim - Findingown data
AUC-ROCs ranged from 0.796 to 0.834 in federated MLP without noise and 0.767-0.830 in federated MLP with noise.
“AUC-ROCs ranged from 0.796 to 0.834 in federated MLP without noise and 0.767–0.830 in federated MLP with noise (Supplemental Figure 1).”
Quote in the paperNot a substantial claim - Findingown data
Federated MLP surpassed federated LASSO at all sites.
“Federated MLP surpassed federated LASSO at all sites.”
Quote in the paperSubstantial claim - Findingown data
At MSW, local LASSO severely underperformed in comparison to federated LASSO, with a difference of 0.351.
“However, at MSW, local LASSO severely underperformed in comparison to federated LASSO, with a difference of 0.351.”
Quote in the paperSubstantial claim - Interpretationown data
Collectively, these results exhibit the potential of federated learning to overcome drawbacks of fragmented local models.
“Collectively, these results exhibit the potential of federated learning to overcome drawbacks of fragmented local models.”
Quote in the paperSubstantial claim - Interpretationown data
Noise may be inserted to increase data security without severely compromising federated model performance.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Adding Gaussian noise to federated model parameters increases data security.
- Interpretationown data
Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.
“Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.”
Quote in the paperSubstantial claim - Premisecitation
This is the first study to evaluate the efficacy of applying federated learning to predict mortality for COVID-19 using clinical data from EHRs.
“While it has been proposed, to our knowledge there are no published studies that implement and assess the utility of federated learning to predict COVID-19 outcomes from Electronic Health Record (EHR) data.[17]”
- Also stated in: § DISCUSSION · hedged
“To our knowledge, this is the first study to evaluate the efficacy of applying federated learning to predict mortality for COVID-19 using clinical data from EHRs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [17] Raisaro JL , Marino F , Troncoso-Pastoriza J , et al. SCOR: A secure international informatics infrastructure to investigate COVID-19. Journal of the American Medical Informatics Association 2020. source
Not checked in the free run.
- Also stated in: § DISCUSSION · hedged
- Interpretationown data
With disparate patient characteristics per hospital in terms of demographics, outcomes, sample size, and lab values, this study reflects a real-world scenario where federated learning can be leveraged for hospitals with diverse patient populations.
“With disparate patient characteristics per hospital in terms of demographics, outcomes, sample size, and lab values, this study reflects a real-world scenario where federated learning can be leveraged for hospitals with diverse patient populations.”
Quote in the paperSubstantial claim - Interpretationown data
The pattern of results emphasizes the benefit of federated learning for sites with small sample sizes and large class imbalance.
“This pattern emphasizes the benefit of federated learning for sites with small sample sizes and large class imbalance.”
Quote in the paperSubstantial claim - Interpretationown data
Federated learning models outperformed locally trained models to predict mortality.
“We demonstrated that federated learning models outperformed locally trained models to predict mortality.”
Quote in the paperSubstantial claim
- May combine two claims. 6
- Classified as probably not a substantial claim. 8
May combine two claims.
Findingown dataBoth federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals.
“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.”
Quote in the paperSubstantial claim- Also stated in: § DISCUSSION · plain
May combine two claims.
Findingown dataFederated LASSO outperformed local LASSO at all sites except MSQ, with AUC-ROC ranging from 0.693 to 0.805.
“Federated LASSO outperformed the site’s local LASSO at all sites except MSQ, with AUC-ROC ranging from 0.693 to 0.805.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataLASSO pooled outperformed federated LASSO at all five sites, with AUC-ROC ranging from 0.736 to 0.822.
“LASSO pooled outperformed federated LASSO at all five sites and had AUC-ROCs ranging from 0.736 to 0.822.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMSW had the lowest sample size of all hospitals at 485 and the lowest COVID-19 mortality prevalence of 5.6%.
“MSW had the lowest sample size of all hospitals at 485 and the lowest COVID-19 mortality prevalence of 5.6%.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenAll models were trained and evaluated using 10-fold stratified cross validation.
“All models were trained and evaluated using 10-fold stratified cross validation.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataExperiments with the addition of Gaussian noise were performed to simulate federated learning in practice.
“To simulate federated learning in practice, we also performed experiments with the addition of Gaussian noise (see Supplementary Materials).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningData collection was limited to MSHS hospitals in NYC, which may limit model generalizability to hospitals in other regions.
“First, data collection was limited to MSHS hospitals in NYC. This may limit model generalizability to hospitals in other regions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningThe study was placed within TRIPOD guidelines and code was released under GPLv3 to promote replicability.
“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).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenFederated learning is an effective method to share ML models without jeopardizing patient privacy.
“Federated learning is an effective method to share ML models without jeopardizing patient privacy.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningFederated models did not receive any raw data.
“Through this technique, federated models did not receive any raw data.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningEach MLP model was built with the same architecture for consistency and direct comparisons.
“For consistency and to enable direct comparisons, each MLP model was built with the same architecture.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningMultiple class balancing techniques were assessed due to varying prevalence across hospitals.
“Due to varying prevalence across hospitals, we assessed multiple class balancing techniques (Supplementary Table 2).”
Quote in the paperNot a substantial claim
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.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The MLP federated model performed better than the MLP local model at all five hospitals for predicting 7-day mortality in hospitalized COVID-19 patients.
“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]
The LASSO federated model outperformed the LASSO local model at three hospitals for predicting 7-day mortality in hospitalized COVID-19 patients.
“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]
The MLP federated model performed better than the MLP local model at all five hospitals for predicting 7-day mortality in hospitalized COVID-19 patients.
“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]
FL provides a privacy-preserving approach that does not require the transfer of sensitive data from the source, instead training local models with their own data and then sharing results with the global network.
“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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Li, H., Li, C., Wang, J., Yang, A., Ma, Z., Zhang, Z., & Hua, D. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.02.021 · Cited by 191 · see citing works
Comparing machine learning algorithms for predicting COVID-19 mortality
Moulaei, K., Shanbehzadeh, M., Mohammadi-Taghiabad, Z., & Kazemi-Arpanahi, H. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01742-0 · Cited by 154 · see citing works
“Vaid et al. [41] in their study analyzed data of 4029 confirmed COVID-19 patients from EHRs of five hospitals, and logistic regression with L1 regularization (LASSO) and MLP models was developed via local data and combined data.”
MethodFederated learning for healthcare applications
Chaddad, A., Wu, Y., & Desrosiers, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3325822 · Cited by 136 · see citing works
“COVID-19: COVID-19 data set is a data set for EHR-based COVID-19 patients mortality prediction [164].”
MethodA comprehensive survey on federated learning techniques for healthcare informatics
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“Choudhury et al. [29] used federated learning to predict adverse drug reactions and Vaid et al. [30] used federated learning for mortality prediction in hospitalized COVID-19 patients.”
BackgroundFederated learning: Applications, challenges and future directions
Bharati, S., Mondal, M. R. H., Podder, P., & Prasath, V. B. S. (2022) · International Journal of Hybrid Intelligent Systems · doi:10.3233/his-220006 · Cited by 117 · see citing works
“[124] CNN UCI Smartphone 5 Activity recognition [125] NN PhysioNet Dataset 16, 32, 64 Arrhythmia detection [126] LASSO, MLP, LRR Mount Sinai COVID-19 5 Mortality prediction”
MethodBadidi, E. (2023) · Future Internet · doi:10.3390/fi15110370 · Cited by 113 · see citing works
“In another work, Vaid et al. in [92] presented a significant advancement in healthcare analytics by applying federated learning to enhance mortality prediction for COVID-19 patients.”
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Chaddad, A., Lu, Q., Li, J., Katib, Y., Kateb, R., Tanougast, C., Bouridane, A., & Abdulkadir, A. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123123 · Cited by 112 · see citing works
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“This procedure is repeated for each client [68].”
Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review
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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 86 · see citing works
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 90 · see citing works
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“[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 · see citing works
Federated Learning in Health care Using Structured Medical Data
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Self-citationA federated machine learning approach for order-level risk prediction in Supply Chain Financing
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A survey on federated learning in data mining
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“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
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COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal
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Multimodal Melanoma Detection with Federated Learning
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Federated Learning in Ocular Imaging: Current Progress and Future Direction
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FedLGA: Toward System-Heterogeneity of Federated Learning via Local Gradient Approximation
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Managing Distributed Machine Learning Lifecycle for Healthcare Data in the Cloud
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“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 50 · see citing works
“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 · see citing works
Self-citationPrivacy-preserving federated machine learning on FAIR health data: A real-world application
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Federated Learning for Privacy-Preserved Medical Internet of Things
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Privacy-Preserving Federated Learning in Healthcare
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A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method
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A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications
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Threats and Defenses in the Federated Learning Life Cycle: A Comprehensive Survey and Challenges
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Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease
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Federated Learning in Smart Healthcare: A Survey of Applications, Challenges, and Future Directions
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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
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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 33 · see citing works
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 33 · see citing works
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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 35 · see citing works
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 32 · see citing works
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 · see citing works
“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 28 · see citing works
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 25 · see citing works
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 · see citing works
“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 · see citing works
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RetractedHasan, M. N., Hamdan, S., Poudel, S., Vargas, J., & Poudel, K. N. (2023) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai54212.2023.00142 · Cited by 22 · see citing works
The value of federated learning during and post-COVID-19
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“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 · see citing works
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 22 · see citing works
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 · see citing works
“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 22 · see citing works
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 21 · see citing works
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 21 · see citing works
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 · see citing works
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 · see citing works
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 19 · see citing works
Malik, H., & Anees, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18065-z · Cited by 19 · see citing works
“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 18 · see citing works
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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 18 · see citing works
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 · see citing works
Self-citationPredicting Readmission Charges Billed by Hospitals: Machine Learning Approach
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Wang, Y., Zhang, R., Yang, Q., Zhou, Q., Zhang, S., Fan, Y., Huang, L., Li, K., & Zhou, F. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103682 · Cited by 17 · see citing works
Fair Federated Learning with Opposite GAN
Han, M., Zhu, T., & Zhou, W. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111420 · Cited by 17 · see citing works
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 · see citing works
“[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 18 · see citing works
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 · see citing works
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Self-citationSong, W., Zhang, L., Liu, L., Sainlaire, M., Karvar, M., Kang, M., Pullman, A., Lipsitz, S. R., Massaro, A. F., Patil, N., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac083 · Cited by 14 · see citing works
FedRAD: Heterogeneous Federated Learning via Relational Adaptive Distillation
Tang, J., Ding, X., Hu, D., Guo, B.-J., Shen, Y., Ma, P., & Jiang, Y. (2023) · Italian National Conference on Sensors · doi:10.3390/s23146518 · Cited by 19 · see citing works
FedScore: A privacy-preserving framework for federated scoring system development
Li, S., Ning, Y., Ong, M. E. H., Chakraborty, B., Hong, C., Xie, F., Yuan, H., Liu, M., Buckland, D. M., Chen, Y., et al. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104485 · Cited by 14 · see citing works
- 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
About these checks
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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