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Federated learning of predictive models from federated Electronic Health Records
Open accessAbstract
Background In an era of "big data," computationally efficient and privacy-aware solutions for large-scale machine learning problems become crucial, especially in the healthcare domain, where large amounts of data are stored in different locations and owned by different entities. Past research has been focused on centralized algorithms, which assume the existence of a central data repository (database) which stores and can process the data from all participants. Such an architecture, however, can be impractical when data are not centrally located, it does not scale well to very large datasets, and introduces single-point of failure risks which could compromise the integrity and privacy of the data. Given scores of data widely spread across hospitals/individuals, a decentralized computationally scalable methodology is very much in need.Objective We aim at solving a binary supervised classification problem to predict hospitalizations for cardiac events using a distributed algorithm. We seek to develop a general decentralized optimization framework enabling multiple data holders to collaborate and converge to a common predictive model, without explicitly exchanging raw data.Methods We focus on the soft-margin l 1-regularized sparse Support Vector Machine (sSVM) classifier. We develop an iterative cluster Primal Dual Splitting (cPDS) algorithm for solving the large-scale sSVM problem in a decentralized fashion. Such a distributed learning scheme is relevant for multi-institutional collaborations or peer-to-peer applications, allowing the data holders to collaborate, while keeping every participant's data private.Results We test cPDS on the problem of predicting hospitalizations due to heart diseases within a calendar year based on information in the patients Electronic Health Records prior to that year. cPDS converges faster than centralized methods at the cost of some communication between agents. It also converges faster and with less communication overhead compared to an alternative distributed algorithm. In both cases, it achieves similar prediction accuracy measured by the Area Under the Receiver Operating Characteristic Curve (AUC) of the classifier. We extract important features discovered by the algorithm that are predictive of future hospitalizations, thus providing a way to interpret the classification results and inform prevention efforts.
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“A similar scheme is used by Xie et al. [485] to design a variant of local SGD in which clients locally perform Adagrad [335, 161].”
From Advances and Open Problems in Federated Learning (2021) · cites this paper as [485]
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In the citing paper
“Federated learning has also enabled predictive modeling based on diverse sources, which can provide clinicians with additional insights into the risks and benefits of treating patients earlier [9, 10, 90].”
From Federated Learning for Healthcare Informatics (2021) · cites this paper as [10]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“In the context of electronic health records (EHR), for example, FL helps to represent and to find clinically similar patients13,47, as well as predicting hospitalisations due to cardiac events14, mortality and ICU stay time19.”
From The future of digital health with federated learning (2020) · cites this paper as [14]
In the citing paper
“Governance of data for FL is maintained locally, alleviating privacy concerns, with only model weights or gradients communicated between the client-sites and the federated server38,39.”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [39]
In the citing paper
“For example, FL can be used to improve the perceiving and learning ability of AIoT devices such as connected vehicles for autonomous driving [199] and wearable devices for health monitoring [222], [197].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [222]
In the citing paper
“The FL algorithm is used to describe and identify physician-related persons and to estimate their hospitalization, deaths, and survival rates based on electronic health records (EHR) [54].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [54]
In the citing paper
“Deep Leakage from Gradients (DLG) [152] obtained the private training data at the inference phase.”
From Challenges, Applications and Design Aspects of Federated Learning: A Survey (2021) · cites this paper as [152]
In the citing paper
“The theoretical and experimental comparisons show that the proposed model converges faster and with less communication overhead compared to an alternative distributed algorithm.”
From Handling privacy-sensitive medical data with federated learning: Challenges and future directions (2023) · cites this paper as [35]
In the citing paper
“Furthermore, an FL-based approach is proposed in [81] to predict hospitalizations for patients diagnosed with heart diseases using their historic EHRs.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [81]
In the citing paper
“In addition, Brisimi et al., (2018) also use EMRs to evaluate whether a patient with heart disease will be hospitalized based on a FL algorithm called cluster Primal Dual Splitting (cPDS).”
From A review of applications in federated learning (2020) · cites this paper as Brisimi et al., (2018)
In the citing paper
“Brisimi et al. 2018 Predict future hospitalizations for patients Cluster Primal Dual Splitting algorithm Yield classifiers using relatively few features Need more iterations for convergence”
From A review of applications in federated learning (2020) · cites this paper as Brisimi et al. 2018
In the citing paper
“Brisimi et al. 2018 Predict future hospitalizations for patients Cluster Primal Dual Splitting algorithm Yield classifiers using relatively few features Need more iterations for convergence”
From A review of applications in federated learning (2020) · cites this paper as Brisimi et al. 2018
In the citing paper
“Brisimi et al. 2018 Predict future hospitalizations for patients Cluster Primal Dual Splitting algorithm Yield classifiers using relatively few features Need more iterations for convergence”
From A review of applications in federated learning (2020) · cites this paper as Brisimi et al. 2018
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“To ground our work in practical context, we refer to the case study conducted by Brisimi et al. in [6], where FL was applied to predict hospitalizations using distributed electronic health records from multiple healthcare institutions.”
Privacy Protection for Medical Information in Federated Learning
Vaishnavi, M., Vemuru, S., & Khan, A. (2025) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-80949-1_6
Artificial Intelligence-Enabled Federated Learning Techniques in Healthcare Sector
Sharma, S., & Gour, R. (2025) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-80949-1_4
Designing Resilient IoT and Edge Computing with Federated TinyML (DRIFT)Preprint
Laiu, P. M., Li, M., Nichols, J. A., Huettel, M., Sikkema, I., & Mathur, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5430788
Federated Learning in the Healthcare Industry
Mondal, R. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-99270-4_2
Revolutionizing Industry 4.0: Multi-level Federated Learning for a Dynamic Ecosystem
Sharma, R., Singh, B., Gulhane, M., Chauhan, P., & Rakesh, N. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-99270-4_8
Enhancing Breast Cancer Detection Through Explainable Federated Learning Models
Pasha, A., Rahman, S. Z., Ahamed, S. S., Kumar, D. N. P., & Manjunatha, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-00715-5_28
Secure federated learning with configurable reliability in heterogeneous edge computing
Liu, Y., Liang, Y., Zhang, R., & Zhou, Z. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111808
Enhancing Security and Insight in Federated Learning for AI-Driven Healthcare
Manju, M. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3306-9.ch007
Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learning
Jiang, X., Gao, Y., Xu, H., Zhang, Q., Yong, L., & Zhou, P. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11210094
“As such, FL effectively safeguards data privacy and provides unique advantages in various domains including healthcare [2], autonomous driving [3], and mobile keyboard prediction [4].”
The Federated Sample Weight Model: A New Strategy for Precision Medicine
Li, J., Peng, Z., Xu, L., Li, Y., Zheng, S., Kong, L., Quan, Z., Xu, X., & Geng, Z. (2025) · International Conference on Health Informatization and Data Analytics · doi:10.1145/3759972.3760151
“Brisimi et al. [11] utilized a federated learning approach to predict if cardiovascular disease patients will be hospitalized while ensuring the privacy of data.”
Mim, S. S., Logofătu, D., Guerrero-Contreras, G., & Medina‐Bulo, I. (2025) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista68122.2025.11249509
“Similarly, Brisimi et al. [9] employed FL for heart disease prediction from electronic health records (EHRs), highlighting the potential of distributed learning frameworks across hospitals.”
An intelligent framework for dynamic modeling of therapeutic response using clinical compliance data
Wang, X., & Xie, H. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1631599
Multimorbidity measurement strategies for predicting hospital visits
Neves, B. D., Moreira, J. M., Gonçalves, S., Cerejo, J., Mota, I., Silva, N. A., Leite, F., & Silva, M. J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13817-z
Survey on Federated Learning in Smart Healthcare
Hammamouche, A., Zamouche, D., Khoukhi, L., & Hamamouche, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00552-6_2
Towards Privacy-Preserving Approach for Diabetes Analytics Using Federated Learning
Keole, P. P., Bhole, R. H., Rathod, R. R., & Dhotre, P. S. (2025) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon66527.2025.11281090
Heartify: A Federated Learning-Based Application for Early Heart Disease Detection
Sinciya, P. O., George, S. S., Sajan, S., & Gayathri Devi, S. (2025) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca66273.2025.00124
Federated Learning in Healthcare Technology: Challenges, Solutions and Opportunities
Abha, E. E. M. C., Sajin, M. I. A., Suva, M. W., Akter, S., Abir, M. R., Reaj, M. A. H., & Rafiq, J. I. (2025) · Studies in computational intelligence · doi:10.1007/978-981-96-8353-6_1
Shala, E., Brunner, J. O., Vehreschild, J. J., Heller, A. R., & Bartenschlager, C. C. (2026) · Flexible Services and Manufacturing Journal · doi:10.1007/s10696-025-09646-1
Meta-FL EHR- Intelligent and Federated System for Transmission of Electronic Health Records
Lakshmaiya, N., Kota, S. R., Dhanasekar, P., Kaliappan, S., & Rajendran, A. (2025) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig68706.2025.11323903
“…patient record management system, referred to as the federated Electronic Health Records, trains local models and global models to make predictions using the adopted machine learning model and information shared collaboratively, thereby addressing data privacy and institutional data silos [14].”
Federated Stacked Selective Distillation for Privacy-Preserving Heart Disease Prediction
Hasan, M. R., & Li, J. (2025) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom60686.2025.11343598
“Since then, FL has gained traction in healthcare for tasks such as medical image analysis [4], electronic health record (EHR) modeling [5], and disease risk prediction [6].”
Improved chi-square feature selection for robust heart disease data classification
Nayl, H., Aly, E. S., Rezk, A., & Fares, M. E. (2026) · AIMS Mathematics · doi:10.3934/math.2026108
Re-examine Federated Rank Learning: Analyzing Its Robustness Against Poisoning Attacks
Huang, X., Zhu, X., Chen, C. L. P., & Verı́ssimo, P. (2025) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1109/raid67961.2025.00019
“FL has gained traction in various applications, such as industry [2], healthcare [3], [4], [5], COVID-19 detection [6], [7], drone technology [8], [9], and IoT intrusion detection [10], where privacy concerns are critical.”
Pais, V., Rao, S., & Muniyal, B. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae416c
Metadata
- Authors
- Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W.
- Affiliations
- Boston University (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Paschalidis, Ioannis Ch.
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 112
- Pages
- 59-67
- ISSN
- 1386-5056 (ISSN-L)
- PMC
- PMC5836813
- OpenAlex
- W2783522756
- Cited by
- 997
- References
- 24
- Citations by year
- 2026: 118 · 2025: 159 · 2024: 146 · 2023: 167 · 2022: 156 · 2021: 151 · 2020: 74 · 2019: 20 · 2018: 5
- FWCI
- 35.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (IIS-1724990, CNS-1645681, CCF-1527292, IIS-1237022)
- Boston University
- Army Research Office (W911NF-12-1-0390)
- National Institutes of Health (1UL1TR001430)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- federated learning, electronic health records, distributed optimization, sparse support vector machines, primal-dual splitting, binary classification, data privacy, multi-institutional collaboration, feature extraction, heart disease prevention
- MeSH terms
- Machine Learning, Algorithms, Hospitalization, Humans, ROC Curve, Databases, Factual, Electronic Health Records, Support Vector Machine
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