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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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“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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BackgroundGeneral Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
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Bianchi, L., Rossi, M., & Romano, G. (2026) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/b293037132
Manohar, P., Singh, S. K., & Misra, R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-31998-2_32
Alam, M. A., Mimi, A., Islam, M. M., Sharmin, K., & Bhuiyan, M. R. I. (2026) · SAGE Open · doi:10.1177/21582440261489866
Adepu, B., & Archana, T. (2026) · International Conference Computational Vision and Bio Inspired Computing · doi:10.1109/iccvbic71195.2026.11688855
Shen, F., Liu, Z., Chao, X. U., Liu, Z., Feng, J., & He, Y. (2026) · Information Sciences · doi:10.1016/j.ins.2026.124194
Costa, G., Mendes, R., Teixeira, B., & Ribeiro, L. (2026) · Journal of Health Informatics and Digital Systems · doi:10.68159/d768883933
Schmidt, O., Weber, L., & Richter, J. (2026) · Journal of Health Informatics and Digital Systems · doi:10.68159/j531076390
Zaffarullah, Z. R. A., Gudala, V. M., Shanthi, J. M., Srivastava, S., Srivastava, S., & Kumar, S. (2026) · International Conference on Secure Information Systems and Technologies (ICSIST) · doi:10.1109/icsist70215.2026.11706080
“Brisimi et al. [15] applied FL to hospital-readmission prediction and obtained performance comparable to centralized training.”
Atmoko, R. A., Pramono, S. H., Purnomo, M. F. E., Mudjirahardjo, P., Rohmatillah, M., & Avian, C. (2026) · International Electronics Symposium (IES) · doi:10.1109/ies71033.2026.11702681
“It has been investigated for federated EHR prediction [3], smart healthcare [4], multi-institutional medicine [5], and global clinical-outcome modeling [6].”
Federated Intelligence for Precision Oncology: Safeguarding Privacy in Breast Cancer Prediction
AlRais, N. Z., Panthakkan, A., & Hamadi, H. A. (2026) · Student Multidisciplinary Innovation and Research Conference (SMIRC) · doi:10.1109/smirc70459.2026.11706679
“FL presents a viable solution, showing comparable performance to centralized models in various healthcare applications [5], [12].”
Ai-Based Federated Learning Framework for Confidential Healthcare Management
Aryavalli, D., & Panwar, R. (2026) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa69367.2026.11708572
“Brisimi et al. [16] applied Federated Learning to predictive modeling using federated EHRs, demonstrating its applicability to structured clinical data.”
Artificial intelligence in microfluidic biosensors: From signal processing to closed-loop detection
Xu, R., Le, V., Abbasi, M., Shakeel, M., Ma, S., Jiale, G., Wan, X., Ni, G., Liu, F., Luo, A., et al. (2026) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2026.119189
MMT: Achieving Exact Federated Unlearning with Improved Post-unlearning Performance
Zhang, Z., Bui, N., Verma, A., Ding, B., & Low, B. K. H. (2026) · Machine Learning · doi:10.1007/s10994-026-07117-8
“FL remains highly relevant across many privacy-sensitive domains, such as healthcare (Brisimi et al., 2018; Rieke et al., 2020 ), finance (Byrd and Polychroniadou, 2020 ; Long et al., 2020), and edge computing (Hard et al., 2018 ; Lim et al., 2020), where the need to protect sensitive data makes…”
Rahman, A., Walee, N. A., Karim, M. A., Sung, A. H., Chen, L., & Rana, M. S. (2026) · International Conference on Bioinformatics and Biomedical Technology (ICBBT) · doi:10.1145/3822521.3822536
“In the context of physiological signal analysis, FL has been shown to support personalization and cross-device learning while preserving data confidentiality [2].”
Blockchain-Based Federated Learning for Privacy-Preserving and Bias-Aware Medical AI
Patel, A., & Chaurasia, N. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-41314-7_9
Towards Federated Learning at Scale: System Design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334
“In addition, Federated Learning has already been proposed in the context of vehicle-to-vehicle communication (Sama-rakoon et al., 2018) and medical applications (Brisimi et al., 2018).”
MethodSCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2019) · arXiv · doi:10.48550/arxiv.1910.06378 · Cited by 1,734
“…(Hard et al., 2018; Yang et al., 2018), emoji prediction (Ramaswamy et al., 2019), decoder models (Chen et al., 2019b), vocabulary estimation (Chen et al., 2019a), low latency vehicle-to-vehicle communication (Samarakoon et al., 2018), and predictive models in health (Brisimi et al., 2018).”
BackgroundDifferential privacy-enabled federated learning for sensitive health data
Choudhury, O., Gkoulalas-Divanis, A., Salonidis, T., Sylla, I., Park, Y., Hsu, G., & Das, A. (2019) · arXiv · doi:10.48550/arxiv.1910.02578 · Cited by 184
“It is especially relevant in healthcare applications, where data is rife with personal, highly-sensitive information, and data analysis methods must comply with regulatory requirements [16].”
BackgroundFree-rider attacks on model aggregation in federated learning
Fraboni, Y., Vidal, R., & Lorenzi, M. (2020) · arXiv · doi:10.48550/arxiv.2006.11901 · Cited by 97
“References [1] Theodora Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Paschalidis, and Wei Shi.”
Learn electronic health records by fully decentralized federated learning
Lu, S., Zhang, Y., Wang, Y., & Mack, C. (2019) · arXiv · doi:10.48550/arxiv.1912.01792 · Cited by 23
“To overcome these challenges, federated learning may be an effective way to increase training efficiency and allow knowledge to be shared without compromising user privacy[1].”
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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