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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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BackgroundA federated learning framework for pneumonia image detection using distributed data
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MethodResource management at the network edge for federated learning
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Federated Learning for Arrhythmia Detection of Non-IID ECG
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“The work in [8] developed a distributed and iterative cluster primal dual splitting (cPDS) algorithm to predict hospitalizations for cardiac events over electronic health records (EHR).”
BackgroundFederated 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
An Intelligent Diagnosis Method for Machine Fault Based on Federated Learning
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“Although this technique is at the early research stage, federated learning appears to be fit-for-purpose for privacy-preserving medical applications (131, 132), and for highgrade glioma monitoring biomarkers in particular.”
MethodMulti-party Diabetes Mellitus risk prediction based on secure federated learning
Su, Y., Huang, C., Zhu, W., Lyu, X., & Ji, F. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104881 · Cited by 35
Privacy-Enhancing Technologies in Biomedical Data Science
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Federated learning-based disease prediction: A fusion approach with feature selection and extraction
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Federated Learning on Distributed Medical Records for Detection of Lung Nodules
Baheti, P., Sikka, M., Arya, K. V., & Rajesh, R. (2020) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0009144704450451 · Cited by 35
“Federated learning(Brisimi et al., 2018) is a machine learning technique involving training of algo-rithm across multiple decentralized servers holding local data samples, without exchanging their data.”
BackgroundApplications of machine learning methods in kidney disease
Chan, L., Vaid, A., & Nadkarni, G. N. (2020) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000604 · Cited by 35
The Need for Advanced Intelligence in NFV Management and Orchestration
Manias, D. M., & Shami, A. A. (2020) · IEEE Network · doi:10.1109/mnet.011.2000373 · Cited by 35
“The ability to handle multiple node’s data to develop a collective insight has enabled the use of FL in situations which traditionally have been hesitant to adopt ML strategies due to privacy concerns; one typical example of this is healthcare due to the sensitivity of the patient’s data [14].”
Survey on Federated Learning enabling indoor navigation for industry 4.0 in B5G
Alsamhi, S. H., Shvetsov, A. V., Hawbani, A., Shvetsov, A. V., Kumar, S., & Zhao, L. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.06.001 · Cited by 35
Federated learning–based global road damage detection
Saha, P. K., Arya, D., & Sekimoto, Y. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13186 · Cited by 37
“Brisimi et al. (2018) have used FL to solve a binary supervised classification problem to predict hospitalizations for cardiac events using electronic health records.”
BackgroundMethodYour Labels are Selling You Out: Relation Leaks in Vertical Federated Learning
Qiu, P., Zhang, X., Ji, S., Du, T., Pu, Y., Zhou, J. W., & Wang, T. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3208630 · Cited by 35
“This property of VFL makes it popular in industry [7], [10], [26], [51], [52],”
BackgroundArtificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions
Nitiéma, P. (2022) · Journal of Medical Internet Research · doi:10.2196/41138 · Cited by 40
Yang, Y., Hu, X., Gao, Z.-P., Chen, J.-F., Ni, C., Xia, X., & Lo, D. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3347898 · Cited by 35
“Extensive prior research [21], [59] has been proposed to study the application on federated [60], of things [64], [65].”
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers
Lu, J., Zhang, X. S., Zhao, T., He, X., & Cheng, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00981 · Cited by 38
“Due to the developments in areas of privacy, large-scale training, and distributed optimization, federated learning methods have been deployed by applications which require computing at the edge [2,9, 13, 14, 28].”
MethodSubramanian, M., Rajasekar, V., Easwaramoorthy, S. V., Shanmugavadivel, K., & Nandhini, P. S. (2022) · Electronics · doi:10.3390/electronics11244117 · Cited by 34
Confederated Learning: Federated Learning With Decentralized Edge Servers
Wang, B., Fang, J., Li, H., Yuan, X., & Ling, Q. H. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3241768 · Cited by 38
“A line of research to address the scalability issue is decentralized FL [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], which has attracted much interest due to their enhanced scalability as well as its strengthened robustness to server failures.”
BackgroundChen, X., Wang, H., Lu, S., & Yan, R. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3264027 · Cited by 33
“Until now, it has been widely utilized in practical applications, such as intelligent software [18], healthcare [19], and traffic monitoring [20].”
MethodThakur, A., Sharma, P., & Clifton, D. A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3134835 · Cited by 35
“To address the aforementioned aspects of healthcare applications, we propose a federated learning [2], [12] framework that is semi-supervised in nature and can be trained for multi-tasking.”
BackgroundA Federated Learning Approach to Pneumonia Detection
Khan, S. H., & Alam, M. G. R. (2021) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet53442.2021.9659591 · Cited by 33
Federated Learning on Internet of Things: Extensive and Systematic Review
Aggarwal, M., Khullar, V., Rani, S., Prola, T. A., Bhattacharjee, S. B., Shawon, S. M., & Goyal, N. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.049846 · Cited by 38
FedLSTM: A Federated Learning Framework for Sensor Fault Detection in Wireless Sensor Networks
Khan, R., Saeed, U., & Koo, I. (2024) · Electronics · doi:10.3390/electronics13244907 · Cited by 32
“This method has been widely used in practice, including in applications such as traffic monitoring [32], healthcare [33], and smart applications [34], demonstrating the feasibility and efficiency of integrating FedAvg with deep neural networks.”
Addressing contemporary threats in anonymised healthcare data using privacy engineering
Narayan, S. M., Kohli, N., & Martin, M. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01520-6 · Cited by 38
“FL has been used to train AI models to identify diabetic retinopathy using optical coherence tomography scans from different scanners 90 and to predict cardiovascular hospitalizations using electronic health records from multiple institutions 91 .”
An Adaptive Model Averaging Procedure for Federated Learning (AdaFed)
Giuseppi, A., Torre, L. D., Menegatti, D., Priscoli, F. D., Pietrabissa, A., & Poli, C. (2022) · Journal of Advances in Information Technology · doi:10.12720/jait.13.6.539-548 · Cited by 33
On the Outsized Importance of Learning Rates in Local Update Methods
Charles, Z., & Konečný, J. (2020) · arXiv · doi:10.48550/arxiv.2007.00878 · Cited by 32
“Federated learning has enabled privacy-aware learning in a variety of applications (Hard et al., 2018; Chen et al., 2019; Brisimi et al., 2018; Samarakoon et al., 2018; Hard et al., 2020), and has seen a large volume of work on the intersection of federated learning with topics including…”
BackgroundDFML: Dynamic Federated Meta-Learning for Rare Disease Prediction
Chen, B., Chen, T., Zeng, X., Zhang, W., Lu, Q., Hou, Z., Zhou, J., & Helal, S. (2024) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3239848 · Cited by 31
A federated learning approach for smart healthcare systems
Mishra, A., Saha, S., Mishra, S. K., & Bagade, P. (2023) · CSI Transactions on ICT · doi:10.1007/s40012-023-00382-1 · Cited by 32
“Brisimi et al. [15]. suggest a binary supervised learning system to predict hospitalizations of cardiac events through distributed algorithms.”
BackgroundApproaches to address the data skew problem in federated learning
Verma, D. C., White, G., Julier, S., Pasteris, S., Chakraborty, S., & Cirincione, G. (2019) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.2519621 · Cited by 31
Dai, W., Jiang, X., Bonomi, L., Li, Y., Xiong, H., & Lucila Ohno-machado, A. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2989301 · Cited by 31
Olshevsky, A., Paschalidis, I. C., & Spiridonoff, A. (2018) · arXiv (Cornell University) · Cited by 31
Self-citationFerrari: A Personalized Federated Learning Framework for Heterogeneous Edge Clients
Yao, Z., Liu, J., Xu, H., Wang, L., Qian, C., & Liao, Y. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3370961 · Cited by 30
A zero-knowledge proof federated learning on DLT for healthcare data
Petrosino, L., Masi, L., D’Antoni, F., Merone, M., & Vollero, L. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104992 · Cited by 33
Cohort-based federated learning services for industrial collaboration on the edge
Hießl, T., Lakani, S. R., Kemnitz, J., Schall, D., & Schulte, S. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.04.021 · Cited by 34
Federated learning approaches for fuzzy cognitive maps to support clinical decision-making in dengue
Hoyos, W., Aguilar, J., & Toro, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106371 · Cited by 33
Practical Defences Against Model Inversion Attacks for Split Neural NetworksPreprint
Titcombe, T., Hall, A. J., Papadopoulos, P., & Romanini, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.05743 · Cited by 71
“FL makes it easier to develop models in domains such as medicine, where legal requirements impose constraints on the sharing of personal data (Brisimi et al., 2018; Kaissis et al., 2020).”
BackgroundAlgorithm Fairness in AI for Medicine and HealthcarePreprint
Chen, R. J., Chen, T., Lipková, J., Wang, J. J., Williamson, D. F. K., Lu, M. Y., Sahai, S., & Mahmood, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.00603 · Cited by 31
“…information, and has since been applied to a variety of clinical setting via: 1) overcoming data interoperability standards that would usually prohibit sensitive health data from being shared: 2) eliminating low data regimes of clinical machine learning tasks that predict rare diseases 171–186 .”
BackgroundSelf-citationHealth Risk Assessment Using Machine Learning: Systematic Review
Abhadiomhen, S. E., Nzeakor, E. O., & Oyibo, K. (2024) · Electronics · doi:10.3390/electronics13224405 · Cited by 32
Almogadwy, B. S., & Alqarafi, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-06574-w · Cited by 29
“Brisimi et al. 30 used FL to address scattered sparse Support Vector Machine problems, which allowed them to provide accurate therapy predictions for individuals suffering from cardiovascular diseases.”
Banad, Y. M., Sharifi, S., & Rezaei, Z. (2025) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2025.101329 · Cited by 45
Energy-efficient client selection in federated learning with heterogeneous data on edge
Zhao, J., Feng, Y., Chang, X., & Liu, C. H. (2022) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-021-01254-8 · Cited by 29
“Due to these reasons, it has been gaining increasing popularity in various research and application fields, such as vehicle network [4], edge cache optimising and computation offloading [5], health care [6], mobile keyboard prediction [7], etc.”
BackgroundEnhancing Federated Learning With Spectrum Allocation Optimization and Device Selection
Zhang, T., Lam, K., Zhao, J., Li, F., Han, H., & Jamil, N. (2023) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3231986 · Cited by 29
“To date, FL has been applied in a wide variety of fields [11], [12], [13], [14].”
BackgroundHashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
Qiu, P., Zhang, X., Ji, S., Fu, C., Yang, X., & Wang, T. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3356164 · Cited by 34
“Vertical federated learning (VFL) [12], [13], [14], [15] is a trending paradigm that addresses a common dilemma faced by companies that share the same user group but differ in the features.”
BackgroundSynergetic Focal Loss for Imbalanced Classification in Federated XGBoost
Tian, J., Tsai, P., Zhang, K., Cai, X., Xiao, H., Yu, K., Zhao, W., & Chen, J. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3254519 · Cited by 33
“Classification methods play a crucial role in knowledge discovery of databases and data mining, such as sparse support vector machine [5], stochastic gradient descent (SGD) based deep learning [6], graph neural networks [7], gradient boosting decision trees (GBDTs) [8], and meta-learning [9] is…”
MethodEnsemble and continual federated learning for classification tasks
Casado, F. E., Lema, D., Iglesias, R., Regueiro, C. V., & Barro, S. (2023) · Machine Learning · doi:10.1007/s10994-023-06330-z · Cited by 33
“…been successfully applied in several real-world tasks, such as mobile keyboard prediction (Hard et al., 2018), malware detection (Rey et al., 2022), energy demand prediction (Saputra et al., 2019), medical diagnosis (Brisimi et al., 2018), or autonomous navigation in robotics (Liu et al., 2019).”
BackgroundFederated Medical Learning Framework Based on Blockchain and Homomorphic Encryption
Yang, X., & Xing, C. (2024) · Wireless Communications and Mobile Computing · doi:10.1155/2024/8138644 · Cited by 30
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
Kong, F., Wang, X., Xiang, J., Yang, S., Wang, X., Yue, M., Zhang, J., Zhao, J., Han, X., Dong, Y., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.03.028 · Cited by 36
“…demonstrated significant potential for sensitive data analytics across various healthcare domains [28], including electronic health records (EHR) [32–36], healthcare Internet of Things (IoT) [37,38], drug discovery [39–41], and medical image analysis (e.g., X-ray, Ultrasound, CT, MRI, PET, WSIs)…”
Ahmed, S. T., Mahesh, T. R., Srividhya, E., Kumar, V. V., Bhatia, S., Albuali, A. A., & Almusharraf, A. I. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01279-4 · Cited by 28
“In this study [7], the researchers have made a detailed study on federal learning for three cloud-edge-devices using the edge devices like smart-phones.”
BackgroundAlzakari, S. A., Sarkar, A., Khan, M. Z., & Alhussan, A. A. (2024) · IEEE Access · doi:10.1109/access.2024.3420078 · Cited by 32
Privacy Sensitive Speech Analysis Using Federated Learning to Assess Depression
Suhas, B. N., & Abdullah, S. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746827 · Cited by 30
“Several recent studies have used FL to reduce privacy risks in analyzing health data [13, 14].”
BackgroundFederated Learning in Healthcare: A Privacy Preserving Approach
Narmadha, K., & Varalakshmi, P. (2022) · Studies in health technology and informatics · doi:10.3233/shti220436 · Cited by 31
“It is possible to extract raw data through model inversion attacks. mechanisms need to be applied to further improve the privacy preservation in FL.”
Walk for Learning: A Random Walk Approach for Federated Learning From Heterogeneous Data
Ayache, G., Dasari, V. R., & Rouayheb, S. E. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3244250 · Cited by 28
“Other applications include learning in IoT networks [4], smart cities and healthcare [5], [6].”
Background
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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