The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.
Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records
Open accessAbstract
Electronic medical records (EMRs) support the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But so far most algorithms have been centralized, taking little account of the decentralized, non-identically independently distributed (non-IID), and privacy-sensitive characteristics of EMRs that can complicate data collection, sharing and learning. To address this challenge, we introduced a community-based federated machine learning (CBFL) algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered the distributed data into clinically meaningful communities that captured similar diagnoses and geographical locations, and learnt one model for each community. Throughout the learning process, the data was kept local at hospitals, while locally-computed results were aggregated on a server. Evaluation results show that CBFL outperformed the baseline federated machine learning (FL) algorithm in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC), Area Under the Precision-Recall Curve (PR AUC), and communication cost between hospitals and the server. Furthermore, communities' performance difference could be explained by how dissimilar one community was to others.
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“Federated learning is a viable method to connect EHR data from medical institutions, allowing them to share their experiences, and not their data, with a guarantee of privacy [9, 25, 34, 45, 65, 82].”
From Federated Learning for Healthcare Informatics (2021) · cites this paper as [45]
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“Moreover, to support the federated mortality prediction, patient clustering is adopted in [86], where patient subgroups capture similar diagnoses and geographical locations to train a neural network model to forecast mortality based on drug features.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [86]
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“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 [19]
In the citing paper
“For example, with access to electronic health records, FL can help to find clinically similar patients and predict hospitalizations due to cardiac events, mortality and ICU stay time [52].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [52]
In the citing paper
“FL is a highly effective method to gather EHR data from hospitals to provide a knowledge-sharing platform without the need of sharing personal data, all while preserving the privacy [104, 105].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [105]
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Citations
Cites
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
Improving Primary Care for Patients With Chronic Illness
Bodenheimer, T. (2002) · JAMA · doi:10.1001/jama.288.14.1775 · Cited by 2,961
Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., & Farhadi, A. (2015) · arXiv · doi:10.48550/arxiv.1511.06335 · Cited by 2,476
Improving Primary Care for Patients With Chronic Illness: The Chronic Care Model, Part 2
Bodenheimer, T., Wagner, E. H., & Grumbach, K. (2002) · JAMA · doi:10.1001/jama.288.15.1909 · Cited by 2,413
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077
Pollard, T. J., Johnson, A. E. W., Raffa, J. D., Celi, L. A., Mark, R. G., & Badawi, O. (2018) · Scientific Data · doi:10.1038/sdata.2018.178 · Cited by 1,993
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Nejad, M. C., Matthiesen, R. V., Dukovska‐Popovska, I., & Johansen, J. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-34459-5_4
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Schmidt, O., Weber, L., & Richter, J. (2026) · Journal of Health Informatics and Digital Systems · doi:10.68159/j531076390
Metadata
- Authors
- Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Beijing University of Technology (CN)
- Northeastern University (US)
- Boston Children's Hospital (US)
- Corresponding author
- Liu, Dianbo
- ORCID
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 99
- Pages
- 103291
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2977072935
- Cited by
- 437
- References
- 49
- Citations by year
- 2026: 31 · 2025: 58 · 2024: 87 · 2023: 95 · 2022: 66 · 2021: 60 · 2020: 35 · 2019: 4
- FWCI
- 18.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- federated learning, patient clustering, non-IID data, electronic health records, intensive care unit, mortality prediction, length of hospital stay, privacy-preserving machine learning, communication overhead
- MeSH terms
- Machine Learning, Algorithms, Female, Humans, Length of Stay, Male, Middle Aged, Cluster Analysis, Critical Illness, Electronic Health Records
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