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Cited by 437Machine Learning in HealthcarePrivacy-Preserving Technologies in DataElectronic Health Records Systems

Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records

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Huang, Li, Shea, Andrew L., Qian, Huining, Masurkar, Aditya, Deng, Hao, Liu, Dianbo·2019·Journal of Biomedical Informatics
DOI 10.1016/j.jbi.2019.103291
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Abstract

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 can connect EHR data across medical institutions, letting them share experiences rather than data while preserving privacy.

    “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]

    The Introduction (page unavailable) says federated learning keeps EMR data local and aggregates locally computed results, allowing institutions to learn together without sharing the records. The Conclusions (page unavailable) describe the authors’ clustering method as avoiding a privacy breach. Neither passage establishes the citing sentence’s stronger claim of a guarantee of privacy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Federated learning has been applied in electronic health records to represent and find clinically similar patients, predict hospitalisations due to cardiac events, mortality and ICU stay time; in medical imaging for whole-brain segmentation in MRI and brain tumour segmentation; for fMRI classification to find disease-related biomarkers; and suggested as a promising approach for COVID-19.

    “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]

  • 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.

    “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]

  • 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 privacy.

    “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]

  • Patient clustering captures similar diagnoses and geographical locations to train a neural network model to forecast mortality based on drug features.

    “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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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)
DOI
10.1016/j.jbi.2019.103291
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 · 2018: 1
FWCI
18.61
Type
journal article
Language
en
Open access
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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