mlsanityData to 14 Oct 2026

Two-stage Federated Phenotyping and Patient Representation Learning

arXiv 1908.05596 ↗First posted 14 Aug 2019cs.IR cs.CL cs.LGcited by 19

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Abstract (arXiv)

A large percentage of medical information is in unstructured text format in electronic medical record systems. Manual extraction of information from clinical notes is extremely time consuming. Natural language processing has been widely used in recent years for automatic information extraction from medical texts. However, algorithms trained on data from a single healthcare provider are not generalizable and error-prone due to the heterogeneity and uniqueness of medical documents. We develop a two-stage federated natural language processing method that enables utilization of clinical notes from different hospitals or clinics without moving the data, and demonstrate its performance using obesity and comorbities phenotyping as medical task. This approach not only improves the quality of a specific clinical task but also facilitates knowledge progression in the whole healthcare system, which is an essential part of learning health system. To the best of our knowledge, this is the first application of federated machine learning in clinical NLP.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Aug 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 7; 2023: 0; 2024: 1; 2025: 1; 2026: 1.

19 papers.

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  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  2. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 43
  3. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  4. 2021Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settingscited by 11
  5. 2022Federated learning: Applications, challenges and future directionscited by 11
  6. 2021Scaling Neuroscience Research using Federated Learningcited by 10
  7. 2022Towards the Practical Utility of Federated Learning in the Medical Domaincited by 9
  8. 2021Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Casecited by 7
  9. 2022Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Datacited by 7
  10. 2021What Do You See in this Patient? Behavioral Testing of Clinical NLP Modelscited by 6
  11. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  12. 2021Communication-Efficient Federated Learning for Neural Machine Translationcited by 5
7 more
  1. 2021PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical recordscited by 4
  2. 2021Aggregation Delayed Federated Learningcited by 2
  3. 2022Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledgecited by 2
  4. 2022Federated Named Entity Recognitioncited by 1
  5. 2024FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regionscited by 1
  6. 2022Construction of extra-large scale screening tools for risks of severe mental illnesses using real world healthcare datacited by 0
  7. 2026FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregationcited by 0