Two-stage Federated Phenotyping and Patient Representation Learning

2019/08/14 by Dianbo Liu, Dmitriy Dligach, Liu, Dianbo +4 · 7 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Artificial intelligence #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Information extraction #Information retrieval #Machine Learning (cs.LG) #Machine Learning in Healthcare #Machine learning #Medical record #Medicine #Natural language processing #Quality (philosophy) #Representation (politics) #Task (project management) #Topic Modeling #cs.CL #cs.IR #cs.LG

paper · pdf · doi:10.48550/arxiv.1908.05596

published in arXiv (Cornell University) (Cornell University) · 9 pages; Proceedings of the 18th BioNLP Workshop and Shared Task

arxiv created 2019/08/14 · openalex publication_date 2019/08/14 · arxiv updated 2019/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Toward a science of learning systems: a research agenda for the high-functioning Learning Health System
• Federated Learning: Strategies for Improving Communication Efficiency
• Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden
• Towards Federated Learning at Scale: System Design
• Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records

Cited by

• Federated Learning for Cyber Physical Systems: A Comprehensive Survey

Related

• Developing a Portable Natural Language Processing Based Phenotyping System
• Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare
• Natural Language Processing for EHR-Based Computational Phenotyping
• Learning Patient Representations from Text
• A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes