Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling
Author:
Navaz Alramzana Nujum1ORCID, Serhani Mohamed Adel2ORCID, El Kassabi Hadeel T. El3ORCID, Taleb Ikbal4ORCID
Affiliation:
1. Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain P.O. Box 15551, United Arab Emirates 2. College of Computing and Informatics, Sharjah University, Sharjah P.O. Box 27272, United Arab Emirates 3. Faculty of Applied Sciences & Technology, Humber College Institute of Technology & Advanced Learning, Toronto, ON M9W 5L7, Canada 4. College of Technological Innovation, Zayed University, Abu Dhabi P.O. Box 144534, United Arab Emirates
Abstract
Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g., the edge). However, data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, it is crucial to keep an eye on data quality and spot problems as quickly as possible, so that they do not mislead clinical judgments and lead to the wrong course of action. In this article, a novel approach called federated data quality profiling (FDQP) is proposed to assess the quality of the data at the edge. FDQP is inspired by federated learning (FL) and serves as a condensed document or a guide for node data quality assurance. The FDQP formal model is developed to capture the quality dimensions specified in the data quality profile (DQP). The proposed approach uses federated feature selection to improve classifier precision and rank features based on criteria such as feature value, outlier percentage, and missing data percentage. Extensive experimentation using a fetal dataset split into different edge nodes and a set of scenarios were carefully chosen to evaluate the proposed FDQP model. The results of the experiments demonstrated that the proposed FDQP approach positively improved the DQ, and thus, impacted the accuracy of the federated patient similarity network (FPSN)-based machine learning models. The proposed data-quality-aware federated PSN architecture leveraging FDQP model with data collected from edge nodes can effectively improve the data quality and accuracy of the federated patient similarity network (FPSN)-based machine learning models. Our profiling algorithm used lightweight profile exchange instead of full data processing at the edge, which resulted in optimal data quality achievement, thus improving efficiency. Overall, FDQP is an effective method for assessing data quality in the edge computing environment, and we believe that the proposed approach can be applied to other scenarios beyond patient monitoring.
Funder
Zayed health science center
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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