Towards digital health: Integrating federated learning and crowdsensing through the Contigo app

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2024-09-16 DOI:10.1016/j.softx.2024.101885

Daniel Flores-Martin , Sergio Laso , Javier Berrocal , Juan M. Murillo

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引用次数: 0

Abstract

The growing demand for effective healthcare has driven advances in digital health. This digitization supposes a challenge from the point of view of privacy and the treatment of sensitive personal data while providing non-intrusive and easy-to-use digital mechanisms. This paper presents Contigo: a health monitoring system that integrates a mobile application and a web platform for detecting anomalies using Federated Learning techniques. The mobile application collects health and personal data to train a personal predictive model. It is then anonymized and aggregated into a global model to improve efficiency, reducing adoption time for new users. At the same time, the web platform allows healthcare professionals to access the data for its analysis and validation. Contigo addresses the need for user-friendly digital mechanisms in healthcare, addressing privacy concerns while improving data-driven decision-making for professionals and personalized patient care. This approach ensures privacy and facilitates continuous model improvement, providing personalized, proactive, and non-intrusive patient health analytics.

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迈向数字健康：通过 Contigo 应用程序整合联合学习和群体感知技术

对有效医疗保健日益增长的需求推动了数字医疗的发展。这种数字化从隐私和敏感个人数据处理的角度提出了挑战，同时还需要提供非侵入性和易于使用的数字机制。本文介绍的 Contigo 是一个健康监测系统，它集成了一个移动应用程序和一个网络平台，可利用联盟学习技术检测异常情况。移动应用程序收集健康和个人数据，以训练个人预测模型。然后对这些数据进行匿名化处理，并汇总到一个全局模型中，以提高效率，缩短新用户的采用时间。同时，网络平台允许医疗保健专业人员访问数据，进行分析和验证。Contigo 满足了医疗保健领域对用户友好型数字机制的需求，解决了隐私问题，同时改善了专业人员的数据驱动决策和个性化患者护理。这种方法既能确保隐私，又能促进持续的模型改进，提供个性化、主动和非侵入性的患者健康分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.