Titlebook: Wireless Mobile Communication and Healthcare; 9th EAI Internationa Juan Ye,Michael J. O‘Grady,Kristina Yordanova Conference proceedings 202

Grasping

织布机

Expanding eVision’s Granularity of Influenza Forecastinge Illnesses (ILI) in the 2019-20 flue season. From which, 410 to 740 thousand were hospitalized and 24 to 62 thousand succumbed to the disease. Therefore, the existence of an early warning mechanism that can alert pharmaceuticals, healthcare providers, and governments to the trends of the influenza

磨坊

Explainable Deep Learning for Medical Time Series Datations, particularly in health care, explanations are essential for building trust in the model. In the field of computer vision, a multitude of explainability methods have been developed to analyze Neural Networks by explaining what they have learned during training and what factors influence their

空洞

树木中

1867-8211 ers are organized in topical sections on wearable technologies; health telemetry; mobile sensing and assessment; machine learning in eHealth applications..978-3-030-70568-8978-3-030-70569-5Series ISSN 1867-8211 Series E-ISSN 1867-822X

Admire

1867-8211 MobiHealth 2020, held in December 2020. Due to Covid-19 pandemic the conference was held virtually.. The book contains 13 full papers selected from the main conference and 10 full papers from two workshops on medical artificial intelligence and on digital healthcare technologies. The conference pap

四目在模仿

Bayesian Inference Federated Learning for Heart Rate Predictionwith autoregression with exogenous variable (ARX) model. The proposed privacy-preserving method achieves accurate and robust heart rate prediction. To validate our method, we conduct extensive experiments with real-world outdoor running exercise data collected from wearable devices.

gangrene

Health Telescope: System Design for Longitudinal Data Collection Using Mobile Applicationsose aiming to undertake a similar endeavor that are vital when developing similar software; this paper aims to highlight both the importance and challenges of measuring the effects of eHealth applications longitudinally.

SHRIK

Explainable Deep Learning for Medical Time Series Data rate and compare explainability methods. As a result, we show that the Grad-CAM++ algorithm outperforms all other methods. Finally, we identify the limits of existing explanation methods for specific datasets, with feature values close to zero.

杀子女者

978-3-030-70568-8ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021