相关论文: Causally Linking Health Application Data and Perso…
Main objective of this study is to introduce an expert system-based mHealth application that takes Artificial Intelligence support by considering previously introduced solutions from the literature and employing possible requirements for a…
In this short paper, we explore the enrichment of event logs with data from wearable devices. We discuss three approaches: (1) treating wearable data as event attributes, linking them directly to individual events, (2) treating wearable…
The growing demand for accurate, continuous, and non-invasive health monitoring has propelled multi-sensor data fusion to the forefront of healthcare technology. This review aims to provide an overview of the development of fusion…
With the heightened digitization of the workplace, alongside the rise of remote and hybrid work prompted by the pandemic, there is growing corporate interest in using passive sensing technologies for workplace wellbeing. Existing research…
Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions…
Consumer-grade smartwatches offer a new personalized health monitoring option for general consumers globally as cardiovascular diseases continue to prevail as the leading cause of global mortality. The development and validation of reliable…
Personalised medicine strives to identify the right treatment for the right patient at the right time, integrating different types of biological and environmental information. Such information come from a variety of sources: omics data…
An infectious kind of disease affects a huge number of human beings. A lot of investigation being conducted throughout the world. There are many interactive hardware platform packages like IoT in healthcare including smart tracking, smart…
In this age of heterogeneous systems, diverse technologies are integrated to create application-specific solutions. The recent upsurge in acceptance of technologies such as cloud computing and ubiquitous Internet has cleared the path for…
The integration of medical devices in the patient treatment process becomes increasingly important due to the efficiency of the technology. On the one hand, medical devices hardware is more powerful and its integration with the software…
Personal devices are omnipresent in our lives, seamlessly monitoring our activities, from smart rings tracking sleep patterns to smartwatches keeping an eye on missed heartbeats. The rich data streams from such devices fuel advanced…
Understanding the relation of events plays an important role in different domains, such as identifying the reasons for users' certain actions from application logs as well as explaining sports players' behaviors according to historical…
To empower users of wearable medical devices, it is important to enable methods that facilitate reflection on previous care to improve future outcomes. In this work, we conducted a two-phase user-study involving patients, caregivers, and…
Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role in developing ML-based healthcare systems that directly affect people's lives. Many of the ethical…
The widespread of data visualisation tools on smartphones has provided end users an easy way to track their health data, leading designers to put more effort into delivering suitable visualisations. Both academia and industry have developed…
Background: Access to medical care is strongly dependent on resource allocation, such as the geographical distribution of medical facilities. Nevertheless, this data is usually restricted to country official documentation, not available to…
Knowing the state of our health at every moment in time is critical for advances in health science. Using data obtained outside an episodic clinical setting is the first step towards building a continuous health estimation system. In this…
The use of AI analytics in health informatics has seen a rapid growth in recent years. In this talk, we look at AI analytics use in managing chronic health conditions such as diabetes, obesity, etc. We focus on the challenges in managing…
Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is…
Visualizing medical histories of patients with complex chronic diseases (e.g., discordant chronic comorbidities (DCCs)) is a challenge for patients, their healthcare providers, and their support network. DCCs are health conditions in which…