Possibilistic activity recognition with uncertain observations to support medication adherence in an assisted ambient living setting

Dialysis resource allocation in critical care: the impact of the COVID-19 pandemic and the promise of big data analytics

The COVID-19 pandemic resulted in an unprecedented burden on intensive care units (ICUs). With increased demands and limited supply, critical care resources, including dialysis machines, became scarce, leading to the undertaking of value-based cost-effectiveness analyses and the rationing of resources to deliver patient care of the highest quality. A high proportion of COVID-19 patients admitted to the ICU required dialysis, resulting in a major burden on resources such as dialysis machines, nursing staff, technicians, and consumables such as dialysis filters and solutions and anticoagulation medications. Artificial intelligence (AI)-based big data analytics are now being utilized in multiple data-driven healthcare services, including the optimization of healthcare system utilization. Numerous factors can impact dialysis resource allocation to critically ill patients, especially during public health emergencies, but currently, resource allocation is determined using a small number of traditional factors. Smart analytics that take into account all the relevant healthcare information in the hospital system and patient outcomes can lead to improved resource allocation, cost-effectiveness, and quality of care. In this review, we discuss dialysis resource utilization in critical care, the impact of the COVID-19 pandemic, and how AI can improve resource utilization in future public health emergencies. Research in this area should be an important priority.

Intelligent health data analytics: A convergence of artificial intelligence and big data

Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand "how big" is health data. Next, we explain the working of artificial intelligence-based data analytics methods and discuss "what insights" can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.

A Heterogeneous Ensemble Approach for Activity Recognition with Integration of Change Point-Based Data Segmentation

One of the main topics of Smart Home (SH) research is the recognition of activities performed by its inhabitants, which is considered to be one of the bases to foster new technological solutions inside the home, including services to prolong independent living of the elderly. However, current activity recognition proposals still find problems when considering all the different types of activities that can be performed at home, namely static, dynamic, and transitional activities. In this paper, we consider recognition of transitional activities, which is often ignored in most studies. In addition, we propose a novel dynamic segmentation method based on change points in data stream and construct an ensemble of heterogeneous classifiers to recognize twelve activities (of all types). The experiment is conducted on the dataset collected over ten hours by a wearable accelerometer placed on the person’s wrist. The base classifiers selected to form this ensemble are support vector machine (SVM), decision tree (DT) and k-nearest neighbors (KNN). As a result, the proposed approach has achieved an overall classification accuracy equal to 96.87% with 10-fold cross-validation. Moreover, all activity types considered have been similarly well identified.