The continuously increasing cost of the US healthcare system has received significant
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …

Predictive data mining in clinical medicine deals with learning models to predict patients'
health. The models can be devoted to support clinicians in diagnostic, therapeutic, or …

Internet of Things (IoT) technology provides a competent and structured approach to handle
service deliverance aspects of healthcare in terms of mobile health and remote patient …

In the last few years, the m-healthcare applications based on Internet of Things (IoT) have
provided multi-dimensional features and real-time services. These applications provide a …

The national adoption of electronic health records (EHR) promises to make an
unprecedented amount of data available for clinical research, but the data are complex …

Supplying a comprehensive overview of healthcare analytics research, Healthcare Data
Analytics provides an understanding of the analytical techniques currently available to solve …

Background and objective Critical care patient events like sepsis or septic shock in intensive
care units (ICUs) are dangerous complications which can cause multiple organ failures and …

We study the problem of learning classification models from complex multivariate temporal
data encountered in electronic health record systems. The challenge is to define a good set …

Objective: To test the feasibility of using data collected in electronic medical records for
development of effective models for diabetes risk forecasting. Methods: Using available …

One of the field in which models of abstraction have been proposed is Artificial Intelligence
(AI). This chapter has two parts: one presents an overview of the formal models, either …