Network Medicine applies network science approaches to investigate disease
pathogenesis. Many different analytical methods have been used to infer relevant molecular …

We present a comprehensive, detailed review of time-series data analysis, with emphasis on
deep time-series clustering (DTSC), and a case study in the context of movement behavior …

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 …

Event sequence data record series of discrete events in the time order of occurrence. They
are commonly observed in a variety of applications ranging from electronic health records to …

Deep Q-Network (DQN), as one type of deep reinforcement learning model, targets to train
an intelligent agent that acquires optimal actions while interacting with an environment. The …

Federal investment in health information technology has incentivized the adoption of
electronic health record systems by physicians and health care organizations; the result has …

Event sequence data analysis is common in many domains, including web and software
development, transportation, and medical care. Few have investigated visualization …

Visual analytics for time series data has received a considerable amount of attention.
Different approaches have been developed to understand the characteristics of the data and …

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

Event sequence data such as electronic health records, a person's academic records, or car
service records, are ordered series of events which have occurred over a period of time …