Bibliometric analysis of emerging technologies in the field of computer science helping in ovarian cancer research

This study is carried out to provide an analysis of the literature available at the intersection of ovarian cancer and computing. A comprehensive search was conducted using Scopus database for English-language peer-reviewed articles. The study administers chronological, domain clustering and text analysis of the articles under consideration to provide high-level concept map composed of specific words and the connections between them

Medical Data Visual Synchronization and Information interaction Using Internet-based Graphics Rendering and Message-oriented Streaming

The rapid technology advances in medical devices make possible the generation of vast amounts of data, which contain massive quantities of diagnostic information. Interactively accessing and sharing the acquired data on the Internet is critically important in telemedicine. However, due to the lack of efficient algorithms and high computational cost, collaborative medical data exploration on the Internet is still a challenging task in clinical settings. Therefore, we develop a web-based medical image rendering and visual synchronization software platform, in which novel algorithms are created for parallel data computing and image feature enhancement, where Node.js and Socket.IO libraries are utilized to establish bidirectional connections between server and clients in real time. In addition, we design a new methodology to stream medical information among all connected users, whose identities and input messages can be automatically stored in database and extracted in web browsers. The presented software framework will provide multiple medical practitioners with immediate visual feedback and interactive information in applications such as collaborative therapy planning, distributed treatment, and remote clinical health care

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either \emph{(a) personalized medicine for individuals} or \emph{(b) public health for populations}, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- \emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloud-based data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \& Participation}, \emph{(2) Health Surveillance \& Data Collection}, and \emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.Comment: Submitted to a journal for revie