DiTeX: Disease-related topic extraction system through internet-based sources

A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

Healthcare information systems can reduce the expenses of treatment, foresee episodes of pestilences, help stay away from preventable illnesses, and improve personal life satisfaction. As of late, considerable volumes of heterogeneous and differing medicinal services data are being produced from different sources covering clinic records of patients, lab results, and wearable devices, making it hard for conventional data processing to handle and manage this amount of data. Confronted with the difficulties and challenges facing the process of managing healthcare big data such as volume, velocity, and variety, healthcare information systems need to use new methods and techniques for managing and processing such data to extract useful information and knowledge. In the recent few years, a large number of organizations and companies have shown enthusiasm for using semantic web technologies with healthcare big data to convert data into knowledge and intelligence. In this paper, we review the state of the art on the semantic web for the healthcare industry. Based on our literature review, we will discuss how different techniques, standards, and points of view created by the semantic web community can participate in addressing the challenges related to healthcare big data.

Word2vec convolutional neural networks for classification of news articles and tweets

Big web data from sources including online news and Twitter are good resources for investigating deep learning. However, collected news articles and tweets almost certainly contain data unnecessary for learning, and this disturbs accurate learning. This paper explores the performance of word2vec Convolutional Neural Networks (CNNs) to classify news articles and tweets into related and unrelated ones. Using two word embedding algorithms of word2vec, Continuous Bag-of-Word (CBOW) and Skip-gram, we constructed CNN with the CBOW model and CNN with the Skip-gram model. We measured the classification accuracy of CNN with CBOW, CNN with Skip-gram, and CNN without word2vec models for real news articles and tweets. The experimental results indicated that word2vec significantly improved the accuracy of the classification model. The accuracy of the CBOW model was higher and more stable when compared to that of the Skip-gram model. The CBOW model exhibited better performance on news articles, and the Skip-gram model exhibited better performance on tweets. Specifically, CNN with word2vec models was more effective on news articles when compared to that on tweets because news articles are typically more uniform when compared to tweets.

PEACOCK: A Map-Based Multitype Infectious Disease Outbreak Information System

A map-based infectious disease outbreak information system, called PEACOCK, that provides three types of necessary infectious disease outbreak information is presented. The system first collects the infectious disease outbreak statistics from the government agencies and displays the number of infected people and infection indices on the map. Then, it crawls online news articles for each infectious disease and displays the number of mentions of each disease on the map. Users can also search for news articles regarding the disease. Finally, it retrieves the portal search query data and plots the graphs of the trends. It divides the risk into three levels (i.e., normal, caution, and danger) and visualizes them using different colors on the map. Users can access infectious disease outbreak information accurately and quickly using the system. As the system visualizes the information using both a map and various types of graphs, users can check the information at a glance. This system is in live at http://www.epidemic.co.kr/map.

DOVE: An Infectious Disease Outbreak Statistics Visualization System

Humans are susceptible to various infectious diseases. However, humanity still has limited responses to emergent and recurrent infectious diseases. Recent developments in medical technology have led to various vaccines being developed, but these vaccines typically require a considerable amount of time to counter infectious diseases. Therefore, one of the best methods to prevent infectious diseases is to continuously update our knowledge with useful information from infectious disease information systems and taking active steps to safeguard ourselves against infectious diseases. Some existing infectious disease information systems simply present infectious disease information in the form of text or transmit it via e-mail. Other systems provide data in the form of files or maps. Most existing systems display text-centric information regarding infectious disease outbreaks. Therefore, understanding infectious disease outbreak information at a glance is difficult for users. In this paper, we propose the infectious disease outbreak statistics visualization system, called to DOVE, which collects infectious disease outbreak statistics from the Korea Centers for Disease Control & Prevention and provides statistical charts with district, time, infectious disease, gender, and age data. Users can easily identify infectious disease outbreak statistics at a glance by simply entering the district, time, and name of an infectious disease into our system. Additionally, each statistical chart allows users to recognize the characteristics of an infectious disease and predict outbreaks by investigating the outbreak trends of that disease. We believe that our system provides effective information to help prevent infectious disease outbreaks. Our system is currently available on the web at http://www.epidemic.co.kr/statistics.