- Includes Text Mining and Natural Language Processing Methods for extracting information from electronic health records and biomedical literature.
- Analyzes text analytic tools for new media such as online forums, social media posts, tweets and video sharing.
- Demonstrates how to use speech and audio technologies for improving access to online content for the visually impaired.
Text Mining of Web-Based Medical Content examines various approaches to deriving high quality information from online biomedical literature, electronic health records, query search terms, social media posts and tweets. Using some of the latest empirical methods of knowledge extraction, the authors show how online content, generated by both professionals and laypersons, can be mined for valuable information about disease processes, adverse drug reactions not captured during clinical trials, and tropical fever outbreaks. Additionally, the authors show how to perform infromation extraction on a hospital intranet, how to build a social media search engine to glean information about patients' own experiences interacting with healthcare professionals, and how to improve access to online health information.
This volume provides a wealth of timely material for health informatic professionals and machine learning, data mining, and natural language researchers.
Topics in this book include:
- Clinical Documents in Electronic Health Records
- Summarization Techniques for Online Health Data
- Natural Language Processing for Text Mining
- Query Expansion Techniques for Tweets
- Online Video Data Retrieval of Health-Related Videos
- Dengue Fever Outbreaks
- Bioemergencies and Social Media Posts
- Speech-based Disease Screening for Malaria, Yellow Fever, Typhoid, and Lassa Fever
- Audio Access to Online Video Data for the Visually Impaired
About the Author: Amy Neustein, Founder and CTO, Linguistic Technology Systems, Fort Lee, NJ, USA.
