Text Mining of Web-based Medical Content (Speech Technology and Text Mining in Medicine and Health Car)

Text Mining of Web-Based Medical Content examines web mining for extracting useful information that can be used for treating and monitoring the healthcare of patients. This work provides methodological approaches to designing mapping tools that exploit data found in social media postings. Specific linguistic features of medical postings are analyzed vis-a-vis available data extraction tools for culling useful information. Read more... Preface Contents List of authors Part I. Methods and techniques for mining biomedical literature and electronic health records 1 Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature 1.1 Introduction 1.2 Background 1.2.1 Clinical and biomedical text 1.2.2 Information retrieval 1.2.2.1 Information retrieval process 1.2.3 Information extraction 1.2.4 Challenges to biomedical information extraction systems 1.2.5 Applications of biomedical information extraction tools 1.3 Biomedical knowledge extraction using text mining. 1.3.1 Unstructured text gathering and preprocessing1.3.1.1 Text gathering 1.3.1.2 Text preprocessing 1.3.2 Extraction of features and semantic information 1.3.3 Analysis of annotated texts 1.3.3.1 Algorithms for text classification 1.3.3.2 Classification evaluation measures 1.3.4 Presentation 1.4 Text mining tools 1.5 Summary Appendix "A" References 2 Unlocking information in electronic health records using natural language processing: a case study in medication information extraction 2.1 Introduction to clinical natural language processing 2.2 Medication information in EHRs. 2.3 Medication information extraction systems and methods2.3.1 Relevant work 2.3.2 Summary of approaches 2.3.2.1 Rule-based methods 2.3.2.2 Machine learning-based methods 2.3.2.3 Hybrid methods 2.4 Uses of medication information extraction tools in clinical research 2.5 Challenges and future work References 3 Online health information semantic search and exploration: reporting on two prototypes for performing information extraction on both a hospital intranet and the world wide web 3.1 Introduction 3.2 Background 3.3 Related work 3.3.1 Semantic search. 3.3.2 Health information search and exploration3.3.3 Information extraction for health 3.3.4 Ontology-based information extraction -- OBIE 3.4 A general architecture for health search: handling both private and public content 3.5 Two semantic search systems for health 3.5.1 MedInX 3.5.1.1 MedInX ontologies 3.5.1.2 MedInX system 3.5.1.3 Representative results 3.5.2 SPHInX -- Semantic search of public health information in Portuguese 3.5.2.1 System architecture 3.5.2.2 Natural language processing 3.5.2.3 Semantic extraction models 3.5.2.4 Semantic extraction and integration. 3.5.2.5 Search and exploration3.6 Conclusion Acknowledgments References Part II. Machine learning techniques for mining medical search queries and health-related social media posts and tweets 4 Predicting dengue incidence in Thailand from online search queries that include weather and climatic variables 4.1 Introduction 4.1.1 Dengue disease in the world 4.2 Epidemiology of dengue disease 4.2.1 Temperature change and the ecology of A. aegypti 4.3 Using online data to forecast incidence of dengue 4.3.1 Background and related work 4.3.2 Methodology for dengue cases prediction.

•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:
•Mining Biomedical Literature and Clinical Narratives
•Medication Information Extraction
•Machine Learning Techniques for Mining Medical Search Queries
•Detecting the Level of Personal Health Information Revealed in Social Media
•Curating Layperson’s Personal Experiences with Health Care from Social Media and Twitter
•Health Dialogue Systems for Improving Access to Online Content
•Crowd-based Audio Clips to Improve Online Video Access for the Visually Impaired
•Semantic-based Visual Information Retrieval for Mining Radiographic Image Data
•Evaluating the Importance of Medical Terminology in YouTube Video Titles and Descriptions