Text mining of web-based medical content

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.