Discovery and Representation of Causal Relationships from a Large Time-Oriented Clinical Database: The RX Project
The RX Project
Published on 12. March 2013
XIX, 242 pages
978-3-642-93235-9 (ISBN)
Description
As a hospital physician it is impossible to escape the notion that the difficult medical problems one encounters are also being confronted by other' physicians throughout the world. It is equally apparent that without special effort one's own patient observations will not be shared with others. Without the medical literature there would be almost no meaningful shadng of experience. Medical textbooks and journals contain reports of the latest tests and treatments. from university hospitals and research centers. There are, however, definite limitations to the medical literature. First. the literature records only a miniscule and highly select portion of medical experience. Second, because of this selectivity, it may be difficult to apply the findings and recommendations in the literature to one's own patients. One serious consequence of these characteristics of the medical literature is that patients are largely overtreated. Tests and treatments are over-prescribed, and adverse effe
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R. L. Blum
Discovery and Representation of Causal Relationships from a Large Time-Oriented Clinical Database: The RX Project
The RX Project
Book
12/1982
1st Edition
Springer
€106.99
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Content
1. The RX Project: An Overview.- 1.1. Introduction.- 1.2. Evolution of Empirical Knowledge.- 1.3. Inference from Non-Randomized Databases: The Problems.- 1.4. Causal Models: The RX Knowledge Base.- 1.5. The Discovery Module.- 1.6. The RX Knowledge Base: Its Role.- 1.7. The Study Module.- 1.8. Conclusions.- 2. The Time-Oriented Database.- 2.1. Introduction.- 2.2. Computer Facilities.- 2.3. The RX Database: Overview of the Logical Structure.- 2.4. Database Implementation Issues.- 2.5. Summary.- 3. The RX Knowledge Base: An Overview.- 3.1. Introduction.- 3.2. Categories of Schema Properties.- 3.3. Contents of the RX Knowledge Base.- 3.4. Inheritance Mechanisms.- 3.5. The RX Knowledge Base: Interactive Use.- 4. The Properties and Representation of Causal Relationships.- 4.1. An Operational Definition of Causality.- 4.2. Features of Individual Cause/Effect Relationships.- 4.3. Representation of Causal Relationships.- 4.4. Representation of Causal Links in RX.- 4.5. AI Research on Causal Models.- 4.6. Conclusion.- 5. Derived Variables, Proxy Variables, and Time-Dependent Access Functions.- 5.1. Introduction.- 5.2. The Derivation of Interval-Events.- 5.3. Time-Dependent Database Access Functions.- 5.4. Latent Variables and Proxies.- 6. The Discovery Module.- 6.1. Introduction.- 6.2. The Algorithm.- 6.3. Automated Inference: A Comparison with Other Work.- 7. The Study Module.- 7.1. Overview.- 7.2. Determination of Feasibility of Study.- 7.3. Confounding Variables and Causal Dominators.- 7.4. Determination of Methods for Controlling Confounding Variables.- 7.5. Choice of Study Design and Statistical Method.- 7.6. Formatting of Database Access Functions.- 7.7. Determination of Eligibility Criteria.- 7.8. Statistical Analysis: Fitting the Model.- 7.9. Interpretation of Results.- 7.10. Incorporation of the New Causal Relationship into the KB.- 8. Statistical Analysis of Longitudinal Data.- 8.1. The Longitudinal Model.- 8.2. Regression Analysis.- 8.3. Adequacy of the Model.- 9. Medical Results.- 9.1. Introduction.- 9.2. Effects of Prednisone.- 9.3. Effect of Prednisone on Cholesterol.- 9.4. Refinements,.- 10. Summary, Applications, Future Development.- 10.1. Introduction.- 10.2. Project Summary.- 10.3. Applicability of the RX Prbject.- 10.4. Accession of Data 189 10.4.1. Post-Marketing Surveillance of Drugs.- 10.5. The RX Project: Limitations and Future Development.
