Knowledge Engineering in Health Informatics - Rilegato

Warner, Homer R.; Sorenson, Dean K.; Bouhaddou, Omar

Rilegato

ISBN 10: 0387949011 ISBN 13: 9780387949017

Casa editrice: Springer-Verlag New York Inc., 1997

Riassunto

The "information explosion" in recent decades has made it impossible for practicing physicians (even specialists) to keep up with all the information which would be potentially useful in making optimal clinical judgements. As a result, it's not surprising that empirical studies have demonstrated that physicians do not always make optimal decisions. Medical Expert systems are intended to support (not replace) physicians and healthcare providers in their goal to provide consistently optimal healthcare to every patient. Knowledge Engineering in Health Informatics is a guide to the creation of such systems. The core material for courses such as Medical Knowledge Engineering and Expert System Development, it allows non-experts (assisted by computer) to make diagnostic decisions with the precision and accuracy of medical experts.

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Contenuti

1 Background and Legacy.- Overview.- Why Build Medical Expert Systems?.- Definitions.- General Design Questions and Related Issues.- State of the Art.- Knowledge Representation and Computation Methodologies.- Rule-Based Medical Expert Systems.- Probabilistic Medical Expert Systems.- Hierarchical Knowledge.- Hybrid Models and Ambitious Adaptations.- 2 The Expert System Model.- to Modeling.- Choosing a System to Model.- Choosing a Model.- 3 Iliad: The Model Used for This Text.- The Frame Concept.- Building Individual Decision Frames.- Bayesian Frames.- Boolean Frames.- Value Frames.- Nested Frames: Clusters.- The Probabilistic Model: Dealing with Uncertainty.- The Bayes Equation.- Ways of Handling the Assumption of Independence.- Probabilistic Information.- Partial Information.- The “Closeness to True/False” Concept.- Information Content.- Passing Information Among Bayesian and Boolean Frames.- Using Partial Information for Decision Making.- Heuristics That Improve the Model.- Risk Flags.- Display Logic.- Data Drivers.- 4 The Data Dictionary: Limiting the Domain of the Model.- Organization of the Dictionary.- Context Versus Concept.- Hierarchical Relationships.- Granularity of the Dictionary.- Modifying the Dictionary.- Knowledge Contained in the Dictionary.- Inferencing from the Hierarchy.- Word Relations.- Data Relations.- 5 The Knowledge Engineering Process.- How to Structure/Model the Knowledge.- The Overall Process.- Knowledge Sources: Advantages and Limitations of Each.- Literature.- Patient Data Repositories.- Expert Opinion.- Which Findings to Include in a Frame.- Probabilistic and Deterministic Logic.- Reasons to Cluster.- Types of Clusters.- Frames That Return a Value.- Estimating Probabilities.- Testing Frames in Isolation.- Sources of Error.- Tools to Facilitate the Knowledge Engineering Process.- Text Editor and Database.- A Working Outline or Hierarchy.- Accessing Normal Values and Frequently Used Numbers.- Accessing the Dictionary.- Maintaining Consistency Between Numerical Estimates.- Relationships Between Frames.- Documenting Sources of Knowledge and the Knowledge Engineering Process.- Saving, Printing, and Statistics.- Combining Frames into a Working System.- Mapping Free Text to a Structured Vocabulary.- Compiling Frames into a Working Knowledge Base.- 6 Evaluation of the Model.- Testing and Refining the Compiled Knowledge.- Appropriateness of Decisions Based on Data Entered by Experts.- Testing with Data Newly Entered from Patient Charts.- Testing with Cases Stored Earlier.- Modifying Source Frames As Required: The Iterative Process.- 7 Applications of the Model.- Modes of Use.- Consultation Mode.- Critiquing Mode.- Simulation Mode.- The User Interface.- Input.- Output.- Browsing Frames.- Viewing and Using the Differential.- Patient Data Window.- Explain Findings.- Most Useful Information.- Minimal Diagnosis.- Bayes Calculator.- Interfaces to Other Knowledge.- Relevant Literature.- Pictures.- Sound.- Animation/Video.- ICD9 Codes.- Other Coding Systems.- Other Expert Systems.- Compromises.- Ease of Data Entry Versus Confusion Regarding “Inferred No”.- Response Time Versus Sophistication of Algorithm.- 8 Lessons Learned.- Teaching Medical Clerks, Physician Assistants, and Other Trainees.- As a Tool for Preauthorization.- As a Screening Tool for Quality Improvement.- Commercial Users of Iliad.- 9 Knowledge Engineering Tools.- Knowledge Acquisition.- Structuring and Coding the Knowledge.- The Dictionary Program.- Frame Authoring.- Syntax Checking and Compilation.- Testing the Knowledge Base.- Summary.- 10 Example Knowledge Bases.- The Knowledge Engineering Class.- Medical and Pediatric HouseCall.- Symptom Analysis.- Deriving HouseCall from Iliad.- Knowledge Engineering for HouseCall.- 11 Future Challenges.- Links to Patient Data: Client Server/Version of Iliad.- Architecture.- Applications.- Benefits.- Future Directions.- References.- Appendices.- 1 Example Hierarchies of Top-Level Diseases (Final Diagnoses) in Various Medical Specialties.- 2 Approximate Estimated Prevalences for Selected Top-Level Diseases in a Family Practice Setting, Categorized by Specialty.- 3 Using the Iliad KE Tool.- 4 Some Example “Domain-Specific” Symptom Lists.- 5 Example Data Relations.- 6 Example Word Relations.

Product Description

Book by Warner Homer R Sorenson Dean K Bouhaddou Omar

Dettagli dell'edizione

• Editore: Springer-Verlag New York Inc.
• Data di pubblicazione: 1997
• Lingua: Inglese
• ISBN 10: 0387949011
• ISBN 13: 9780387949017
• Rilegatura: Copertina rigida
• Numero di pagine: 281
• Contatto del produttore: non disponibile
• Persona responsabile: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania