Knowledge engineering in health informatics
著者
書誌事項
Knowledge engineering in health informatics
（Computers and medicine）
Springer, c1997
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注記
Includes bibliographical references (p. 99-103) and index
System requirements for accompanying CD: IBM PC or compatible (386/16 MHz or greater with at least 6 MB of memory) ; CD-ROM drive ; Windows '95/NT
Accompanied by 1 CD-ROM: Iliad knowledge engineering tools CD-ROM
内容説明・目次
内容説明
The "information explosion" in recent decades has made it impossible for practicing physicians (even specialists) to keep up with all the information potentially at their disposal. As a result, it is not surprising that empirical studies have shown that physicians do not always make optimal decisions. Thus, medical expert systems are now available to support - not replace - physicians and healthcare providers in their goal of providing the best possible healthcare to every patient. Knowledge Engineering in Health Informatics is a guide to the creation of such systems. Presenting the core material for courses such as Medical Knowledge Engineering and Expert System Development, it allows non-experts to make diagnostic decisions with the precision and accuracy of medical experts thanks to the help of the computer.
目次
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.
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