Del 0 - Computers and Medicine

Knowledge Engineering in Health Informatics

Inbunden, Engelska, 1997

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

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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.

Produktinformation

Utgivningsdatum1997-07-10
Mått155 x 235 x 24 mm
Vikt608 g
FormatInbunden
SpråkEngelska
SerieComputers and Medicine
Antal sidor265
Upplaga1997
FörlagSpringer-Verlag New York Inc.
ISBN9780387949017

Tillhör följande kategorier

Systemvetenskap och AI inom Data och IT
Diagnostiska metoder inom Medicin
Samhällsmedicin och preventiv medicin inom Medicin

1 Background and Legacy.- Overview.- Knowledge Representation and Computation Methodologies.- 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.- The Probabilistic Model: Dealing with Uncertainty.- Probabilistic Information.- Heuristics That Improve the Model.- 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.- 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.- Combining Frames into a Working System.- 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.- The User Interface.- Minimal Diagnosis.- Bayes Calculator.- Interfaces to Other Knowledge.- Compromises.- 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.- 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.- 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.