Chapter 12. DECISION RULES AND EXPRESSIONS

1. Chapter 12. DECISION RULES ANDEXPRESSIONS

2. 12.1 Introduction • Decision rules often are represented in one of two formats • Procedures • Production rules

3. 12.2 PROCEDURAL KNOWLEDGE • Two key features characterize this representation • Clinical knowledge and inferencing or control knowledge are mixed in the same representation • The flow of control is made explicit

4. FIGURE 12-1 Decision rule represented as a decision tree. The decision rule helps determine the diagnosis in a case of a patient with a sore throat based on physical examination findings.

5. Flaws • The mixture of control and clinical knowledge • Makes it difficult to acquire and to maintain the knowledge • Subsequent edits of the clinical knowledge may inadvertently alter the control structures embedded in the same statements • To avoid these challenges, many CDS systems employ an architecture that separates control knowledge from clinical knowledge.

6. 12.3 KNOWLEDGE AS PRODUCTION RULES • IF <condition> THEN <action> FIGURE 12-2 Decision rule represented as production rules. This collection of production rules represents the same knowledge as the decision tree in Figure 12-1. Each rule associates a Boolean condition that evaluates to true or false with an action (in this case, a diagnosis). The terms “erythema,”“pus,” and “adenopathy” are Boolean variables that evaluate to true or false based on data available to the CDSS.

7. The operation of a production rule CDS system consists of repeated cycles of • Match • Select • Execute • Applying • The knowledge base against data available to the CDS system in order to reach a desired conclusion

8. Inferencingmechanisms • Forward chaining • There is a large amount of data relative to the possible conclusions to be drawn from those data • If the CDS system is triggered or driven by the arrival of new data • Backward chaining • If the CDS system is used to critique a selection such as a treatment or a diagnosis made by a clinician

9. The key advantage of a production-rule CDS system over a procedural representation • The representation of knowledge is independent of the control knowledge needed to operate the CDS system and manage the inferencing process

10. 12.4 THE HYBRID APPROACH: ARDEN SYNTAX

11. 12.5 EXPRESSION LANGUAGES • To allow the knowledge engineer to build up statements that query data, logically manipulate them, provide for reasoning over them • GELLO (Sordo 2004) • A standard of the Object Management Group (OMG), and can be used with any object-oriented data mode • Can be used with the standard HL7 RIM to extract data from clinical repositories and to manipulate those data

12. FIGURE 12-5 Example of GELLO encoding a simple guideline. This guideline rep resents the same knowledge contained in the Arden Syntax MLM in Figure 12-4. Because GELLO was created to extract data from clinical repositories, to manipulate those data and to reason over them, it does not have an explicit syntax for sending messages to clinicians. The CELLO code would he embedded in complete guideline representation or other application for use by the CDSS.

13. 12.6 FUTURE WORK • Demand for computer-based CDS will grow • Increasing emphasis on patient safety and prevention of medical errors • Increasing use of electronic health records • Clinical practice guidelines -> CDS systems • Emphasis on interoperability of clinical information systems • Standard for representing decision rules in general • Clinical practice guidelines in particular in a computable format

14. 12.7 CONCLUSION • A decision rule is a representation of deterministic reasoning • Represent a decision rule in a computable format • The sequential execution and explicit flow of control of a procedure • The inferencing mechanism and other control processes is the production rule • HL7 standard Arden Syntax • GELLO is HL7 standard expression language