Name
Title | ||
|---|---|---|
A Constraint Satisfaction Approach to Data-Driven Implementation of Clinical Practice Guidelines |
Abstract | ||
|---|---|---|
Despite significant research efforts, the implementation of computerized clinical practice guidelines (CPG) in practice remains problematic for a number of reasons. In particular most guideline representation models do not deal adequately with incomplete or inconsistent clinical data. We present a constraint satisfaction approach to address such shortcomings by focusing on CPG data rather than CPG representation. We model a CPG as a set of data-driven constraints which are used to generate complete solutions for describing a patient state from incomplete clinical data, where the patient state is confirmed by the user. Inconsistent input data can be temporarily eliminated and final feasible solutions (permitted complete solutions from a CPG) can pinpoint inconsistencies in original input data alongside allowable guideline data. We demonstrate a sample implementation of the approach for a pediatric asthma CPG. |
Year | Venue | Keywords |
|---|---|---|
2008 | AMIA | artificial intelligence,natural language processing,algorithms,subject headings |
Field | DocType | Citations |
Data mining,Computer software,Constraint satisfaction,Data-driven,CpG site,Computer science,Clinical Practice,Healthcare industry,Artificial intelligence,Guideline,Machine learning,Patient state | Conference | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Craig Kuziemsky | 1 | 40 | 5.11 |
| Dympna O'Sullivan | 2 | 45 | 11.29 |
| Wojtek Michalowski | 3 | 266 | 41.48 |
| Szymon Wilk | 4 | 461 | 40.94 |
| Ken Farion | 5 | 106 | 12.61 |