Name
Abstract | ||
|---|---|---|
Clinical Decision Support Systems embed data-driven decision models designed to represent clinical acumen of an experienced physician. We argue that eliminating physicians' diagnostic biases from data improves the overall quality of concepts, which we represent as decision rules. Experiments conducted on prospectively collected clinical data show that analyzing this filtered data produces rules with better coverage, certainty and confirmation. Cross-validation testing shows improvement in classification performance. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13529-3_23 | RSCTC |
Keywords | Field | DocType |
decision rule,clinical acumen,clinical decision support,clinical data,automatic generation,data-driven decision model,experienced physician,cross-validation testing,diagnostic bias,better coverage,filtered data,classification performance,cross validation,clinical decision support system | Decision analysis,Decision rule,Decision tree,Certainty,Computer science,Knowledge management,Decision model,Clinical decision support system,Evidential reasoning approach | Conference |
Volume | ISSN | ISBN |
6086 | 0302-9743 | 3-642-13528-5 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| William Klement | 1 | 21 | 2.90 |
| Szymon Wilk | 2 | 461 | 40.94 |
| Martin Michalowski | 3 | 155 | 15.03 |
| Ken Farion | 4 | 106 | 12.61 |