Name
Abstract | ||
|---|---|---|
Data mining methods used a racially diverse sample (n=19,970) of pregnant women and 1,622 variables that were collected in Duke's TMR electronic patient record over a 10-year period. Different statistical and data mining methods were similar when compared using receiver operating characteristic (ROC) curves. Best results found that seven demographic variables yielded .72 and addition, of hundreds of other clinical variables added only .03 to the area under the curve (AUC). Similar results across methods suggest that results were data-driven and not method-dependent, and that demographic variables may offer a small set of parsimonious variables with predictive accuracy in a racially. diverse population. Work to determine relevant variables for improved predictive accuracy is ongoing. |
Year | Venue | Keywords |
|---|---|---|
2000 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | risk assessment,artificial intelligence,area under curve,roc curve |
DocType | Issue | ISSN |
Conference | SUPnan | 1067-5027 |
Citations | PageRank | References |
1 | 0.38 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linda K. Goodwin | 1 | 136 | 18.32 |
| Sean Maher | 2 | 5 | 0.93 |
| Lucila Ohno-Machado | 3 | 1426 | 187.95 |
| M A Iannacchione | 4 | 1 | 0.72 |
| P Crockett | 5 | 1 | 0.72 |
| Stephan Dreiseitl | 6 | 338 | 34.80 |
| Staal Vinterbo | 7 | 361 | 32.66 |
| W Hammond | 8 | 1 | 0.38 |