Name
Title | ||
|---|---|---|
Finding appropriate clinical trials: evaluating encoded eligibility criteria with incomplete data. |
Abstract | ||
|---|---|---|
We describe our work on creating a system that selects appropriate clinical trials by automating the evaluation of eligibility criteria. We developed a data model of eligibility for breast cancer clinical trials, upon which the criteria were encoded. Standard vocabularies are utilized to represent concepts used in the system, and retrieve their hierarchical relationships. The system incorporates Bayesian networks to handle missing patient information. Protocols are ranked by the belief that the patient is eligible for each of them. In a preliminary evaluation, we found good agreement (kappa 0.86) between the system and an independent physician in selection of protocols, but poor agreement (kappa 0.24) in protocol ranking. We conclude that our approach is feasible, and potentially useful in assisting both physicians and patients in the task of selecting appropriate trials. |
Year | Venue | Keywords |
|---|---|---|
2001 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | expert systems,bayes theorem |
Field | DocType | Issue |
Data mining,Kappa,Breast cancer,Ranking,Expert system,Clinical trial,Bayesian network,Artificial intelligence,Data model,Medicine,Machine learning,Bayes' theorem | Conference | SUPnan |
ISSN | Citations | PageRank |
1067-5027 | 4 | 0.83 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nachman Ash | 1 | 162 | 30.27 |
| O Ogunyemi | 2 | 345 | 44.55 |
| Qing Zeng | 3 | 547 | 67.98 |
| Lucila Ohno-Machado | 4 | 1426 | 187.95 |