Name
Abstract | ||
|---|---|---|
Existing Clinical Decision Support Systems (CDSSs) typically rely on rule-based algorithms and focus on tasks like guidelines adherence and drug prescribing and monitoring. However, the increasing dominance of Electronic Health Record technologies and personalized medicine suggest great potential for prognostic data-driven CDSS. A major goal for such systems would be to accurately predict the outcome of patients' candidate treatments by statistical analysis of the clinical data stored at a Health Care Organization. We formally define the concepts involved in the development of such a system, highlight an inherent difficulty arising from bias in treatment allocation, and propose a general strategy to address this difficulty. Experiments over hypertension clinical data demonstrate the validity of our approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.3233/978-1-60750-806-9-140 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Clinical Decision Support,Data Driven,Machine Learning,Prognostic | Data-driven,Knowledge management,Clinical decision support system,Medicine | Conference |
Volume | ISSN | Citations |
169 | 0926-9630 | 3 |
PageRank | References | Authors |
0.46 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruty Rinott | 1 | 87 | 6.25 |
| Boaz Carmeli | 2 | 41 | 6.70 |
| Carmel Kent | 3 | 10 | 4.95 |
| Daphna Landau | 4 | 3 | 0.46 |
| Yonatan Maman | 5 | 11 | 2.07 |
| Yoav Rubin | 6 | 6 | 1.62 |
| Noam Slonim | 7 | 735 | 113.04 |