Name
Title | ||
|---|---|---|
Identifying Patients at Risk for Aortic Stenosis Through Learning from Multimodal Data. |
Abstract | ||
|---|---|---|
In this paper we present a new method of uncovering patients with aortic valve diseases in large electronic health record systems through learning with multimodal data. The method automatically extracts clinically-relevant valvular disease features from five multimodal sources of information including structured diagnosis, echocardiogram reports, and echocardiogram imaging studies. It combines these partial evidence features in a random forests learning framework to predict patients likely to have the disease. Results of a retrospective clinical study from a 1000 patient dataset are presented that indicate that over 25 % new patients with moderate to severe aortic stenosis can be automatically discovered by our method that were previously missed from the records. |
Year | Venue | Field |
|---|---|---|
2016 | MICCAI | Disease,Computer science,Internal medicine,Valvular disease,Cardiology,Stenosis,Aortic valve,Medical record,Clinical study,Radiology,Random forest,Aortic valve diseases |
DocType | Citations | PageRank |
Conference | 1 | 0.37 |
References | Authors | |
5 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tanveer F. Syeda-Mahmood | 1 | 81 | 20.02 |
| Yanrong Guo | 2 | 190 | 18.11 |
| Mehdi Moradi | 3 | 18 | 3.26 |
| Beymer David | 4 | 420 | 87.32 |
| Rajan Deepta | 5 | 30 | 6.55 |
| Yu Cao | 6 | 31 | 5.70 |
| Yaniv Gur | 7 | 125 | 12.44 |
| Mohammadreza Negahdar | 8 | 14 | 4.55 |