Name
Abstract | ||
|---|---|---|
To provide quick diagnostic insights to medical practitioners into echocardiograms by only analyzing the echocardiogram workflows (defined as the sequence of modalities examined).We define a dictionary of workflows, called subflows, which are commonly encountered in echocardiography workflows but are mutually exclusive. We represent each workflow as a mixture of dictionary subflows and learn discriminative models for various cardiac diseases using Support Vector Machines. Using these discriminative models, we can predict occurrences of diseases for any, yet unseen, echocardiogram workflow.Working with a corpus of 2300 echocardiograms workflows, we build a dictionary of 172 subflows. Using the associated reports (expert created) we identify the ground-truth diagnoses. We then build discriminative models for 7 different cardiac diseases. Using just the workflow as input, these models can predict diseases on average with over 75% accuracy.Mining collection of echocardiography workflows, for the first time, we are able to predict diseases without even looking at the image contents. |
Year | Venue | Keywords |
|---|---|---|
2013 | AMIA | data mining,workflow,diagnosis |
Field | DocType | Volume |
Data science,Modalities,Disease,Artificial intelligence,Natural language processing,Workflow,Medicine,Discriminative model | Conference | 2013 |
ISSN | Citations | PageRank |
1942-597X | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ritwik Kumar | 1 | 86 | 8.23 |
| Tanveer Fathima Syeda-Mahmood | 2 | 447 | 84.69 |
| Beymer David | 3 | 420 | 87.32 |
| Colin B. Compas | 4 | 54 | 7.02 |
| Karen Brannon | 5 | 7 | 1.69 |