Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
New Regularized Algorithms for Transductive Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
| Hi-index | 0.00 |
Medical imaging modalities, such as computed tomography (CT), have a large number of parameters that must be correctly set to produce a diagnostic image. In current clinical practice this is done with input from a radiologist, relying on the patient history provided in textual form by the referring physician. Since the set of parameters is so extensive, radiologists choose from a limited number of protocols, each of which is suited to a group of diseases. We propose a machine learning approach automate to this process, relying on the free-form textual input provided by the referring physician. We exploit an ontology built by the National Library of Medicine to provide domain expertise, as well as an associated parser that maps free-form text into this ontology. We use a graph-based semi-supervised learning technique, where the nodes of the graph are concepts from the ontology and the labels are protocols. The semi-supervised learning approach is motivated by the easy availability of unlabeled training data, for example from patient histories where the protocol is unknown, or even patient histories who did not receive imaging. In our initial experiments we used the adsorption algorithm of [2], running on a database of 1,000 patients who were assigned a protocol by radiologists at New York Presbyterian Hospital. On a stratified sample from our dataset we predicted the protocol selected by a radiologist 60% of the time, compared with a baseline accuracy of 20% achieved by always predicting the most popular protocol. Our results suggest that modern machine learning and NLP techniques show considerable promise for solving this important clinical problem.