Publicly available code is hosted under our MUSC-TBIC@GitHub.com account. Please see those pages for the latest updates and patches. Also feel free to update us on issues you have found using our software. If you like these tools, help us out by spreading the word.
Ensemble Method Framework
Ensemble methods can provide more flexible and robust integration of IE models. For clinical note de-identification, we applied a diverse set of classifiers using different types of learning algorithms, including RNN, CRF, MIRA, and SVM. To regulate less accurate outputs from individual classifiers, we have created a voting ensemble that effectively generates more accurate predictions. If you're interested in using our ensemble system, you can download a copy from ensemble@GitHub.com.
- Evaluation Tool for Unstructured Data and Extractions Engine
- A Python-based tool to help with scoring and evaluating text extraction performance
- If you're interested in using the ETUDE engine, you can download a copy from etude-engine@GitHub.com.