Implementation and demo of explainable coding of clinical notes with Hierarchical Label-wise Attention Networks (HLAN) - acadTags/Explainable-Automated-Medical-Coding
LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM.
Main features of LIBLINEAR include
* Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
* Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
* Cross validation for model selection
* Probability estimates (logistic regression only)
* Weights for unbalanced data
* MATLAB/Octave, Java interfaces
H. Dong, V. Suárez-Paniagua, W. Whiteley, and H. Wu. (2020)cite arxiv:2010.15728Comment: Structured abstract in full text, 17 pages, 5 figures, 4 supplementary materials (3 extra pages), submitted to Journal of Biomedical Informatics.
J. Wehrmann, R. Cerri, and R. Barros. Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, page 5075--5084. Stockholmsmässan, Stockholm Sweden, PMLR, (10--15 Jul 2018)
O. Gharroudi, H. Elghazel, and A. Aussem. Advances in Artificial Intelligence, volume 8436 of Lecture Notes in Computer Science, page 95-106. Springer, (2014)