Clinical Semantic Textual Similarity
- AdaBoost
- BayesianRidgeRegression
- DecisionTreeClassifier
- ensemble
- Extra Trees
- Gradient Boost
- Lasso Regression
- LassoLarsRegression
- LinearRegression
- LogisticRegression NeuralNetSingle
- RandomForestRegression
- SVM_OneVsAllClassifier
- SVM_OneVsOneClassifier
- XGBoostClassifier
- XGBoostRegressor
- Deep Neural Network 1 layer
- Deep Neural Network Multi layer BioSentVec
- Deep Neural Network Multi layer
- Deep Neural Network ReLU
- Biomedical sentence embedding, BioSentVec
- Cosine distance, Euclidean distance, Squared-Euclidean Distance, Correlation and Word-Mover distance
- Token-level similarity
- Jaccard (threshold of 0.7), Q-gram(q=2,3,4), Cosine, Dice, Overlap-based, Tversky Index, Monge-Elkan, Affine, Bag-Distance, TF-IDF, Editex, Levenstein, Needleman-Wunsh and Smith-Waterman similarity both for the given sentence pairs and also for the modified sentences having a common prefix.
- Numerical similarity we converted them into words and evaluate through a 200-dimension BioWordVec5 model.
- Natural language inference-based(NLI) features for the task.
- Clinical concepts similarity using Metamap