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Table 1 Machine learning models with hyperparameters

From: Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients

ModelHyper-parameters
Logistic Regression-CUIsC = 1, penalty = L1, class_weight = balanced
Logistic Regression-WordsC = 1, penalty = L1, class_weight = balanced
Convolutional Neural Network-CUIsFilters = 1024, Filter Size = 1, Dropout = 0.5, Units = 1024, Learning Rate = 0.0001
Convolutional Neural Network-WordsFilters = 1024, Filter Size = 3, Dropout = 0.25, Units = 128, Learning Rate = 0.0001
Convolutional Neural Network-CharacterFilters = 1024, Filter Size = 11, Dropout = 0.25, Units = 1024, Learning Rate = 0.0001
Deep Averaging Network-CUIsDropout = 0.25, Units in layer 1 = 2048, Units in layer 2 = 512, Learning Rate = 0.001
Deep Averaging Network-WordsDropout = 0.75, Units = 128, Learning Rate = 0.001
Max Pooling Network-CUIsDropout = 0.5, Units = 128, Learning Rate = 0.001
Max Pooling Network-WordsDropout = 0.5, Units = 128, Learning Rate = 0.001
Deep Averaging + Max Pooling Network-CUIsDropout = 0.5, Units = 1024, Learning Rate = 0.001
Deep Averaging + Max Pooling Network-WordsDropout = 0.25, Units = 512, Learning Rate = 0.001
  1. Logistic regression’s C value is inverse of regularization strength, and penalty term that penalizes the loss function using different regularization techniques. Optimizer Adam is selected for all the neural networks. Units are the number of neurons in the dense layer of the neural network