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I use the data extraction process to get the MIMIC-Ⅲ dataset which contains 53,211 records. I use the given code to split the train, valid and test set. And I use the hyperparameters given to run mTAN-full, but I do not achieve 0.8544 AUROC on MIMIC-Ⅲ dataset . The highest is AUROC ~0.838.
This is achieved with this command:
python3 tan_classification.py --alpha 5 --niters 300 --lr 0.0001 --batch-size 128 --rec-hidden 256 --gen-hidden 50 --latent-dim 128 --enc mtan_rnn --dec mtan_rnn --save 1 --classif --norm --learn-emb --k-iwae 1 --dataset mimiciii
I use the data extraction process to get the MIMIC-Ⅲ dataset which contains 53,211 records. I use the given code to split the train, valid and test set. And I use the hyperparameters given to run mTAN-full, but I do not achieve 0.8544 AUROC on MIMIC-Ⅲ dataset . The highest is AUROC ~0.838.
This is achieved with this command:
python3 tan_classification.py --alpha 5 --niters 300 --lr 0.0001 --batch-size 128 --rec-hidden 256 --gen-hidden 50 --latent-dim 128 --enc mtan_rnn --dec mtan_rnn --save 1 --classif --norm --learn-emb --k-iwae 1 --dataset mimiciii
Classification Task on MIMIC-III Dataset (mTAND-Full).log
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