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There are two datasets TwADR-L (from Twitter) and AskAPatient (Link: https://zenodo.org/record/55013#.YgU2NN-ZO4T) for medical concept normalization. However, the two datasets have serious data quality problems. Please read the introduction of the datasets and clean the two datasets by following the steps in the statement. In the original dataset, the TwADR-L had 48,057 training, 1,256 validation and 1,427 test examples. The test set (all test samples from 10 folds combined) consists of 765 unique phrases and 273 unique classes (medical concepts). The AskAPatient dataset contained 156,652 training, 7,926 validation, and 8,662 test examples. The entire test set (all test samples from 10 folds combined) consists of 3,749 unique phrases and 1,035 unique classes (medical concepts). The authors randomly split each dataset into ten equal folds, ran 10-fold cross validation and reported the accuracy averaged across the ten folds. We found that, in the original data set, many phrase-label pairs appeared multiple times within the same training data file and also across the training and test data sets in the same fold. In the AskAPatient data set, on average 35.82% of the test data overlapped with training data in the same fold. In the Twitter (TwADR-L) dataset, on average 8.62% of the test set had an overlap with the training data in the same fold. Having a large overlap between the training and the test data can potentially introduce bias in the model and contribute to high accuracy. It is not unlikely that the high model performance reported in the original paper may be triggered by the the large overlap between the training and test sets. Therefore to remove the bias, we further cleaned and recreated the training, validation, and test sets such that each phrase-label pair appears only once in the entire dataset (either in training, validation or test set). (1) First, we combined all examples in training, validation and test data from the original data set and then removed all duplicate phrase-label pairs (examples that have the same phrase and label pair and appear more than once in training/validation/test datasets). Table II shows the statistics of the new dataset (after removing duplicates). The Twitter data set had 3,157 unique phrase-label pairs and 2,220 unique labels (medical concepts) while 173 phrases had multiple labels (i.e., they were assigned to more than one label). Many concepts had only one example, and the concept that had the most number of examples had 36 phrases. On average, each concept had 1.42 examples. The AskAPatient data set had 4,496 unique phrase-label pairs, 1,036 unique labels while 26 phrases had multiple labels. Table III shows examples of phrases that had multiple labels. For example, ‘mad’ can be mapped to ‘anger’ or ‘rage’ and ‘sore’ can be mapped to ‘pain’ or ‘myalgia’. (2) Second, we remove all concepts that had less than five examples. The statistics of the final data are shown in Table IV. (3) Third, we divide all examples without multiple labels into random 10 folds such that each unique phrase-label pair appears once in one of the 10 test sets. We add the pairs with multiple labels into the training data. This final 10-folds dataset is used in all our experiments

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