1. Figure 1. Examples of e-cigarette discussions in social media
From: Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation
J Am Med Inform Assoc. Published online May 13, doi: /jamia/ocx045
J Am Med Inform Assoc | © The Author Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please

2. Figure 3. The Bi-LSTM RNN architecture
From: Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation
J Am Med Inform Assoc. Published online May 13, doi: /jamia/ocx045
J Am Med Inform Assoc | © The Author Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please

3. Figure 2. Annotation example
From: Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation
J Am Med Inform Assoc. Published online May 13, doi: /jamia/ocx045
J Am Med Inform Assoc | © The Author Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please

4. Figure 4. Word embedding visualization for e-cigarette related entities
From: Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation
J Am Med Inform Assoc. Published online May 13, doi: /jamia/ocx045
J Am Med Inform Assoc | © The Author Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please