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28:39Z","updated_at":"2019-07-25T03:45:32Z","closed_at":null,"author_association":"COLLABORATOR","body":"Goals : \r\n1. Produce an LSTM based classification model \r\n\r\n**Target definition** :\r\n    if sum(criterion_collumn)>=1 then criterion_labelled\r\n\r\nMajor blocker at the moment :\r\n      - Availability training the data, architecture setup. "},"comment":{"url":"https://api.github.com/repos/omdena/ptsd/issues/comments/514883804","html_url":"https://github.com/omdena/ptsd/issues/11#issuecomment-514883804","issue_url":"https://api.github.com/repos/omdena/ptsd/issues/11","id":514883804,"node_id":"MDEyOklzc3VlQ29tbWVudDUxNDg4MzgwNA==","user":{"login":"sylvia31096","id":22942860,"node_id":"MDQ6VXNlcjIyOTQyODYw","avatar_url":"https://avatars3.githubusercontent.com/u/22942860?v=4","gravatar_id":"","url":"https://api.github.com/users/sylvia31096","html_url":"https://github.com/sylvia31096","followers_url":"https://api.github.com/users/sylvia31096/followers","following_url":"https://api.github.com/users/sylvia31096/following{/other_user}","gists_url":"https://api.github.com/users/sylvia31096/gists{/gist_id}","starred_url":"https://api.github.com/users/sylvia31096/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sylvia31096/subscriptions","organizations_url":"https://api.github.com/users/sylvia31096/orgs","repos_url":"https://api.github.com/users/sylvia31096/repos","events_url":"https://api.github.com/users/sylvia31096/events{/privacy}","received_events_url":"https://api.github.com/users/sylvia31096/received_events","type":"User","site_admin":false},"created_at":"2019-07-25T03:45:32Z","updated_at":"2019-07-25T03:45:32Z","author_association":"NONE","body":"Owing to the fact we have a small dataset. I had a very crazy idea but I'm thinking right now we have to try anything. \r\nSo I was thinking, what if we trained an LSTM with the descriptions of each of symptom/criterion (because what I was working on was A1 let's have that in mind) and then predict on the dataset from Colton as test and validation.\r\n\r\nWhen I say description I mean, like for A1 the criterion is exposure to traumatic events, so basically a description of a traumatic event. We could scrape the web for such data and get hundreds of rows (would be tedious but possible). We would obviously need a psychologist or an expert in the field to guide us with what we should be looking at as we collect the data.\r\n\r\nI created a colab notebook to just sort of demonstrate this. I built an LSTM model with a pretrained BERT model. \r\n\r\nIt's predictions are not perfect but it could be due to the fact the dataset is extremely small, hyperparameters have not been tuned, the idea is downright insane or all the above.\r\n\r\nIn the notebook my created \"dataset of symptoms\" is not exactly how I would want it to be so I will work on that. And my code is not well commented so apologies for that also.\r\n\r\nHere is the link to my notebook:\r\nhttps://colab.research.google.com/drive/1vwbUlcZy9-3Nou3Jp0w2sGsI7gnbi5PL\r\n\r\n"}}
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