A supervised classification model for concept normalization of adversed drug reaction (ADR) mentions. Given a concept mention (without contextual information), this model predicts the corresponding MEDDRA Preferred Term (PT) code.
The deep architecture is hierarchical such that a forward character-level LSTM composes word representations. These word representations are then composed via a bi-directional LSTM to form the phrase (corresponding to the mention). The model is designed such that there are as many softmax output units as there are unique MEDDRA codes appearing in the training set.
Packages required:
numpy
sklearn
pandas
tensorflow 1.0.0 with tensorflow-fold
Format of the training data:
ID <-tab-> Text <-tab-> MEDDRA PT
Format of the unlabeled testing data:
ID <-tab-> Text
The model is trained using annotated data located the data_train directory. The program will train 10 such models as part of an ensemble and store them in a local tmp directory. The program will then annotate unlabeled instances from data_test and print corresponding results to a new file named test_hiercharlstm.txt.
To execute the file, simply run:
python main.py
See the sample_output.txt file for the output of this model on this particular dataset.
The output format is:
ID <-tab-> MEDDRA PT
For example:
44675 10041349
40103 10000125
41585 10016322
41834 10019211
46301 10061920
41783 10019133
41184 10016322
45250 10047700
45856 10048010
45048 10044565
40652 10011469
...
To evaluate the test predictions, you must have the official test labels which are not provided in this repository.
For info on the related shared task, please refer to: https://healthlanguageprocessing.org/sharedtask2/
Please consider citing the following paper(s) if you use this software in your work:
Sifei Han, Tung Tran, Anthony Rios, and Ramakanth Kavuluru. "Team UKNLP: Detecting ADRs, Classifying Medication Intake Messages, and Normalizing ADR Mentions on Twitter" In Proceedings of the 2nd Social Media Mining for Health Research and Applications Workshop co-located with the American Medical Informatics Association Annual Symposium (AMIA 2017), vol-1996, pp. 49-53. 2017.
@inproceedings{han2017team,
title={Team UKNLP: Detecting ADRs, Classifying Medication Intake Messages, and Normalizing ADR Mentions on Twitter},
author={Han, Sifei and Tran, Tung and Rios, Anthony and Kavuluru, Ramakanth},
booktitle={Social Media Mining for Health Research and Applications},
pages={49--53},
year={2017},
organization={AMIA}
}
Tung Tran
tung.tran [at] uky.edu
http://tttran.net/