Named Entity Recognition using Neural Networks for Clinical Notes

Edson Florez, Frederic Precioso, Michel Riveill, Romaric Pighetti
Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection, PMLR 90:7-15, 2018.

Abstract

Currently, the best performance for Named Entity Recognition in medical notes is obtained by systems based on neural networks. These supervised systems require precise features in order to learn well fitted models from training data, for the purpose of recognizing medical entities like medication and Adverse Drug Events (ADE). Because it is an important issue before training the neural network, we focus our work on building comprehensive word representations (the input of the neural network), using character-based word representations and word representations. The proposed representation improves the performance of the baseline LSTM. However, it does not reach the performances of the top performing contenders in the challenge for detecting medical entities from clinical notes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v90-florez18a, title = {Named Entity Recognition using Neural Networks for Clinical Notes}, author = {Florez, Edson and Precioso, Frederic and Riveill, Michel and Pighetti, Romaric}, booktitle = {Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection}, pages = {7--15}, year = {2018}, editor = {Liu, Feifan and Jagannatha, Abhyuday and Yu, Hong}, volume = {90}, series = {Proceedings of Machine Learning Research}, month = {04 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v90/florez18a/florez18a.pdf}, url = {https://proceedings.mlr.press/v90/florez18a.html}, abstract = {Currently, the best performance for Named Entity Recognition in medical notes is obtained by systems based on neural networks. These supervised systems require precise features in order to learn well fitted models from training data, for the purpose of recognizing medical entities like medication and Adverse Drug Events (ADE). Because it is an important issue before training the neural network, we focus our work on building comprehensive word representations (the input of the neural network), using character-based word representations and word representations. The proposed representation improves the performance of the baseline LSTM. However, it does not reach the performances of the top performing contenders in the challenge for detecting medical entities from clinical notes.} }
Endnote
%0 Conference Paper %T Named Entity Recognition using Neural Networks for Clinical Notes %A Edson Florez %A Frederic Precioso %A Michel Riveill %A Romaric Pighetti %B Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection %C Proceedings of Machine Learning Research %D 2018 %E Feifan Liu %E Abhyuday Jagannatha %E Hong Yu %F pmlr-v90-florez18a %I PMLR %P 7--15 %U https://proceedings.mlr.press/v90/florez18a.html %V 90 %X Currently, the best performance for Named Entity Recognition in medical notes is obtained by systems based on neural networks. These supervised systems require precise features in order to learn well fitted models from training data, for the purpose of recognizing medical entities like medication and Adverse Drug Events (ADE). Because it is an important issue before training the neural network, we focus our work on building comprehensive word representations (the input of the neural network), using character-based word representations and word representations. The proposed representation improves the performance of the baseline LSTM. However, it does not reach the performances of the top performing contenders in the challenge for detecting medical entities from clinical notes.
APA
Florez, E., Precioso, F., Riveill, M. & Pighetti, R.. (2018). Named Entity Recognition using Neural Networks for Clinical Notes. Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection, in Proceedings of Machine Learning Research 90:7-15 Available from https://proceedings.mlr.press/v90/florez18a.html.

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