Clinical NER and Relation Extraction using Bi-Char-LSTMs and Random Forest Classifiers

Arjun Magge, Matthew Scotch, Graciela Gonzalez-Hernandez
; Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection, PMLR 90:25-30, 2018.

Abstract

Identifying named entities from electronic health record notes and extracting relations between the entities is a crucial task for many applications in clinical and public health informatics. In this work, we present an natural language processing pipeline consisting of a named entity recognizer for identifying 9 medical named entities in clinical notes and a random forests classifier for extracting 7 types of relations between the entities.

Cite this Paper


BibTeX
@InProceedings{pmlr-v90-magge18a, title = {Clinical NER and Relation Extraction using Bi-Char-LSTMs and Random Forest Classifiers}, author = {Arjun Magge and Matthew Scotch and Graciela Gonzalez-Hernandez}, booktitle = {Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection}, pages = {25--30}, year = {2018}, editor = {Feifan Liu and Abhyuday Jagannatha and Hong Yu}, volume = {90}, series = {Proceedings of Machine Learning Research}, month = {04 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v90/magge18a/magge18a.pdf}, url = {http://proceedings.mlr.press/v90/magge18a.html}, abstract = {Identifying named entities from electronic health record notes and extracting relations between the entities is a crucial task for many applications in clinical and public health informatics. In this work, we present an natural language processing pipeline consisting of a named entity recognizer for identifying 9 medical named entities in clinical notes and a random forests classifier for extracting 7 types of relations between the entities.} }
Endnote
%0 Conference Paper %T Clinical NER and Relation Extraction using Bi-Char-LSTMs and Random Forest Classifiers %A Arjun Magge %A Matthew Scotch %A Graciela Gonzalez-Hernandez %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-magge18a %I PMLR %J Proceedings of Machine Learning Research %P 25--30 %U http://proceedings.mlr.press %V 90 %W PMLR %X Identifying named entities from electronic health record notes and extracting relations between the entities is a crucial task for many applications in clinical and public health informatics. In this work, we present an natural language processing pipeline consisting of a named entity recognizer for identifying 9 medical named entities in clinical notes and a random forests classifier for extracting 7 types of relations between the entities.
APA
Magge, A., Scotch, M. & Gonzalez-Hernandez, G.. (2018). Clinical NER and Relation Extraction using Bi-Char-LSTMs and Random Forest Classifiers. Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection, in PMLR 90:25-30

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