Knowledge-based Feature Engineering for Detecting Medication and Adverse Drug Events from Electronic Health Records

Duy-Hoa Ngo, Alejandro Metke-Jimenez, Anthony Nguyen
Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection, PMLR 90:31-38, 2018.

Abstract

This paper presents a Conditional Random Field (CRF) learning model integrated with a series of knowledge-based feature engineering functions for detecting medication and adverse drug events from electronic health records (EHRs). Our experimental evaluation shows high performance in terms of the F-score measure (83.4% and 92.5% respectively for strict and relax modes) in the detection of medication and clinical finding named entities. It also shows promising results (overall 78.8% F-score) in the recognition of detailed properties of medication use as well as different types of clinical findings mentioned in electronic health records.

Cite this Paper

BibTeX
@InProceedings{pmlr-v90-ngo18a, title = {Knowledge-based Feature Engineering for Detecting Medication and Adverse Drug Events from Electronic Health Records}, author = {Ngo, Duy-Hoa and Metke-Jimenez, Alejandro and Nguyen, Anthony}, booktitle = {Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection}, pages = {31--38}, 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/ngo18a/ngo18a.pdf}, url = {https://proceedings.mlr.press/v90/ngo18a.html}, abstract = {This paper presents a Conditional Random Field (CRF) learning model integrated with a series of knowledge-based feature engineering functions for detecting medication and adverse drug events from electronic health records (EHRs). Our experimental evaluation shows high performance in terms of the F-score measure (83.4% and 92.5% respectively for strict and relax modes) in the detection of medication and clinical finding named entities. It also shows promising results (overall 78.8% F-score) in the recognition of detailed properties of medication use as well as different types of clinical findings mentioned in electronic health records.} }
Endnote
%0 Conference Paper %T Knowledge-based Feature Engineering for Detecting Medication and Adverse Drug Events from Electronic Health Records %A Duy-Hoa Ngo %A Alejandro Metke-Jimenez %A Anthony Nguyen %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-ngo18a %I PMLR %P 31--38 %U https://proceedings.mlr.press/v90/ngo18a.html %V 90 %X This paper presents a Conditional Random Field (CRF) learning model integrated with a series of knowledge-based feature engineering functions for detecting medication and adverse drug events from electronic health records (EHRs). Our experimental evaluation shows high performance in terms of the F-score measure (83.4% and 92.5% respectively for strict and relax modes) in the detection of medication and clinical finding named entities. It also shows promising results (overall 78.8% F-score) in the recognition of detailed properties of medication use as well as different types of clinical findings mentioned in electronic health records.
APA
Ngo, D., Metke-Jimenez, A. & Nguyen, A.. (2018). Knowledge-based Feature Engineering for Detecting Medication and Adverse Drug Events from Electronic Health Records. Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection, in Proceedings of Machine Learning Research 90:31-38 Available from https://proceedings.mlr.press/v90/ngo18a.html.

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