The class imbalance problem detecting adverse drug reactions in electronic health records
S Santiso, A Casillas, A Pérez - Health informatics journal, 2019 - journals.sagepub.com
This work focuses on adverse drug reaction extraction tackling the class imbalance problem.
Adverse drug reactions are infrequent events in electronic health records, nevertheless, it is …
Adverse drug reactions are infrequent events in electronic health records, nevertheless, it is …
Learning to extract adverse drug reaction events from electronic health records in Spanish
A Casillas, A Pérez, M Oronoz, K Gojenola… - Expert Systems with …, 2016 - Elsevier
Objective: To tackle the extraction of adverse drug reaction events in electronic health
records. The challenge stands in inferring a robust prediction model from highly unbalanced …
records. The challenge stands in inferring a robust prediction model from highly unbalanced …
Text and data mining techniques in adverse drug reaction detection
We review data mining and related computer science techniques that have been studied in
the area of drug safety to identify signals of adverse drug reactions from different data …
the area of drug safety to identify signals of adverse drug reactions from different data …
Adverse Drug Reaction extraction: Tolerance to entity recognition errors and sub-domain variants
S Santiso, A Pérez, A Casillas - Computer Methods and Programs in …, 2021 - Elsevier
Background and Objective: This work tackles the Adverse Drug Reaction (ADR) extraction in
Electronic Health Records (EHRs) written in Spanish. This task is within the framework of …
Electronic Health Records (EHRs) written in Spanish. This task is within the framework of …
Explainable detection of adverse drug reaction with imbalanced data distribution
Analysis of health-related texts can be used to detect adverse drug reactions (ADR). The
greatest challenge for ADR detection lies in imbalanced data distributions where words …
greatest challenge for ADR detection lies in imbalanced data distributions where words …
[HTML][HTML] Portable automatic text classification for adverse drug reaction detection via multi-corpus training
A Sarker, G Gonzalez - Journal of biomedical informatics, 2015 - Elsevier
Objective Automatic detection of adverse drug reaction (ADR) mentions from text has
recently received significant interest in pharmacovigilance research. Current research …
recently received significant interest in pharmacovigilance research. Current research …
Classifying adverse drug reactions from imbalanced twitter data
Background Nowadays, social media are often being used by general public to create and
share public messages related to their health. With the global increase in social media …
share public messages related to their health. With the global increase in social media …
Discovering novel adverse drug events using natural language processing and mining of the electronic health record
C Friedman - Artificial Intelligence in Medicine: 12th Conference on …, 2009 - Springer
Discovering Novel Adverse Drug Events Using Natural Language Processing and Mining of
the Electronic Health Record Page 1 C. Combi, Y. Shahar, and A. Abu-Hanna (Eds.): AIME …
the Electronic Health Record Page 1 C. Combi, Y. Shahar, and A. Abu-Hanna (Eds.): AIME …
[PDF][PDF] Detecting signals in noisy data-can ensemble classifiers help identify adverse drug reaction in tweets
M Rastegar-Mojarad, RK Elayavilli… - Proceedings of the …, 2016 - researchgate.net
In this paper, we describe our system for detecting adverse reactions from tweets, a task
organized as part of Pacific Symposium of Biocomputing (PSB)-Social Media Mining Shared …
organized as part of Pacific Symposium of Biocomputing (PSB)-Social Media Mining Shared …
Detecting adverse drug reaction with data mining and predicting its severity with machine learning
Adverse Drug Reaction (ADR) is one of the many uncertainties that are considered a fatal
threat to the pharmacy industry and the field of medical diagnosis. Utmost care is taken to …
threat to the pharmacy industry and the field of medical diagnosis. Utmost care is taken to …