AZDrugMiner: An Information Extraction System for Mining Patient-Reported Adverse Drug Events in Online Patient Forums

Liu, Xiao; Chen, Hsinchun

doi:10.1007/978-3-642-39844-5_16

Cited by 75 publications

(79 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some existing work on experience extraction from the Web includes [8], [9]. In [9], the experience is extracted from a MedHelp health forum post by classifying each sentence as personal experience or hearsay.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Context-Aware Experience Extraction from Online Health Forums

Liu

Chen

Tang

et al. 2015

2015 International Conference on Healthcare Informatics

View full text Add to dashboard Cite

Abstract-Online health forums provide a large repository for patients, caregivers, and researchers to seek valuable information. The extraction of patient-reported personal health experience from the forums has many important applications. For example, medical researchers can discover trustable knowledge from the extracted experience. Patients can search for peers with similar experience and connect with them. In this paper, we model the extraction of patient experience as a classification problem: classifying each sentence in a forum post as containing patient experience or not containing patient experience. We propose to exploit the sentence context information for such experience extraction task, and classify the context information into global context and local context. A unified Context-Aware expeRience Extraction (CARE) framework is proposed to incorporate these two types of context information. Our experimental results show that the global context can significantly improve the experience extraction accuracy, while the local context can also improve the performance when less labeled data is available.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In [9], the experience is extracted from a MedHelp health forum post by classifying each sentence as personal experience or hearsay. They used the bag of words (BOW), bigram, and Part of Speech (POS) features extracted from each individual sentence for such classification task.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Experience Extraction from Online Health Forums

Liu

Chen

Tang

et al. 2015

2015 International Conference on Healthcare Informatics

View full text Add to dashboard Cite

show abstract

“…Another area of interest is the surveillance of adverse drug reactions (ADRs) which has been a leading cause of death in the United States [12]. It is estimated that approximately 2 million patients in USA are affected by ADRs and the researchers in [13], [14] and [15] propose an analytical framework for extracting patient-reported adverse drug events from online patient forums such as DailyStrength and PatientsLikeMe.…”

Section: Health Informaticsmentioning

confidence: 99%

Data Reduction and Regression Using Principal Component Analysis in Qualitative Spatial Reasoning and Health Informatics

Sabharwal

Anjum

2016

Polibits

View full text Add to dashboard Cite

Abstract-The central idea of principal component analysis (PCA) is to reduce the dimensionality of a dataset consisting of a large number of interrelated variables, while retaining as much as possible of the variation present in the dataset. In this paper, we use PCA based algorithms in two diverse genres, qualitative spatial reasoning (QSR) to achieve lossless data reduction and health informatics to achieve data reduction along with improved regression analysis respectively. In an adaptive hybrid approach, we have employed PCA to traditional regression algorithms to improve their performance and representation. This yields prediction models that have both a better fit and reduced number of attributes than those produced by using standard logistic regression alone. We present examples using both synthetic data and real health datasets from UCI Repository.

show abstract

“…The Third and Fourth i2b2 Shared Tasks included medication detection from clinical texts (Uzuner et al, 2010;Uzuner et al, 2011), and the Fourth i2b2 Shared Task also included relation classification between treatments (including medications), problems, and tests. Recently, there has been growing interest in extracting medication information from other types of text, such as Twitter, online health forums, and drug review sites (e.g., (Leaman et al, 2010;Bian et al, 2012;Liu and Chen, 2013;Yates and Goharian, 2013;Segura-Bedmar et al, 2014)). Much of this research is geared toward identifying adverse drug events or drug-drug interactions.…”

Section: Related Workmentioning

confidence: 99%

Extracting Information about Medication Use from Veterinary Discussions

Ding

Riloff

2015

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard Cite

Our research aims to extract information about medication use from veterinary discussion forums. We introduce the task of categorizing information about medication use to determine whether a doctor has prescribed medication, changed protocols, observed effects, or stopped use of a medication. First, we create a medication detector for informal veterinary texts and show that features derived from the Web can be very powerful. Second, we create classifiers to categorize each medication mention with respect to six categories. We demonstrate that this task benefits from a rich linguistic feature set, domain-specific semantic features produced by a weakly supervised semantic tagger, and balanced self-training.

show abstract

AZDrugMiner: An Information Extraction System for Mining Patient-Reported Adverse Drug Events in Online Patient Forums

Cited by 75 publications

References 6 publications

Context-Aware Experience Extraction from Online Health Forums

Context-Aware Experience Extraction from Online Health Forums

Data Reduction and Regression Using Principal Component Analysis in Qualitative Spatial Reasoning and Health Informatics

Extracting Information about Medication Use from Veterinary Discussions

Contact Info

Product

Resources

About