scholarly journals Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach (Preprint)

2017 ◽  
Author(s):  
Danushka Bollegala ◽  
Simon Maskell ◽  
Richard Sloane ◽  
Joanna Hajne ◽  
Munir Pirmohamed
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Adverse Drug Reactions ◽  
Drug Reactions ◽  
Social Media Text

scholarly journals Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach

2018 ◽  
Vol 4 (2) ◽  
pp. e51 ◽  
Author(s):  
Danushka Bollegala ◽  
Simon Maskell ◽  
Richard Sloane ◽  
Joanna Hajne ◽  
Munir Pirmohamed
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Adverse Drug Reactions ◽  
Drug Reactions ◽  
Social Media Text

scholarly journals Normalization of Long-tail Adverse Drug Reactions in Social Media

2020 ◽  
Author(s):  
Emmanouil Manousogiannis ◽  
Sepideh Mesbah ◽  
Alessandro Bozzon ◽  
Robert-Jan Sips ◽  
Zoltan Szlanik ◽  
...  
Keyword(s):  
Social Media ◽  
Adverse Drug Reactions ◽  
Drug Reactions ◽  
Long Tail

scholarly journals CyberCan: A New Dictionary for Cantonese Social Media Text Segmentation

2021 ◽  
Author(s):  
Fei Shen ◽  
Wenting Yu ◽  
Chen Min ◽  
Qianying Ye ◽  
Chuanli Xia ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Word Segmentation ◽  
Unstructured Data ◽  
Text Segmentation ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Text Data ◽  
Social Media Text

Text mining has been a dominant approach to extracting useful information from massive unstructured data online. But existing tools for Chinese word segmentation are not ideal for processing social media text data in Cantonese. This project developed CyberCan (https://github.com/shenfei1010/CyberCan), a lexicon of contemporary Cantonese based on more than 100 million pieces of internet texts. We compared the performance of CyberCan with existing Mandarin and Cantonese lexicons in terms of their word segmentation performance. Findings suggest that CyberCan outperforms all existing lexicons by a considerable margin.

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

2020 ◽  
Vol 34 (5) ◽  
pp. 826-844 ◽  
Author(s):  
Louis Tay ◽  
Sang Eun Woo ◽  
Louis Hickman ◽  
Rachel M. Saef
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Text Mining ◽  
Personality Assessment ◽  
Ground Truth ◽  
Psychometric Validation ◽  
Learning Approaches ◽  
Text Data ◽  
Personality Psychology ◽  
Social Media Text

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology

The role of advanced technologies supplemented with traditional methods in pharmacovigilance sciences

2020 ◽  
Vol 14 ◽  
Author(s):  
M Vijaya Satwika Naidu ◽  
Dudala Sai Sushma ◽  
Varun Jaiswal ◽  
S. Asha ◽  
Tarun Pal
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Adverse Drug Reactions ◽  
Mobile Apps ◽  
Health Sector ◽  
Early Developmental Stage ◽  
Drug Reactions ◽  
Traditional Methods ◽  
Advanced Technologies

Background: The immediate automatic systemic monitoring and reporting of adverse drug reaction, improving the efficacy is the utmost need of medical informatics community. The venturing of advanced digital technologies into the health sector has opened new avenues for rapid monitoring. In recent years, data shared through social media, mobile apps and on other social websites has increased manifolds requiring data mining techniques. Objective: The objective of this report is to highlight the role of advanced technologies together with traditional methods to proactively aid in early detection of adverse drug reactions concerned with drug safety and pharmacovigilance. Methods: A thorough search was conducted for papers and patents regarding pharmacivigilance. All articles with respect to relevant subject were explored and mined from public repositories such as Pubmed, Google Scholar, Springer, ScienceDirect (Elsevier), Web of Science, etc. Results: The European Union’s Innovative Medicines Initiative WEB-RADR project emphasized the development of mobile applications and social media data for reporting adverse effects. Only relevant data has to be captured through the data mining algorithms (DMAs) playing an important role in timely prediction of risk with high accuracy using two popular approaches the frequentist and Bayesian approach. The pharmacovigilance at premarketing stage is useful for the prediction of the adverse drug reactions in early developmental stage of a drug. Later postmarketing safety reports and clinical data reports are important to be monitored through electronic health records, prescription-event monitoring, spontaneous reporting databases, etc approaches. Conclusion: The advanced technologies supplemented with traditional technologies is the need of hour for evaluating product’s risk profile and reducing risk in population esp. with comorbid conditions and on concomitant medications.

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