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Data Mining Techniques in Online Health Communities

Authors:
Cassandra Mikkelson
Cali Sweitzer

Keywords: data mining; online health communities; Patient Sentiment; Sentiment Analysis; Healthcare Data Transformation.

Abstract:
Online health communities are an untapped domain of unlimited data on patient sentiment towards drugs and medical devices that can provide academia and industry an inside scope of in demand research according to patient responses. These communities are often found on social media platforms, such as Facebook and Reddit, where patients who have similar medical histories connect to share their experiences, advice, and support for each other. This review explores how data mining methods, specifically machine learning and Natural Language Processing (NLP), can be applied to analyze large data sets derived from user-generated responses on social media and health databases. Methods discussed include sentiment analysis, clustering algorithms, and text classification models as effective tools to generate new knowledge on patterns within online health discussions. The paper also highlights potential applications of data mining to improve pharmaceutical research, enhance drug monitoring, and identify adverse events in terms of postmarket surveillance for regulatory bodies like the U.S Food and Drug Administration (FDA). Lastly, challenges related to data transformation, cleaning, and privacy concerns are addressed along with proposed augmentations to improve data quality.

Pages: 19 to 22

Copyright: Copyright (c) IARIA, 2025

Publication date: March 9, 2025

Published in: conference

ISBN: 978-1-68558-247-0

Location: Lisbon, Portugal

Dates: from March 9, 2025 to March 13, 2025