Social media analytics can considerably contribute to understanding health conditions beyond clinical practice, by capturing patients’ discussions and feelings about their quality of life in relation to disease treatments. In this article, we propose a methodology to support a detailed analysis of the therapeutic experience in patients affected by a specific disease, as it emerges from health forums. As a use case to test the proposed methodology, we analyze the experience of patients affected by hypothyroidism and their reactions to standard therapies. Our approach is based on a data extraction and filtering pipeline, a novel topic detection model named Generative Text Compression with Agglomerative Clustering Summarization (GTCACS), and an in-depth data analytic process. We advance the state of the art on automated detection of adverse drug reactions (ADRs) since, rather than simply detecting and classifying positive or negative reactions to a therapy, we are capable of providing a fine characterization of patients along different dimensions, such as co-morbidities, symptoms, and emotional states.

Supporting Personalized Health Care With Social Media Analytics: An Application to Hypothyroidism / Grani, Giorgio; Lenzi, Andrea; Velardi, Paola. - In: ACM TRANSACTIONS ON COMPUTING FOR HEALTHCARE. - ISSN 2691-1957. - 3:1(2021), pp. 1-28. [10.1145/3468781]

Supporting Personalized Health Care With Social Media Analytics: An Application to Hypothyroidism

Grani, Giorgio;Lenzi, Andrea;Velardi, Paola
2021

Abstract

Social media analytics can considerably contribute to understanding health conditions beyond clinical practice, by capturing patients’ discussions and feelings about their quality of life in relation to disease treatments. In this article, we propose a methodology to support a detailed analysis of the therapeutic experience in patients affected by a specific disease, as it emerges from health forums. As a use case to test the proposed methodology, we analyze the experience of patients affected by hypothyroidism and their reactions to standard therapies. Our approach is based on a data extraction and filtering pipeline, a novel topic detection model named Generative Text Compression with Agglomerative Clustering Summarization (GTCACS), and an in-depth data analytic process. We advance the state of the art on automated detection of adverse drug reactions (ADRs) since, rather than simply detecting and classifying positive or negative reactions to a therapy, we are capable of providing a fine characterization of patients along different dimensions, such as co-morbidities, symptoms, and emotional states.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1587497
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