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

被引：0
作者：
Grani G. [1 ]
Lenzi A. [2 ]
Velardi P. [2 ]
机构：
[1] Department of Translational and Precision Medicine, Sapienza University of Rome, Viale dell'Universita n. 37 RM, Rome
[2] Department of Computer Science, Sapienza University of Rome, Via Salaria n. 113 RM, Rome
来源：
ACM Transactions on Computing for Healthcare | 2022年 / 3卷 / 01期
关键词：
adverse drug reactions; generative adversarial networks; hypothyroid patients; meta-learning; Personalized health care; social analytics;
D O I：
10.1145/3468781
中图分类号：
学科分类号：
摘要：
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. © 2021 Association for Computing Machinery.
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