ALTRUIST:利用社交媒体数据模拟虚拟数字队列研究的 Python 软件包

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Charline Bour;Abir Elbeji;Luigi De Giovanni;Adrian Ahne;Guy Fagherazzi
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引用次数: 0

摘要

流行病学队列研究在确定参与者中各种结果的风险因素方面发挥着至关重要的作用。由于招募和长期随访,这些研究往往耗时费钱。社交媒体(SM)数据已成为数字流行病学和健康研究的重要补充来源,因为患者在线社区会定期分享有关他们疾病的信息。与传统的临床问卷调查不同,社交媒体提供了有关患者疾病负担的非结构化但有洞察力的信息。然而,将 SM 数据作为前瞻性队列进行分析的指导却很有限。我们提出了虚拟数字队列研究(VDCS)的概念,作为利用 SM 数据复制队列研究的一种方法。在本文中,我们介绍了 ALTRUIST,这是一个开源 Python 软件包,可在 SM 上标准化生成 VDCS。ALTRUIST 简化了模拟传统队列研究的数据收集、预处理和分析步骤。我们提供了一个以糖尿病为重点的实用案例来说明该方法。通过利用 SM 数据,我们展示了 VDCS 作为解决特定研究问题的重要工具的潜力。ALTRUIST 是可定制的,可应用于各种患者在线社区的数据,是对传统流行病学方法的补充,促进了破坏性最小的健康研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ALTRUIST: A Python Package to Emulate a Virtual Digital Cohort Study Using Social Media Data
Epidemiological cohort studies play a crucial role in identifying risk factors for various outcomes among participants. These studies are often time-consuming and costly due to recruitment and long-term follow-up. Social media (SM) data has emerged as a valuable complementary source for digital epidemiology and health research, as online communities of patients regularly share information about their illnesses. Unlike traditional clinical questionnaires, SM offer unstructured but insightful information about patients’ disease burden. Yet, there is limited guidance on analyzing SM data as a prospective cohort. We presented the concept of virtual digital cohort studies (VDCS) as an approach to replicate cohort studies using SM data. In this paper, we introduce ALTRUIST, an open-source Python package enabling standardized generation of VDCS on SM. ALTRUIST facilitates data collection, preprocessing, and analysis steps that mimic a traditional cohort study. We provide a practical use case focusing on diabetes to illustrate the methodology. By leveraging SM data, which offers large-scale and cost-effective information on users’ health, we demonstrate the potential of VDCS as an essential tool for specific research questions. ALTRUIST is customizable and can be applied to data from various online communities of patients, complementing traditional epidemiological methods and promoting minimally disruptive health research.
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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