III: Small: Collaborative Research: Social Media Based Analysis of Adverse Drug Events: User Modeling, Signal Reliability, and Signal Validation

III：小：协作研究：基于社交媒体的药物不良事件分析：用户建模、信号可靠性和信号验证

• 批准号: 2039915
• 负责人: Ahmed Abbasi
• 金额: $ 15.61万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-25 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039915&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Social

Adverse drug reactions (ADRs) have been associated with significant morbidity and mortality, and have been a significant cause of hospital admissions, accounting for as much as 5% of all admissions. About 2,000,000 serious ADRs are reported yearly in the US; 100,000 annual deaths are related to adverse drug events; serious ADRs rank 4th to 6th as causes of death. The problem stems from the fact that the ADR profile of a given drug is rarely complete at the time of official approval. The typically limited preapproval evaluation often results in the possibility that when the drug is finally approved for use in the general population (with significant diversity in race, gender, age, lifestyle), some previously unidentified ADRs are often observed. This problem is acute for psychotropic medications, given the fact that most people with psychiatric diseases tend to have other health issues, with the individual taking multiple drugs at the same time (both psychotropic and non-psychotropic), with often unknown interactions between them. Initial results have shown the promise of using social-media data for ADR signal detection. However, these methods are still faced with two critical challenges, namely, signal reliability and biological validation. Thus, this project proposes a detailed study on key determinants of signal reliability: credibility of social media sources, model of the users that generate source content, signal generation from such sources, and validation of the generated signals. This work will be relevant to government agencies charged with drug approval, drug monitoring, and disease monitoring, drug companies, hospitals, and the general public. The impact of the proposed work will go beyond drug surveillance, since the approaches proposed can be adapted for other healthcare problems, and for other scenarios, such as financial markets, and national security. Planned educational activities include outreach to high-school students, and involvement of undergraduate and graduate students. Research results will be disseminated via technical publications in professional journals and conference presentations. The project has three specific aims: (1) Enrich signal reliability in social media analysis of adverse drug events, using credibility analysis, user modeling and signal fusion via deep learning; (2) Signal validation via molecular level analysis; (3) Prototype development and evaluation. The ubiquity, veracity and diversity of data from various social media channels and other sources of user-generated content necessitate a serious consideration of their credibility, recency, uniqueness and salience. To enrich signal reliability, the team will propose novel methods for ADR signal detection using credibility analysis, and for user modeling and signal fusion based on deep leaning techniques. For signal validation, biological support for hypothesized ADRs, essentially connecting high-level observations from social media interactions to potential associations at molecular level networks and pathways, will be used. The results will change the current largely passive approach to post-marketing drug surveillance that relies heavily on voluntary reports, by ensuring reliability in social-media based approaches, thus making the public an integral part of a proactive drug surveillance system. The idea of signal fusion and deep learning for user modeling and signal generation can be extended for other uses beyond drug surveillance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

药物不良反应（adr）已与显著的发病率和死亡率相关，并已成为住院的一个重要原因，占所有住院的5%。在美国，每年报告的严重不良反应约为200万例；每年有10万例死亡与药物不良事件有关；严重的不良反应排在死亡原因的第四位到第六位。问题的根源在于，在正式批准时，特定药物的不良反应概况很少完整。通常有限的预批准评估通常会导致当药物最终被批准用于普通人群（在种族、性别、年龄、生活方式上存在显著差异）时，经常会观察到一些以前未发现的不良反应。这个问题对于精神药物来说是严重的，因为大多数患有精神疾病的人往往有其他健康问题，个人同时服用多种药物（包括精神药物和非精神药物），它们之间的相互作用往往是未知的。初步结果显示，利用社交媒体数据检测不良反应信号是有希望的。然而，这些方法仍然面临着两个关键的挑战，即信号可靠性和生物学验证。因此，本项目提出了对信号可靠性关键决定因素的详细研究：社交媒体来源的可信度、生成源内容的用户模型、这些来源产生的信号以及产生的信号的验证。这项工作将涉及到负责药物审批、药物监测和疾病监测的政府机构、制药公司、医院和公众。拟议工作的影响将超越药物监测，因为拟议的方法可以适用于其他卫生保健问题，以及金融市场和国家安全等其他情况。计划的教育活动包括向高中生推广，以及本科生和研究生的参与。研究成果将通过专业期刊的技术出版物和会议发言来传播。该项目有三个具体目标：(1)通过可信度分析、用户建模和深度学习的信号融合，丰富社交媒体分析药物不良事件的信号可靠性；(2)分子水平分析信号验证；(3)原型开发与评价。来自各种社交媒体渠道和其他用户生成内容来源的数据无处不在、真实性和多样性，需要认真考虑它们的可信度、近代性、独特性和显著性。为了提高信号的可靠性，该团队将提出使用可信度分析进行ADR信号检测的新方法，以及基于深度学习技术的用户建模和信号融合方法。对于信号验证，将使用假设adr的生物学支持，本质上是将社交媒体互动的高级观察与分子水平网络和途径的潜在关联联系起来。通过确保基于社交媒体的方法的可靠性，研究结果将改变目前很大程度上被动的上市后药物监测方法，这种方法严重依赖于自愿报告，从而使公众成为主动药物监测系统的组成部分。用于用户建模和信号生成的信号融合和深度学习的想法可以扩展到药物监测以外的其他用途。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Examining User Heterogeneity in Digital Experiments

• DOI: 10.1145/3578931
• 发表时间: 2023-01
• 期刊: ACM Transactions on Information Systems
• 影响因子: 5.6
• 作者: S. Somanchi;A. Abbasi;Ken Kelley;David G. Dobolyi;T. Yuan

Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality

• DOI: 10.1287/isre.2022.1111
• 发表时间: 2022-03
• 期刊: Inf. Syst. Res.
• 作者: Kai Yang;Raymond Y. K. Lau;A. Abbasi

Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts

• DOI: 10.18653/v1/2022.acl-long.72
• 发表时间: 2022
• 作者: Yue Guo-;Yi Yang;A. Abbasi

Timely, Granular, and Actionable: Designing a Social Listening Platform for Public Health 3.0

及时、精细且可操作：为公共卫生 3.0 设计社交聆听平台

• 发表时间: 2024
• 期刊: Management information systems quarterly
• 作者: Kitchens, Brent;Claggett, Jennifer;Abbasi, Ahmed
• 通讯作者: Abbasi, Ahmed

Deep Learning for Adverse Event Detection From Web Search

• DOI: 10.1109/tkde.2020.3017786
• 发表时间: 2022-06
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Faizan Ahmad;A. Abbasi;Brent Kitchens;D. Adjeroh;D. Zeng