Social Media Mining for Pharmacovigilance

项目摘要 在我们的母基金中，总体目标是开发新的NLP方法，以利用社交媒体（SM）数据， 特定的药物警戒（PV）工作受到传统PV方法的已知缺点的阻碍。 我们专注于促进纵向SM数据用于探索的方法（a）影响药物治疗的因素 一般人群中的依从性和持久性（目标1），以及（B） 妊娠期间服用的药物和妊娠结局（目的2）。我们还解决了一个主要的障碍， 基于这些数据的病例对照研究：寻找对照（目标3）。总的来说， 家长赠款是推进NLP方法，使大规模集成SM作为一个内容丰富的 流行病学研究中用户生成的数据来源，包括随时间推移发布的相关数据。我们 寻求扩展为母基金的目标1、2和3开发的方法，以专注于阿尔茨海默病 和阿尔茨海默病相关痴呆（AD/ADRD）。本补编的目标是响应 NOT-AG-20-034，包括以下内容：目标1是开发和评估NLP方法，以识别 并描述了一组受AD/ADRD诊断影响的Twitter用户，特别是 声明他们的直系亲属（父母，祖父母或兄弟姐妹），重要的其他人，甚至自己， 被诊断为AD/ADRD。将进一步挖掘收集的队列中用户的时间线， 人口统计学特征（年龄、性别、种族和居住地理位置）和特定 将开发方法来提取与AD/ADRD患者的家族关系，以及与 两项具体的重点研究：1）关于早发性（也称为年轻发病）和潜在终生因素的研究 反映在SM中，2）整个队列中使用者服用的药物研究，特别注意 神经精神症状也与AD/ADRD相关。目标2是开发和评价一个 语言模型，可以自动分类患者的书面生产在SM，以确定潜在的 通过分析被诊断患有EOAD的人随时间推移在SM上的帖子， 认知障碍在其叙述文本的内容、语法和形式上明显。除了方法 和系统已经开发下的父母赠款，任务杠杆基线方法，以及 我们自己的推特收藏这些资源使我们处于独特的位置，以完成研究方法 这通常需要几年的时间，而这一年正好在行政当局所涵盖的时间范围内。 补充，使我们能够迅速加强我们的合作和研究，以进一步提交提案， NIA或NIGMH。

Project Summary In our parent grant, the overall goal is to develop novel NLP methods to leverage Social Media (SM) data for specific pharmacovigilance (PV) efforts that are hindered by known drawbacks of traditional PV approaches. We focus on methods to facilitate the use of longitudinal SM data for exploring (a) factors affecting medication adherence and persistence among the general population (Aim 1), and (b) possible associations between medications taken during pregnancy and pregnancy outcomes (Aim 2). We also address a major roadblock for case-control studies based on this data: finding controls (Aim 3). In general, the methodological focus of the parent grant is on advancing NLP methods that enable the large-scale integration of SM as a content-rich source of user-generated data in epidemiological studies, including relevant data posted over time. We are seeking to extend methods developed for Aims 1, 2, and 3 of the parent grant to focus on Alzheimer's Disease and Alzheimer's Disease Related Dementia (AD/ADRD). The Objectives for this supplement are responsive to NOT-AG-20-034, and include the following: Objective 1 is to develop and evaluate NLP methods to identify and characterize a cohort of Twitter users affected by an AD/ADRD diagnosis, specifically, Twitter users who declare that their direct relatives (parents, grandparents, or siblings), significant others, or even themselves, have been diagnosed with AD/ADRD. The collected timelines of users in the cohort will be further mined for demographic characterization (age, gender, ethnicity, and geographic location of residence) and specific methods will be developed to extract familial relationship to the AD/ADRD patient, and information relevant to two specific focus studies: 1) a study on early onset (also known as young onset) and potential lifetime factors reflected in SM and 2) study of medications taken by users in the whole cohort, with specific attention to neuropsychiatric symptoms also associated with AD/ADRD. Objective 2 is the development and evaluation of a linguistic model that can automatically classify patients' written productions in SM to determine potential cognitive impairment by analyzing postings over time on SM by people diagnosed with EOAD for signs of cognitive impairment evident in the content, grammar, and form of their narrative text. In addition to methods and systems already developed under the Parent Grant, the tasks leverage baseline approaches as well as our own collection of Tweets. These resources put us in the unique position to complete research approaches that would normally take several years well within the span of the year covered by the administrative supplement, allowing us to quickly ramp up our collaborations and research for further proposal submissions to the NIA or NIGMH.