Increases in synthetic cannabinoids-related harms: Results from a longitudinal web-based content analysis

BackgroundSynthetic Cannabinoid Receptor Agonists (SCRA), also known as “K2” or “Spice,” have drawn considerable attention due to their potential of abuse and harmful consequences. More research is needed to understand user experiences of SCRA-related effects. We use semi-automated information processing techniques through eDrugTrends platform to examine SCRA-related effects and their variations through a longitudinal content analysis of web-forum data.MethodEnglish language posts from three drug-focused web-forums were extracted and analyzed between January 1st 2008 and September 30th 2015. Search terms are based on the Drug Use Ontology (DAO) created for this study (189 SCRA-related and 501 effect-related terms). EDrugTrends NLP-based text processing tools were used to extract posts mentioning SCRA and their effects. Generalized linear regression was used to fit restricted cubic spline functions of time to test whether the proportion of drug-related posts that mention SCRA (and no other drug) and the proportion of these “SCRA-only” posts that mention SCRA effects have changed over time, with an adjustment for multiple testing.Results19,052 SCRA-related posts (Bluelight (n = 2782), Forum A (n = 3882), and Forum B (n = 12,388)) posted by 2543 international users were extracted. The most frequently mentioned effects were “getting high” (44.0%), “hallucinations” (10.8%), and “anxiety” (10.2%). The frequency of SCRA-only posts declined steadily over the study period. The proportions of SCRA-only posts mentioning positive effects (e.g., “High” and “Euphoria”) steadily decreased, while the proportions of SCRA-only posts mentioning negative effects (e.g., “Anxiety,” ‘Nausea,” “Overdose”) increased over the same period.ConclusionThis study’s findings indicate that the proportion of negative effects mentioned in web forum posts and linked to SCRA has increased over time, suggesting that recent generations of SCRA generate more harms. This is also one of the first studies to conduct automated content analysis of web forum data related to illicit drug use.     

Determining the difficulty of Word Sense Disambiguation

Automatic processing of biomedical documents is made difficult by the fact that many of the terms they contain are ambiguous. Word Sense Disambiguation (WSD) systems attempt to resolve these ambiguities and identify the correct meaning. However, the published literature on WSD systems for biomedical documents report considerable differences in performance for different terms. The development of WSD systems is often expensive with respect to acquiring the necessary training data. It would therefore be useful to be able to predict in advance which terms WSD systems are likely to perform well or badly on.This paper explores various methods for estimating the performance of WSD systems on a wide range of ambiguous biomedical terms (including ambiguous words/phrases and abbreviations). The methods include both supervised and unsupervised approaches. The supervised approaches make use of information from labeled training data while the unsupervised ones rely on the UMLS Metathesaurus. The approaches are evaluated by comparing their predictions about how difficult disambiguation will be for ambiguous terms against the output of two WSD systems. We find the supervised methods are the best predictors of WSD difficulty, but are limited by their dependence on labeled training data. The unsupervised methods all perform well in some situations and can be applied more widely.     

What is the potential for context aware communication aids?

Abstract

Use of voice output communication aids (VOCAs) can be a very effective strategy to assist people with speech impairments in communicating. Despite this, people who use communication aids often express frustration with VOCAs—desiring devices that are simpler, quicker and more effective to use. Whilst it is not possible to resolve all these issues with technology, it is argued that significant progress can be made. The use of contextual information is one development that could improve the simplicity and effectiveness of communication aid design. Improving the effectiveness of communication aids, including through the use of context support, is a goal of the NIHR Devices for Dignity Assistive Technology Theme. This discussion paper examines the potential for creating ‘context aware’ communication aids. Three projects in which the authors have been involved are described to illustrate different approaches to the use of contextual information.     

Generation of an annotated reference standard for vaccine adverse event reports

As part of a collaborative project between the US Food and Drug Administration (FDA) and the Centers for Disease Control and Prevention for the development of a web-based natural language processing (NLP) workbench, we created a corpus of 1000 Vaccine Adverse Event Reporting System (VAERS) reports annotated for 36,726 clinical features, 13,365 temporal features, and 22,395 clinical-temporal links. This paper describes the final corpus, as well as the methodology used to create it, so that clinical NLP researchers outside FDA can evaluate the utility of the corpus to aid their own work. The creation of this standard went through four phases: pre-training, pre-production, production-clinical feature annotation, and production-temporal annotation. The pre-production phase used a double annotation followed by adjudication strategy to refine and finalize the annotation model while the production phases followed a single annotation strategy to maximize the number of reports in the corpus. An analysis of 30 reports randomly selected as part of a quality control assessment yielded accuracies of 0.97, 0.96, and 0.83 for clinical features, temporal features, and clinical-temporal associations, respectively and speaks to the quality of the corpus.     

基于框架语义分析的社交网络药品不良事件抽取

目的/意义 基于社交网络评论文本抽取药品不良事件,为药品研发和安全监管提供参考。方法/过程 采用框架语义理论,结合《监管活动医学词典》术语集构建药品不良事件分类词表;基于词典和规则匹配的方法识别事件类别和框架元素,利用语义信息实现药品不良事件框架填充。结果/结论 选取社交网络药品评价实例进行药品不良事件信息抽取可行有效,有助于框架语义分析方法在医疗专业领域的深度应用和价值实现。     

CHIP 2021评测任务1概述:医学对话临床发现阴阳性判别任务

介绍基于医学对话的信息抽取相关研究现状,结合第七届中国健康信息处理会议组织的基于医学对话的临床发现阴阳性判别评测任务,阐述该任务评测数据集、评估指标,对比介绍前4名参赛者的方法并进行总结,以推进医疗人工智能领域的发展。     

医疗多模态信息抽取技术评测数据集概述

阐述医疗多模态信息抽取技术评测数据集的结构、构建方法、应用情况等,包括面向“基因-疾病”的关联语义挖掘数据集、中文医疗因果关系抽取数据集、医疗文本诊疗决策树抽取数据集、医疗材料要素提取数据集、临床诊断编码数据集,提出上述数据集有望为各种技术、算法以及系统的评估和实施提供有力支撑和参考。     

基于数据治理的专病数据库建设实践

提出运用数据治理理念和全生命周期技术路径解决专病数据库建设实践中的数据相关问题,阐述专病数据治理方法,介绍专病数据库核心功能,加速真实世界临床研究的成果转化,助力专病、学科高质量发展。     

Exploring prevalence of wound infections and related patient characteristics in homecare using natural language processing

We aimed to create and validate a natural language processing algorithm to extract wound infection-related information from nursing notes. We also estimated wound infection prevalence in homecare settings and described related patient characteristics. In this retrospective cohort study, a natural language processing algorithm was developed and validated against a gold standard testing set. Cases with wound infection were identified using the algorithm and linked to Outcome and Assessment Information Set data to identify related patient characteristics. The final version of the natural language processing vocabulary contained 3914 terms and expressions related to the presence of wound infection. The natural language processing algorithm achieved overall good performance (F-measure = 0.88). The presence of wound infection was documented for 1.03% (n = 602) of patients without wounds, for 5.95% (n = 3232) of patients with wounds, and 19.19% (n = 152) of patients with wound-related hospitalisation or emergency department visits. Diabetes, peripheral vascular disease, and skin ulcer were significantly associated with wound infection among homecare patients. Our findings suggest that nurses frequently document wound infection-related information. The use of natural language processing demonstrated that valuable information can be extracted from nursing notes which can be used to improve our understanding of the care needs of people receiving homecare. By linking findings from clinical nursing notes with additional structured data, we can analyse related patients' characteristics and use them to develop a tailored intervention that may potentially lead to reduced wound infection-related hospitalizations.