SIPHS: Semantic interpretation of personal health messages for generating public health summaries

公开的在线数据，如微博和讨论板信息，有可能成为一个非常有价值的人群健康信息来源。这种数据一直在迅速增长，成本低，实时，似乎有可能覆盖人口的很大一部分。举两个例子，PatientsLikeMe增长了10%，现在有超过20万用户，覆盖1500多种健康状况;通用Twitter服务每年以30%的速度增长，活跃用户超过2亿。超越简单的关键字搜索，利用这些数据进行公共卫生，对自然语言处理（NLP）来说既是机遇也是挑战。该奖学金提案旨在帮助健康专家通过自动技术利用社交媒体进行自己的临床和科学研究，这些技术根据机器可理解的语义表示对消息进行编码。该项目寻求解决三个主要挑战：（1）知识中介：开发算法来识别和编码对标准本体（如UMLS）的条件、治疗、药物、行为和态度的非正式描述;（2）知识管理：创建博客文本中使用的患者词汇的结构化资源，并将其与现有编码系统链接;以及（3）为证据添加洞察力：与领域专家合作，利用编码信息自动生成有意义的摘要，以供后续调查。在技术层面，该奖学金旨在开拓NLP和机器学习（ML）的新方法。由于各种原因，社交媒体仍然是NLP的一个具有挑战性的领域：短的脱离语境的消息，高度的歧义/词汇量，俚语的使用和不断发展的词汇，以及对耸人听闻的话题的固有偏见。该奖学金旨在利用NLP迄今为止在商业领域的社交媒体分析中取得的进展，并进一步发展它，以提供有意义的公共卫生证据。一个以前没有提到的关键方面是患者消息的临床编码。虽然存在用于临床和科学文本的知识中介系统（例如NLM的MetaMap），但它们在社交媒体消息上的表现一直很差。该奖学金将利用生物医学中丰富的本体论资源以及ML对注释消息数据的使用，以消除非正式语言的歧义。研究还将旨在了解信息的沟通功能，例如，信息是否报告直接经验或与新闻，幽默或营销有关。如果成功克服这些问题，将消除数据与其他类型临床数据集成的重要障碍。为社交媒体报告提供健康编码的优势在于其研究超大规模队列的潜力，以及实时早期异常警报。在奖学金中，我将研究语义编码特征的多变量时间序列警报的潜力，与领域专家合作，评估一系列指标（例如敏感性，及时性，错误警报率）。将探索各种方法，以生成跨社交媒体来源的真实的时间风险摘要。已经选择了两个现实世界的应用程序来推进这一点：药物不良反应（ADR）和传染病监测（IDS）的早期警报。项目成果将包括基础技术以及开源算法、数据集和本体。该奖学金的一个令人兴奋的方面是各级利益相关者之间的跨学科合作：科学家，公共卫生专家和行业。最后，将通过公布公开来源数据向国际社会开放参与。将邀请从事社交媒体技术工作的同事参加新挑战评估研讨会，与用户进行讨论。

Open online data such as microblogs and discussion board messages have the potential to be an incredibly valuable source of information about health in populations. Such data has been rapidly growing, is low cost, real-time and seems likely to cover a significant proportion of the demographic. To take two examples, PatientsLikeMe has enjoyed 10% growth and now has over 200,000 users covering over 1500 health conditions; the generic Twitter service is expanding at a rate of 30% annually with over 200 million active users. Going beyond simple keyword search and harnessing this data for public health represents both an opportunity and a challenge to natural language processing (NLP). This fellowship proposal is about helping health experts leverage social media for their own clinical and scientific studies through automatic techniques that encode messages according to a machine understandable semantic representation. There are three major challenges this project seeks to address: (1) knowledge brokering: to develop algorithms to identify and code the informal descriptions of conditions, treatments, medications, behaviours and attitudes to standard ontologies such as the UMLS; (2) knowledge management: to create a structured resource of patient vocabulary used in blog texts and link it to existing coding systems; and (3) adding insight to evidence: to work with domain experts to utilize the coded information to automatically generate meaningful summaries for follow up investigation. At the technological level the fellowship seeks to pioneer new methods for NLP and machine learning (ML). Social media remains a challenging area for NLP for a variety of reasons: short de-contextualised messages, high levels of ambiguity/out of vocabulary words, use of slang and an evolving vocabulary, as well as inherent bias towards sensational topics. The fellowship seeks to harness the progress made so far in NLP for social media analysis in the commercial domain and develop it further to provide meaningful public health evidence. One key aspect not previously addressed is in the clinical coding of patient messages. Although knowledge brokering systems exist for clinical and scientific texts (e.g. the NLM's MetaMap), their performance on social media messages has been poor. The fellowship will utilise the rich availability of ontological resources in biomedicine together with ML on annotated message data to disambiguate informal language. Research will also aim to understanding the communicative function of messages, for example whether the message reports direct experience or is related to news, humour or marketing. If these problems are successfully overcome an important barrier to data integration with other types of clinical data will be removed. The advantage of providing health coding for social media reports is its potential for studying very-large scale cohorts and also in real-time early alerting of aberrations. In the fellowship I will research the potential for multi-variate time series alerting from semantically coded features, working with domain experts to evaluate across a range of metrics (e.g. sensitivity, timeliness, false alerting rates). A variety of approaches will be explored to generate real time risk summaries across social media sources. Two real-world applications have been chosen to take this forwards: early alerting for Adverse drug reactions (ADRs) and Infectious disease surveillance (IDS). Project outcomes will include fundamental technologies as well as open source algorithms, data sets and ontology. An exciting aspect of this fellowship is inter-disciplinary collaboration across stakeholders at all levels: scientists, public health experts and industry. Finally, participation will be opened up to the international community through the release of open source data. Colleagues working on social media technologies will be invited to participate in discussions with users at a new challenge evaluation workshop.