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Healtex: UK Healthcare Text Analytics Research Network

Healtex：英国医疗保健文本分析研究网络

基本信息

批准号：
EP/N027280/1
负责人：
Goran Nenadic
金额：
$ 43.38万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FN027280%2F1
关键词：
Healtex UK Healthcare Text Analytics

项目摘要

Healthcare is a prime example of "big data science" with a number of challenges and successful stories where actionable information extracted from data has improved and saved lives [1]. The majority of concerted efforts focused on real-time processing and integration of structured data streams coming from clinical coding, diagnostic tests, sensor measurements, questionnaires, etc. to support timely clinical interventions and facilitate patients' self-management. Nonetheless, natural language remains the main means of communication within healthcare with its written accounts becoming increasingly available in an electronic form, thus giving rise to big text data. Prominent examples include text data embedded within electronic health records (e.g. referral letters, case notes, pathology reports, hospital discharge summaries, etc.), patient-reported outcome measures (e.g. questionnaires, diaries, etc.) or unsolicited informal feedback shared openly on the Web 2.0 (e.g. social media, fora, etc.). Unfortunately, the capacity to effectively utilise information from unstructured text data on a big scale is lagging behind its structured counterpart. The fact that the majority of actionable information in healthcare is contained within text data (some estimates shows as much as 85%) clearly indicates a potential to dramatically transform community health and care by the ability to process and integrate such information in real time. However, automated and large-scale "understanding" of diverse healthcare sublanguages is still largely unsolved research challenge due to their dynamics, idiosyncrasy, ambiguity and variability.The aim of this proposal is to build a UK-wide multi-disciplinary research network in order to explore the barriers to effectively utilising healthcare narrative text data, road-map research efforts and principles for sharing text data and text analytics methods between academia, NHS and industry. The network will directly address the "Transforming Community Health and Care" grand challenge by enabling research that will deploy healthcare narratives as real-time sensors and integrate them with the structured data streams into a patient-focused collaborative ecosystem, which will involve healthcare professionals, patients, carers and researchers. Such systemic network of healthcare activities will facilitate informed decision making, timely interventions, deeper digital phenotyping for clinical epidemiology and population-based modelling. On the other hand, by processing patient-generated narratives, which are often a preferred and likely means to provide patient responses (e.g. text messages) to complement structured healthcare data (e.g. signals from wearable devices), we will "use real-time information to support self-management of health and wellbeing".The main outcome of the network will be a strong, sustainable community that will continue its mission after the initial 3 years of support. Other outcomes will include (1) reports describing the state-of-the-art and challenges for key barriers in harnessing text narratives and making sense from them; (2) a research roadmap for healthcare text analytics; (3) an enlarged membership and expanded collaborations within the network, in particular with early career researchers and internationally; (4) a series of focused pilot/feasibility projects that will inform further developments and kick-start collaborative projects; (5) a collection of research papers at conferences and journals, improving the UK competitiveness in this growing area; (6) several project proposals scoped during the project and prepared for submission; (7) proposals for discipline-bridging personal fellowships, and (8) an interactive registry of healthcare text analytics expertise, resources and tools so that the users and collaborators can identify existing resources and initiate new collaboration.

医疗保健是“大数据科学”的一个典型例子，它具有许多挑战和成功的故事，其中从数据中提取的可操作信息已经改善并挽救了生命[1]。大部分的协同努力集中在实时处理和整合来自临床编码、诊断测试、传感器测量、问卷调查等的结构化数据流，以支持及时的临床干预并促进患者的自我管理。尽管如此，自然语言仍然是医疗保健领域的主要通信手段，其书面记录越来越多地以电子形式提供，从而产生了大文本数据。突出的例子包括嵌入电子健康记录中的文本数据（例如转诊信、病例记录、病理报告、出院摘要等），患者报告的结局指标（例如问卷、日记等）或在Web 2.0上公开分享的未经请求的非正式反馈（例如社交媒体、论坛等）。不幸的是，大规模有效利用非结构化文本数据中信息的能力落后于结构化数据。医疗保健中的大多数可操作信息都包含在文本数据中（一些估计显示高达85%），这一事实清楚地表明，通过真实的时间处理和整合这些信息的能力，有可能极大地改变社区健康和护理。然而，由于其动态性、特质性、模糊性和可变性，不同医疗保健亚语言的自动化和大规模“理解”仍然是很大程度上未解决的研究挑战。本提案的目的是建立一个英国范围内的多学科研究网络，以探索有效利用医疗保健叙事文本数据的障碍，路线图研究工作和原则，用于在学术界，NHS和行业之间共享文本数据和文本分析方法。该网络将直接解决“转变社区卫生和护理”的重大挑战，通过支持将医疗保健叙事部署为实时传感器的研究，并将其与结构化数据流集成到以患者为中心的协作生态系统中，这将涉及医疗保健专业人员，患者，护理人员和研究人员。这种系统性的医疗保健活动网络将促进明智的决策，及时的干预措施，更深入的临床流行病学数字表型分析和基于人群的建模。另一方面，通过处理患者生成的叙述，这通常是提供患者反应的首选和可能的手段（例如文本消息），以补充结构化医疗保健数据（例如来自可穿戴设备的信号），我们将“使用实时信息来支持健康和福祉的自我管理”。该网络的主要成果将是一个强大的，可持续发展的社区，将继续其使命后，最初3年的支持。其他成果将包括（1）描述利用文本叙述并从中获得意义的关键障碍的最新技术和挑战的报告;（2）医疗保健文本分析的研究路线图;（3）扩大成员和扩大网络内的合作，特别是与早期职业研究人员和国际合作;（4）一系列有重点的试点/可行性项目，将为进一步的发展提供信息，并启动合作项目;（5）收集会议和期刊上的研究论文，提高英国在这一不断增长的领域的竞争力;（6）在项目期间确定范围并准备提交的几个项目提案;（7）跨学科个人奖学金提案，以及（8）医疗保健文本分析专业知识、资源和工具的交互式注册表，以便用户和合作者可以识别现有资源并启动新的合作。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Changes in daily mental health service use and mortality at the commencement and lifting of COVID-19 'lockdown' policy in 10 UK sites: a regression discontinuity in time design.

DOI：
10.1136/bmjopen-2021-049721
发表时间：
2021-05-26
期刊：
BMJ open
影响因子：
2.9
作者：
Bakolis I;Stewart R;Baldwin D;Beenstock J;Bibby P;Broadbent M;Cardinal R;Chen S;Chinnasamy K;Cipriani A;Douglas S;Horner P;Jackson CA;John A;Joyce DW;Lee SC;Lewis J;McIntosh A;Nixon N;Osborn D;Phiri P;Rathod S;Smith T;Sokal R;Waller R;Landau S
通讯作者：
Landau S

Predictors of physical activity recording in routine mental healthcare

常规心理保健中体力活动记录的预测因素

DOI：
10.1016/j.mhpa.2020.100329
发表时间：
2020
期刊：
Mental Health and Physical Activity
影响因子：
4.7
作者：
Ashdown-Franks G
通讯作者：
Ashdown-Franks G

Mental health consequences of urban air pollution: prospective population-based longitudinal survey.

DOI：
10.1007/s00127-020-01966-x
发表时间：
2021-09
期刊：
Social psychiatry and psychiatric epidemiology
影响因子：
4.4
作者：
Bakolis I;Hammoud R;Stewart R;Beevers S;Dajnak D;MacCrimmon S;Broadbent M;Pritchard M;Shiode N;Fecht D;Gulliver J;Hotopf M;Hatch SL;Mudway IS
通讯作者：
Mudway IS

Antipsychotic monitoring in dementia: quality of completion of antipsychotic monitoring forms in an older adult mental health service.