Creating an initial ethics framework for biomedical data modeling by mapping and exploring key decision points

项目摘要 生物医学数据科学数据建模与大量的信息学研究活动相关，例如 自然语言处理、机器学习、人工智能和预测分析。为电子 健康记录系统变得更加先进和成熟，有可能纳入广泛的， 从基因组学到移动的健康（mHealth）应用程序的各种数据， 生物医学数据科学研究人员提出的问题变得更加广泛。与此同时，他们的答案 问题有可能影响数百万患者的护理-积极主动地获得正确的答案， 高风险然而，在目前的数据建模中，没有一个生物伦理学框架来指导数据建模的过程。 绘制关键决策点并记录所作选择的理由。制定数据建模决策 点，以及他们背后的推理，明确将有双重影响，改善生物医学 数据科学：1。提高透明度和可重复性，最大限度地发挥数据科学的价值 研究和2。支持评估决策点和基本原理的能力， 道德后果这一领域的研究特别及时， 利用大数据资源，为改善患者人群健康和公众健康服务 公众美国国立卫生研究院（NIH）最近发布了一项数据科学战略计划;没有 比现在更好的时间来创建一个初始的生物伦理框架，以告知共同的数据建模决策点。 决策点绘图和生物伦理审查将提高数据质量， 加强在一系列高影响力领域应用数据模型的努力，从预测分析到支持 临床决策，以强大的趋势模型在人口健康，以更好地告知地方，区域， 国家卫生政策和资源分配。为了制定这一初步的生物伦理学框架，我们将充分利用- 建立定性研究方法（访谈，焦点小组和亲自审议），以绘制 生物医学数据建模研究中的决策点，并记录支持这些决策点的基本原理 决策（目标1关键知情人访谈）;评估这些数据科学决策点和决策 其生物伦理学分支的基本原理（目标2焦点小组）;并创建初始生物伦理学数据模型 框架（目标3审议会议）。这项研究将首次提供一个生物伦理学框架， 生物医学数据建模活动中的一个关键差距，其中开发数据的下游后果 没有对道德问题进行认真和全面审查的模式可能是严重的。这种方法直接 支持包容性、透明度、问责制和可重复性的核心科学价值观，反过来， 对生物医学数据建模输出和潜在应用程序的信任，无论是本地的，国家的还是全球的。

Project Summary Biomedical data science data modeling is relevant to a plethora of informatics research activities, such as natural language processing, machine learning, artificial intelligence, and predictive analytics. As Electronic Health Record systems become more advanced and more mature, with the potential to incorporate a wide and diverse array of data from genomics to mobile health (mHealth) applications, the scope and nature of the biomedical data science questions researchers ask become broader. Concomitantly, the answers to their questions have the potential to impact the care of millions of patients—getting the answers right, proactively, is high stakes. However, in data modeling currently, there is no bioethics framework to guide the process of mapping key decision points and recording the rationale for choices made. Making data modeling decision points, as well as the reasoning behind them, explicit would have a twofold impact on improving biomedical data science by: 1. Enhancing transparency and reproducibility and maximizing the value of data science research and 2. Supporting the ability to assess decision points and rationales in terms of their most crucial ethical ramifications. Research in this area is particularly timely amid the interest in, and enthusiasm for, leveraging Big Data sources in the service of improving patient population health and the health of the general public. The National Institutes of Health (NIH) recently released a strategic plan for data science; there is no better time than now to create an initial bioethical framework to inform common data modeling decision points. The improvements in data quality that will derive from decision point mapping and bioethical review will enhance efforts to apply data models across a range of high-impact areas, from predictive analytics to support clinical decision-making to robust trending models in population health to better inform local, regional, and national health policies and resource allocation. To develop this initial bioethics framework, we will use well- established qualitative research methods (interviews, focus groups, and in-person deliberation) to map the decision points in biomedical data modeling research and document the rationales invoked to support those decisions (Aim 1 key informant interviews); assess those data science decision points and decision-making rationales for their bioethical ramifications (Aim 2 focus groups); and create an initial bioethics data modeling framework (Aim 3 deliberative meeting). This study would be the first to provide a bioethics framework to meet a critical gap in biomedical data modeling activities, where the downstream consequences of developing data models without careful and comprehensive review of ethical issues can be severe. This approach directly supports core scientific values of inclusivity, transparency, accountability, and reproducibility that, in turn, foster trust in biomedical data modeling output and potential applications, whether local, national, or global.