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。作为电子产品 健康记录系统变得更加先进和成熟，有可能纳入广泛的和 从基因组学到移动健康(MHealth)应用的各种数据，其范围和性质 研究人员提出的生物医学数据科学问题变得更加广泛。随之而来的是他们的答案 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 赌注很高。然而，在目前的数据建模中，还没有一个生命伦理学框架来指导这一过程 绘制关键决策点，并记录所做选择的理由。做出数据建模决策 明确的观点及其背后的推理将对改进生物医学产生双重影响 数据科学：1.提高数据科学的透明度和再现性，最大限度地发挥数据科学的价值 研究和2.支持从最关键的方面评估决策点和理由的能力 道德后果。这一领域的研究尤其适时，因为人们对 利用大数据资源改善患者群体健康和普通民众的健康 公开的。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 比现在更好的时机是创建一个初始的生物伦理框架，为共同的数据建模决策点提供信息。 决策点测绘和生物伦理审查带来的数据质量改进将 加大工作力度，将数据模型应用于从预测分析到支持等一系列高影响领域 对人口健康中的强健趋势模型进行临床决策，以更好地为当地、地区和 国家卫生政策和资源分配。为了建立这个初步的生物伦理学框架，我们将很好地利用- 建立了定性研究方法(访谈、焦点小组和面对面审议)，以绘制 生物医学数据建模研究中的决策点，并记录支持这些点的基本原理 决策(目标1与关键线人面谈)；评估数据科学决策点和决策 其生物伦理影响的理论基础(目标2个焦点小组)；并创建初始生物伦理数据模型 框架(目标3审议会议)。这项研究将是第一个提供生物伦理学框架以满足 生物医学数据建模活动中的一个关键差距，即开发数据的下游后果 没有对道德问题进行仔细和全面审查的模特可能会很严重。这种方法直接 支持包容性、透明度、问责制和可再现性等核心科学价值观，这些价值观反过来又促进 信任生物医学数据建模输出和潜在的应用，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.