Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care.
Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care.
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DOI：
10.1162/99608f92.8439a336
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发表时间：
2022
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“数据科学”一词通常是指从大量个人获得的大数据中提取价值的过程。另一种形式，我们称之为个性化数据科学（Per-DS），旨在收集、分析和解释个人数据，为个人决策提供信息。本文介绍了Per-DS的主要特性，并回顾了它的现状和未来展望。Per-DS调查由Per-DS调查员个人进行，同时作为她自己的调查员、研究参与者和受益人，并为研究设计和实施做出个性化的决定。Per-DS研究的范围可能包括生理或行为模式的系统监测，症状触发的病例交叉研究，暴露-结果关系的前后试验，以及有效性的个性化（N-of-1）试验。Per-DS研究产生的个人知识可推广到个人未来的自我（从而使自己受益），而不是可推广到外部人群（从而使他人受益）。这种努力需要从数据挖掘或提取到数据园艺的枢纽，类似于家庭园丁为家庭消费生产食物- Per-DS调查员需要通过设定目标，指定研究设计，识别必要的数据元素以及组装数据收集的仪器和工具来“培育田地”。然后，她可以执行研究方案，获取她的个人数据，并对数据进行挖掘以提取个人知识。为了促进Per-DS研究，Per-DS研究人员需要社区、科学、慈善、商业和政府实体的支持，以开发和部署诸如同行论坛、移动应用程序、“虚拟现场指南”以及科学和监管指导等资源。数据科学通常被理解为从大量个人获得的“大数据”中提取或挖掘知识的过程，以获得可用于制定临床、企业或公共政策的见解。本文介绍了一个互补的结构：个性化数据科学（Per-DS），对个人自己的数据进行科学调查。每个人的Per-DS调查产生的个人知识是为了使自己受益，而不是为了使他人受益的泛化知识。每个Per-DS研究者同时作为研究者、研究参与者和她自己研究的受益者。这样的研究需要个人积极参与研究设计、数据收集、分析和解释——我们称之为数据园艺的过程，类似于家庭园丁为家庭消费生产食物，以强调“耕种田地”的必要性，以便从日常生活的丰富地形中产生个人数据，以收获个人知识，为个人的个人决策提供信息。Per-DS调查可用于识别个体生理或行为中的异常；找出症状的诱因；评估暴露（药物、营养物、环境因素和行为）与结果之间的关系；并比较治疗的效果。这些调查直接从“我这个病人”那里获得知识，而不是依赖于“像我这样的病人”的代理结果。尽管Per-DS很有吸引力，但仍有许多障碍威胁着它的普及。重要的是，大多数对进行自己的Per-DS调查感兴趣的人无法独自完成。他们需要同行的社会支持，公共和私营部门的资源支持，以及数据科学界和学科领域专家的支持，以“土木工程师”的身份加强建设所需的基础设施和工具，例如虚拟同行论坛；“虚拟实地指南”，协助个别Per-DS调查员进行具体研究；用户友好的应用程序，方便自我管理的调查；研究设计模板；收集数据的调查表和传感器；分析算法;以及帮助审查和解释结果的工具。反过来，这些支持团体将需要道德和监管指导，以确保新的Per-DS方法的安全性和有效性，并优化其实施。将数据科学带入数以百万计的“家庭数据花园”的前景既是一个艰巨的挑战，也是一个巨大的机遇。在同行的家庭园丁和信誉良好的种子、肥料、指南等供应商的帮助下，许多家庭园丁都为自己的美味牛排西红柿感到自豪。在同行数据园丁的帮助和数据科学和健康科学供应商的支持下，许多家庭数据园丁可能也准备好了他们的家庭数据“西红柿”。“如果成功，这些努力不仅可以为个人福祉带来好处，还可以创造一个更包容、更少等级的知识企业，以及一种基于证据的决策文化。”
The term ‘data science’ usually refers to the process of extracting value from big data obtained from a large group of individuals. An alternative rendition, which we call personalized data science (Per-DS), aims to collect, analyze, and interpret personal data to inform personal decisions. This article describes the main features of Per-DS, and reviews its current state and future outlook. A Per-DS investigation is of, by, and for an individual, the Per-DS investigator, acting simultaneously as her own investigator, study participant, and beneficiary, and making personalized decisions for study design and implementation. The scope of Per-DS studies may include systematic monitoring of physiological or behavioral patterns, case-crossover studies for symptom triggers, pre-post trials for exposure–outcome relationships, and personalized (N-of-1) trials for effectiveness. Per-DS studies produce personal knowledge generalizable to the individual’s future self (thus benefiting herself) rather than knowledge generalizable to an external population (thus benefiting others). This endeavor requires a pivot from data mining or extraction to data gardening, analogous to home gardeners producing food for home consumption—the Per-DS investigator needs to ‘cultivate the field’ by setting goals, specifying study design, identifying necessary data elements, and assembling instruments and tools for data collection. Then, she can implement the study protocol, harvest her personal data, and mine the data to extract personal knowledge. To facilitate Per-DS studies, Per-DS investigators need support from community-based, scientific, philanthropic, business, and government entities, to develop and deploy resources such as peer forums, mobile apps, ‘virtual field guides,’ and scientific and regulatory guidance. Data science is commonly construed as the process of extracting or mining knowledge from ‘big data’ obtained from a large group of individuals, for insights that can be used to shape clinical, corporate, or public policies. This article introduces a complementary construction: personalized data science (Per-DS), the scientific investigation of an individual’s own data. Each individual’s Per-DS investigation produces personal knowledge meant to benefit the individual herself, rather than generalizable knowledge meant to benefit others. The individual Per-DS investigator acts simultaneously as the investigator, study participant, and beneficiary of her own study. Such studies require the active involvement of the individual in study design, data collection, analysis, and interpretation—a process we call data gardening, analogous to home gardeners producing food for home consumption, to highlight the need to ‘cultivate the field’ in order to produce personal data from the rich terrain of daily life, to be harvested for personal knowledge to inform the individual’s personal decision. Per-DS investigations can be used to identify aberrations in an individual’s physiology or behavior; to ferret out symptom triggers; to evaluate relationships between exposures (drugs, nutrients, environmental agents, and behaviors) and outcomes; and to compare the effectiveness of treatments. Those investigations derive knowledge directly from ‘the patient that is me,’ rather than relying on proxy results from ‘patients like me.’ Despite the appeal of Per-DS, numerous barriers threaten its uptake. Importantly, most individuals who are interested in conducting their own Per-DS investigations cannot do it on their own. They need social support from peers, resource support from public and private sectors, and support from the data science community and subject area experts to step up as ‘civil engineers’ to build the needed infrastructure and tools, such as virtual peer forums; ‘virtual field guides’ to assist individual Per-DS investigators with their specific studies; user-friendly apps to facilitate self-administered investigations; templates for study design; questionnaires and sensors for data collection; analytic algorithms; and tools to aid review and interpretation of results. These support groups will, in turn, require ethical and regulatory guidance to ensure safety and effectiveness of new Per-DS approaches and to optimize their implementation. The prospect of bringing data science into millions of ‘home data gardens’ is both a daunting challenge and a tremendous opportunity. Numerous home gardeners take pride in their tasty beefsteak tomatoes, with help from peer home gardeners and reputable suppliers for seeds, fertilizer, guidebooks, and so on. Many home data gardeners might also be ready for their home data ‘tomatoes,’ with help from peer data gardeners and support from data science and health science ‘suppliers.’ If successful, these efforts could produce not only benefits for individual well-being, but also a more inclusive and less hierarchical knowledge enterprise, and a culture of evidenced-based decision-making.