Computable Phenotyping: Disease-Agnostic Translational Methods to Puberty and Diabetes in Adolescent Males.

IF 2.2 4区 医学 Q1 NURSING
David J Dj Schnabel, Lorah Dorn
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引用次数: 0

Abstract

Background: Computable phenotyping is a data science method that systematically synthesizes clinical attributes, such as a disease, condition, or patient cohort, enabling a database to be queried for entries matching these characteristics. Developing computable phenotypes will enhance current clinical and research efforts and is foundational for effective nurse scholar participation in future data science endeavors, such as artificial intelligence (AI) and machine learning (ML) research.

Objective: (a) Present a foundational, disease-agnostic framework for systematic computable phenotype construction; (b) demonstrate the framework used by exploring the following question: "Does early pubertal timing increase the risk of developing type II diabetes in males?"; and (c) outline the methodologic utility and limitations of computable phenotyping for nursing research.

Methods: A proof-of-concept pilot project explored computable phenotype research utility by querying the TriNetX© de-identified health record database. Various computable phenotypes were constructed to retrieve complete case frequency counts of specific health records for children experiencing puberty. These retrieved records allowed for quantifying type 2 diabetes (T2D) risk by comparing children diagnosed with precocious puberty (medically diagnosed early puberty) to those without an abnormal puberty diagnosis. A translational science lens informed the extraction and synthesis of the underlying scientific and operational principles relevant to systematic computable phenotyping.

Results: A six-step, disease-agnostic, computable phenotyping framework is synthesized for nurse researchers and clinicians to leverage "big data" applications in their work. The puberty case example-illustrating foundational use of the framework-suggests that males with precocious puberty may be six times more likely to develop T2D when 14-18 years old than those without diagnosed early puberty. The framework provides a foundation for sophisticated statistical analyses, such as leveraging computable phenotypes in multivariate modeling and machine learning algorithms.

Discussion: The six-step, computable phenotype framework will introduce nurse scholars and clinicians to leverage data science principles in real-world interfaces. Applications using the framework can include generating and testing epidemiologic hypotheses, identifying participants for research with specific clinical attributes, deploying statistical models for health care monitoring and decision-making, and participating in future research on AI and ML algorithms. The puberty case example generates foundational evidence to justify future puberty research.

可计算表型:青少年男性青春期和糖尿病的疾病不可知论转化方法。
背景:可计算表型是一种数据科学方法,它系统地综合临床属性,如疾病、状况或患者队列,使数据库能够查询符合这些特征的条目。发展可计算表型将加强当前的临床和研究工作,并为护士学者有效参与未来的数据科学努力(如人工智能(AI)和机器学习(ML)研究)奠定基础。目的:(a)为系统的可计算表型构建提供一个基础的疾病不可知论框架;(b)通过探讨以下问题展示所使用的框架:“青春期提前是否会增加男性患II型糖尿病的风险?”(c)概述护理研究中可计算表型的方法学效用和局限性。方法:一个概念验证试点项目通过查询TriNetX©去识别健康记录数据库探索可计算表型研究的实用性。构建了各种可计算表型,以检索经历青春期的儿童特定健康记录的完整病例频率计数。通过比较被诊断为性早熟(医学上诊断为性早熟)的儿童与未被诊断为性早熟的儿童,这些检索到的记录可以量化2型糖尿病(T2D)的风险。翻译科学镜头告知提取和综合相关的系统可计算表型的潜在科学和操作原则。结果:为护士研究人员和临床医生在他们的工作中利用“大数据”应用,合成了一个六步、疾病不可知论、可计算的表型框架。青春期的例子——说明了该框架的基本用法——表明,在14-18岁时,性早熟的男性患T2D的可能性是未被诊断出性早熟的男性的6倍。该框架为复杂的统计分析提供了基础,例如在多变量建模和机器学习算法中利用可计算表型。讨论:六步,可计算的表型框架将介绍护士学者和临床医生利用数据科学原理在现实世界的接口。使用该框架的应用程序可以包括生成和测试流行病学假设,识别具有特定临床属性的研究参与者,部署用于医疗保健监测和决策的统计模型,以及参与未来对人工智能和机器学习算法的研究。青春期的案例为未来的青春期研究提供了基础证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nursing Research
Nursing Research 医学-护理
CiteScore
3.60
自引率
4.00%
发文量
102
审稿时长
6-12 weeks
期刊介绍: Nursing Research is a peer-reviewed journal celebrating over 60 years as the most sought-after nursing resource; it offers more depth, more detail, and more of what today''s nurses demand. Nursing Research covers key issues, including health promotion, human responses to illness, acute care nursing research, symptom management, cost-effectiveness, vulnerable populations, health services, and community-based nursing studies. Each issue highlights the latest research techniques, quantitative and qualitative studies, and new state-of-the-art methodological strategies, including information not yet found in textbooks. Expert commentaries and briefs are also included. In addition to 6 issues per year, Nursing Research from time to time publishes supplemental content not found anywhere else.
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