Digital Phenotyping of Nonalcoholic Fatty Liver Disease

非酒精性脂肪肝的数字表型分析

• 批准号: 10376825
• 负责人: Alina M Allen
• 金额: $ 11.93万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10376825
• 关键词: Address; Adult; Affect; Algorithms; Artificial Intelligence; Cardiovascular Diseases; Cessation of life; Cirrhosis; Complex; Data; Data Analytics; Data Set; Decision Modeling; Development; Diagnosis; Disease; Early Diagnosis; Early identification; Electronic Health Record; Future; General Population; Goals; Health; Healthcare; Human; Individual; Intervention; Life; Liver; Liver diseases; Longevity; Machine Learning; Malignant Neoplasms; Methodology; Methods; Morbidity - disease rate; Obesity; Obesity Epidemic; Onset of illness; Outcome; Patient-Focused Outcomes; Patients; Pattern; Phenotype; Population; Predictive Factor; Probability; Process; Public Health; Research; Resources; Risk; Subgroup; Testing; Time; Training; United States; base; burden of illness; care outcomes; chronic liver disease; clinical decision-making; clinical phenotype; cohort; comorbidity; digital; disorder risk; follow-up; health data; improved; improved outcome; innovation; liver development; liver transplantation; machine learning model; mortality; non-alcoholic fatty liver disease; novel; patient stratification; population based; population health; predict clinical outcome; predictive modeling; prevent; risk stratification; screening; tool; trait; unsupervised learning

1 2 PROJECT SUMMARY/ABSTRACT 3 4 One of the most critical gaps in management of nonalcoholic fatty liver disease (NAFLD) is the lack of effective 5 methods of early identification in the population. The objective of this study is to leverage data and analytics to 6 improve healthcare outcomes by early detection and risk stratification of NAFLD, before onset of liver-related 7 complications. Artificial intelligence applications in large electronic health records have the potential to identify 8 disease traits before onset of disease. The central hypothesis of this proposal is that targeted screening with 9 machine-learning models applied to large integrated healthcare datasets can identify individuals with NAFLD 10 and, more specifically, those with a progressive phenotype. We will test the central hypothesis in 2 specific 11 AIMs. First, we will train a machine learning model of NAFLD prediction using multiple longitudinal data points 12 of all health-care encounters of a well-characterized population-based cohort of individuals diagnosed with 13 NAFLD in reference to individuals without NAFLD from the general population. We hypothesize that 14 unsupervised machine learning can identify complex processes and patterns without a human's guidance and 15 discover early comorbidity clusters (“latent traits” present prior to NAFLD development) that reflect a phenotype 16 at risk to develop NAFLD later in life. Second, we will test and optimize the model for the prediction of patient 17 outcomes (development of cirrhosis, liver-related complications and death) in the NAFLD cohort. We 18 hypothesize that machine learning approaches could be used to further stratify patients into subgroups with 19 different disease trajectories, with the goal of identifying those individuals at risk of progressive NAFLD and 20 liver-related outcomes. The research proposed in this application is innovative because it expands the 21 analytical toolbox beyond conventional methods to identify individuals with NAFLD using all health-encounters 22 of a large, well-characterized population-based cohort with long follow-up. This proposal is significant because 23 it addresses a critical need of identification and management of the most prevalent chronic liver disease and 24 offers a practical solution to large scale implementation of screening and risk-stratification strategies using 25 routinely collected data. The ultimate goal of this proposal is to improve the population health in obesity- 26 associated diseases.

1 2 项目概要/摘要 3 4 非酒精性脂肪性肝病 (NAFLD) 治疗中最关键的差距之一是缺乏有效的治疗方法 人群早期识别的 5 种方法。本研究的目的是利用数据和分析 6 在肝脏相关疾病发生之前，通过早期发现 NAFLD 并对其进行风险分层，改善医疗保健结果 7 并发症。人工智能在大型电子健康记录中的应用有潜力识别 发病前的8种疾病特征。该提案的中心假设是有针对性的筛查 应用于大型综合医疗数据集的 9 种机器学习模型可以识别患有 NAFLD 的个体 10，更具体地说，是那些具有进行性表型的人。我们将在 2 个具体方面检验中心假设 11 个目标。首先，我们将使用多个纵向数据点训练 NAFLD 预测的机器学习模型 在一个基于人群的明确特征的被诊断患有此病的个体队列中，有 12 次就诊 13 NAFLD 指一般人群中没有 NAFLD 的个体。我们假设 14 无监督机器学习可以识别复杂的流程和模式，无需人类指导和 15 发现反映表型的早期合并症群（NAFLD 发展之前存在的“潜在特征”） 16 人在以后的生活中有患 NAFLD 的风险。其次，我们将测试和优化患者预测模型 NAFLD 队列中有 17 种结果（肝硬化的发展、肝脏相关并发症和死亡）。我们 18 假设机器学习方法可用于进一步将患者分为具有以下特征的亚组： 19 种不同的疾病轨迹，旨在识别那些有进展性 NAFLD 风险的个体 20 个与肝脏相关的结果。本申请中提出的研究具有创新性，因为它扩展了 超越传统方法的 21 个分析工具箱可利用所有健康状况来识别 NAFLD 患者 22 是一个大型、特征良好的基于​​人群的队列，并进行了长期随访。这个提议意义重大，因为 23 它解决了识别和管理最流行的慢性肝病和 24 为大规模实施筛查和风险分层策略提供了实用的解决方案，使用 25 例行收集的数据。该提案的最终目标是改善肥胖人群的健康—— 26种相关疾病。

项目成果

It is time to expand the fatty liver disease community of practice.

• DOI: 10.1097/hep.0000000000000411
• 发表时间: 2023-11-01
• 期刊: Hepatology (Baltimore, Md.)