Digital Phenotyping of Nonalcoholic Fatty Liver Disease

1 2项目概要/摘要 3 4非酒精性脂肪性肝病（NAFLD）管理中最关键的差距之一是缺乏有效的治疗方法。 5种早期识别方法。本研究的目的是利用数据和分析， 6.在肝脏相关疾病发生前，通过NAFLD的早期检测和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可能识别 发病前的8种疾病特征。这项建议的中心假设是， 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NAFLD患者 10，更具体地说，那些具有进行性表型的。我们将在2个具体的实验中检验中心假设。 11个瞄准器。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 12的所有卫生保健遇到的一个良好的特征人群为基础的队列的个人诊断为 13 NAFLD与普通人群中无NAFLD的个体有关。我们假设 无监督机器学习可以在没有人类指导的情况下识别复杂的过程和模式， 15发现反映表型的早期共病簇（在NAFLD发展之前存在的“潜在性状”） 16在以后的生活中有发展NAFLD的风险。其次，我们将测试和优化模型，用于预测患者 NAFLD队列中的17个结局（肝硬化、肝脏相关并发症和死亡）。我们 18假设机器学习方法可用于将患者进一步分层为亚组， 19种不同的疾病轨迹，目的是确定那些有进展性NAFLD风险的个体， 20个肝脏相关结果。本申请中提出的研究是创新的，因为它扩展了 21个超越传统方法的分析工具箱，使用所有健康接触来识别NAFLD患者 22个大型的、特征良好的基于人群的队列，长期随访。这一建议意义重大，因为 它解决了识别和管理最流行的慢性肝病的迫切需要， 24为大规模实施筛查和风险分层策略提供了一个实用的解决方案， 25个常规数据。该提案的最终目标是改善肥胖人群的健康状况- 26种相关疾病

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.