A Novel Probabilistic Methodology for Prediction of Emerging Diseases in Patients with Multiple Chronic Conditions

一种预测多种慢性病患者新发疾病的新概率方法

• 批准号: 9269622
• 负责人: Adel Alaeddini
• 金额: $ 14.7万
• 依托单位: UNIVERSITY OF TEXAS SAN ANTONIO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-04 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9269622
• 关键词: Address; Affect; Age; Algorithms; American; Bayesian Modeling; Big Data; Caring; Characteristics; Chronic; Chronic Disease; Clinical; Communities; Comorbidity; Complex; Coronary Arteriosclerosis; Data; Data Analytics; Data Set; Development; Disease; Economics; Education; Epidemiology; Ethnic Origin; Foundations; General Population; Goals; Health; Health Care Costs; Health Status; Healthcare; Heterogeneity; High Performance Computing; Hypertension; Individual; Intervention; Knowledge; Lead; Learning; Link; Machine Learning; Marital Status; Medical; Methodology; Methods; Modeling; Monitor; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Patients; Pattern; Population; Probability; Process; Public Health; Race; Records; Research Proposals; Resources; Risk; Risk Factors; Scheme; Statistical Models; Stroke; Techniques; Testing; Time; Weight; Work; base; behavioral health; data mining; improved; individual patient; learning strategy; multiple chronic conditions; multitask; novel; physical conditioning; population based; predictive modeling; public health relevance; sex; socioeconomics; young adult

 DESCRIPTION (provided by applicant): Treatment for people living with multiple chronic conditions (MCC) currently accounts for an estimated 66 percent of the Nation's health care costs and will continue to grow. This mounting challenge has become a major public health issue since MCC is linked to suboptimal health outcomes and rising health care costs. However, it now known how MCC emerge among individuals or in the general population. Traditional epidemiological approaches have led to important findings of disease links and comorbidity associations. However, they are limited in their ability to characterize how patients acquire new chronic conditions and predict/personalize the emergence of MCC for individual patients. Objectives: This study will develop approaches that can be used to identify the most likely combinations of comorbidity within a population. These approaches can be tailored to examine MCC patterns in specific sub- populations or at the level of the individual patient. We will also study the effect of a large set of risk factors on MCC combinations emergence. Furthermore, we will use data mining approaches to predict and monitor the development of MCC combinations in at the population and individual levels. Hypotheses: We hypothesize that the emergence and progression of comorbidities in MCC patients form patterns that can be predicted and which are associated with prior medical conditions, demographic, and socio-economic characteristics. We further hypothesize that these methods can predict the timing and emergence of new chronic diseases more accurately by personalizing the records for individual patients. Aims and methodology: in Aim1, we will characterize how MCC emerge and progress in distinct patterns and how these patterns transition between different combinations of diseases over time. We will then identify and group major MCC transitions using the Markov clustering (MCL) algorithm, which is a novel, efficient graphical approach to handle big data. In Aim 2, we will identify which risk factors are associated with MCC emergence using a machine learning approach that can handle the complex heterogeneity of comorbidity patterns. Risk factors include age, sex, race/ethnicity, education, economic status, marital status, and prior medical conditions. In Aim 3, we will use a similarity learning approach to develop models that can predict if MCC will emerge in individual patients or among populations and we will be able to use these models to monitor MCC emergence over time. Conclusion: Our findings will provide a foundation for future research that will evaluate specific treatment patterns associated with progression in MCC patterns and ultimately identify optimal time points of intervention for those with, or at risk for multiple chronic conditions. These findings will also provide information that can be used at the community level to manage healthcare resources to improve continuity and accessibility of care.

 描述（由申请人提供）：对患有多种慢性病（MCC）的人的治疗目前估计占全国医疗保健费用的66%，并将继续增长。这一日益严峻的挑战已成为一个重大的公共卫生问题，因为MCC与次优的健康结果和不断上升的医疗保健成本有关。然而，现在已经知道MCC是如何在个人或一般人群中出现的。传统的流行病学方法导致了关于疾病联系和共病关联的重要发现。然而，他们在表征患者如何获得新的慢性疾病和预测/个性化个体患者MCC的出现方面的能力有限。目的：本研究将开发可用于识别人群中最可能的合并症组合的方法。这些方法可以定制以检查特定亚群或个体患者水平的MCC模式。我们还将研究大量风险因素对MCC组合出现的影响。此外，我们将使用数据挖掘方法来预测和监测MCC组合在群体和个体水平上的发展。假设条件：我们假设MCC患者合并症的出现和进展形成了可以预测的模式，并且与既往医疗状况、人口统计学和社会经济特征相关。我们进一步假设，这些方法可以通过个性化个体患者的记录更准确地预测新慢性病的发生时间和出现。目标和方法：在目标1中，我们将描述MCC如何以不同模式出现和发展，以及这些模式如何随时间在不同疾病组合之间转变。然后，我们将使用马尔可夫聚类（MCL）算法来识别和分组主要MCC转换，这是一种处理大数据的新颖、高效的图形方法。在目标2中，我们将确定 风险因素与MCC的出现相关，使用机器学习方法，可以处理复杂的异质性共病模式。风险因素包括年龄、性别、种族/民族、教育、经济状况、婚姻状况和既往医疗状况。在目标3中，我们将使用相似性学习方法来开发模型，可以预测MCC是否会在个体患者或人群中出现，并且我们将能够使用这些模型来监测MCC随着时间的推移而出现。结论：我们的研究结果将为未来的研究提供基础，这些研究将评估与MCC模式进展相关的特定治疗模式，并最终确定对患有或有多种慢性疾病风险的患者进行干预的最佳时间点。这些发现还将提供可用于社区一级管理医疗资源的信息，以改善护理的连续性和可及性。