BIGDATA: Small: DA: Patient-level predictive modeling from massive longitudinal databases

大量的纵向医疗保健数据，如行政索赔和电子健康记录，提供了一个机会，大大提高了患者水平预测的准确性和临床影响，涵盖了广泛的结果。这项研究针对医疗保健IT的国家优先领域，并展示了大数据在帮助患者做出明智的医疗保健决策方面的进步，从而改善了结果。其他参与的利益相关者包括医疗保健提供者，保险公司和政府机构，而这项拟议的赠款所使用的数据库涵盖了各种脆弱的患者群体，包括年轻人，穷人和老年人。在此背景下，该补助金旨在根据个人特征和条件预测患者水平的健康事件。准确和校准良好的预测可以显着改善患者和人群的福祉。这项资助计划从大量的观察数据中推导出预测模型，然后预测某个特定的患者在未来12个月内有18%的可能性发生中风。有了这个预测，护理人员和患者可以优化医疗干预措施，并实施行为改变，以防止预测的事件。此外，该资助整合了两名研究生研究人员，他们的指导经验开始纠正了在统计学和医学交叉领域培训的数据科学家的短缺，并提供了通用统计软件工具，用于从跨科学领域的海量数据中构建大规模预测模型。拟议的赠款旨在首先评估现有预测模型在涵盖8000多万人的五个行政索赔和电子健康记录数据库中的性能和适用性，使用CHADS 2中风风险作为激励示例。然后，该基金将开发一种创新的数据驱动过程，用于从纵向观察数据构建患者水平的预测模型，并初步将该过程应用于预测房颤患者的卒中，以与CHADS 2进行性能比较，最后，该基金旨在探索该过程和所得模型的特征，例如：在不同数据库中评估样本外预测性能;考虑模型如何随时间变化;以及评估哪些临床变量对患者水平的预测最有贡献。总之，这项研究将侧重于确定从纵向电子医疗保健数据中提取临床相关预测因子的算法，开发算法，通过使用图形处理单元的大规模并行化在多变量建模中使用这些信息，优化数据稀疏性，并根据患者水平预测结果的准确性评估性能。作为概念验证，该补助金将开发一种方法来预测中风风险，并将这种方法应用于五个不同的数据源（8000多万患者，包括药物，实验室值，程序，急诊室就诊，初级保健就诊，住院患者等），这些数据源反映了美国各地的不同患者人群，包括私人保险，Medicare资格和Medicaid受益人。该补助金的基本目标是将创新的统计和机器学习技术应用于大规模观察数据，利用先进的计算机技术开发准确和校准良好的患者水平预测模型，从而预测个体患者未来的医疗事件。

Massive longitudinal healthcare data, such as administrative claims and electronic health records, provide an opportunity to greatly enhance the accuracy and clinical impact of patient-level predictions across a wide range of outcomes. This research targets the national priority domain of healthcare IT and showcases the advances that Big Data afford in helping patients make informed healthcare decisions leading to improved outcomes. Other involved stakeholders include healthcare providers, insurers and governmental agencies, and the databases this proposed grant employs encompass diverse and vulnerable patient populations, including the young, the poor and the elderly. Within this context, this grant is seeking to predict patient-level health events based upon personal characteristics and conditions. Accurate and well-calibrated predictions could significantly improve the wellbeing of patients and populations. This grant proposes to derive predictive models from massive observational data and then, for example, predict that a particular patient has an 18% chance of experiencing a stroke in the next 12 months. With this prediction in hand, caregivers and patients can optimize medical interventions and implement behavioral changes to hopefully prevent the predicted event. Further, this grant integrates two graduate student researchers, whose mentored experiences begin to rectify the shortage of data scientists trained at the intersection of statistics and medicine, and provides general statistical software tools for building large-scale predictive models from massive data across scientific domains.From a technical perspective, the proposed grant aims to first evaluate performance and applicability of an existing predictive model across five administrative claims and electronic health record databases covering over 80 million lives, using CHADS2 stroke risk as a motivating example. Then the grant will develop an innovative data-driven process for building patient-level predictive models from longitudinal observational data, and initially apply the process to predicting stroke in patients with atrial fibrillation for comparison of performance against CHADS2, Finally, the grant aims to explore characteristics of the processand resulting models, such as: evaluation of out-of-sample predictive performance in different databases; consideration of how models change over time; and assessment of which clinical variables most substantially contribute to patient-level predictions. Together, this research will focus on identifying heuristics to extract clinically relevant predictors from longitudinal electronic healthcare data, developing algorithms to use this information in multivariate modeling through massive parallelization using graphics processing units, optimized for data sparsity, and evaluating performance based on accuracy in predicting outcomes at the patient level. As a proof-of-concept, the grant will develop an approach to predict stroke risk and apply this approach across five disparate data sources (80+ million patients, including drugs, lab values, procedures, emergency room visits, primary care visits, inpatient encounters, etc) that reflect diverse patient populations across the US, including the privately insured, Medicare-eligible, and Medicaid beneficiaries. The underlying goal of the grant is to apply innovative statistical and machine learning techniques using advancing computer technology to large-scale observational data to develop accurate and well-calibrated patient-level predictive models enabling the prediction of future medical events for individual patients.