Application of Machine Learning Algorithms to Thiopurine Monitoring in IBD

描述(申请人提供)：在常规医疗过程中，以血细胞计数、血液化学和其他生物标记物的形式收集大量数据。尽管投入了巨大的资金，但对这些数据的解读却少之又少。在医学之外，从识别信用卡诈骗到为购书提供建议，机器学习技术在不同的应用领域推动了一场大数据集分析的革命。尽管生物信息学在NIH路线图计划中占有重要地位，但这些显著的进步对医疗保健几乎没有影响。这项提议的广泛、长期的目标是优化、实施、测试和在全国范围内分发机器学习算法，这些算法将利用大数据集中的模式来提高医学诊断和预测的准确性。我们建议将硫嘌呤治疗炎症性肠病过程中免疫抑制的监测作为一个示范案例。低的治疗指数使得优化硫嘌呤治疗炎症性肠病的剂量变得重要，而对血清代谢物的检测益处有限。我们的初步数据表明，机器学习算法可以用来显著提高预测准确性，并降低监测硫嘌呤使用的成本。这一建议的中心假设是，与硫代嘌呤药物有效抑制免疫有关的血细胞计数和血液化学指标存在模式，可用于指导药物剂量。这一假设的理论基础是基于两个观察结果。首先，我们的初步数据表明，机器学习可以通过分析实验室数据来识别免疫系统激活的显著变化。其次，已发表的数据表明，不太准确的硫嘌呤代谢物测试在指导硫嘌呤的剂量调整方面是相当有效的。这项申请建议优化、实施和测试一套改进的硫代嘌呤监测算法，并通过国家癌症研究所支持的LIDDEx(实验室信息数字数据交换)架构在全国范围内交付优化的算法。这项建议的具体目的是：(1)利用纵向临床数据和新颖的数学方法，以肠道炎症的客观证据为金标准，改进现有的硫嘌呤治疗临床反应算法；(2)前瞻性测试硫嘌呤监测算法是否能够准确地将免疫抑制的IBD患者和未坚持使用硫嘌呤药物的患者进行分类，以及这些算法是否能够前瞻性地指导患者使用硫嘌呤；以及(3)在使用LIDDEx网格架构的网络服务器上实施这些修订的算法，以实现全国临床应用，并在Ann Arbor VA IBD诊所进行现场测试。拟议中的研究将直接影响全美的患者护理，并通过展示这种信息学架构的有效性，刺激生物信息学在临床护理中的进一步创新和应用。

DESCRIPTION (provided by applicant): During routine medical care, enormous amounts of data are collected in the form of blood counts, blood chemistries, and other biomarkers. Despite this huge investment, remarkably little effort is applied to the interpretation of this data. Outside of medicine, a revolution in the analysis of large datasets has been driven by machine learning techniques in diverse applications ranging from identifying credit card fraud to making recommendations for book purchases. Despite the prominence of bioinformatics in the NIH Roadmap Initiative, these remarkable advances have had little impact on medical care. The broad, long-term objective of this proposal is to optimize, implement, test, and nationally distribute machine learning algorithms which will utilize patterns in large datasets to improve diagnostic and prognostic accuracy in medicine. We propose to use the monitoring of immune suppression during thiopurine therapy for inflammatory bowel disease as a demonstration case. A low therapeutic index makes it important to optimize thiopurine dosage for inflammatory bowel disease, and assays for serum metabolites are of limited benefit. Our preliminary data show that machine learning algorithms can be used to substantially improve prognostic accuracy and reduce costs in monitoring thiopurine use. The central hypothesis of this proposal is that there are patterns in the blood counts and blood chemistries associated with effective immune suppression by thiopurine medications which can be used to guide medication dosing. The rationale for this hypothesis is based on two observations. First, our preliminary data demonstrates that machine learning can identify significant changes in immune system activation through analysis of laboratory data. Second, published data suggests that the less accurate thiopurine metabolite tests are reasonably effective in guiding dose adjustment of thiopurines. This application proposes the optimization, implementation and testing of a improved set of thiopurine monitoring algorithms, and the nationwide delivery of the optimized algorithms through the National Cancer Institute-supported LIDDEx (Laboratory Information Digital Data Exchange) architecture. The specific aims of this proposal are to: (1) use longitudinal clinical data and novel mathematical methods to improve the existing algorithm for clinical response to thiopurine therapy, using objective evidence of bowel inflammation as the gold standard; (2) prospectively test whether the thiopurine monitoring algorithms can accurately classify IBD patients who are immunosuppressed and patients who are non-adherent to thiopurine medications, and whether these algorithms can prospectively guide dosing of thiopurines in patients; and (3) implement these revised algorithms on a web server using the LIDDEx grid architecture to enable nationwide clinical use, and field test this implementation in the Ann Arbor VA IBD clinic. The proposed studies will directly impact patient care throughout the United States, and by demonstrating the effectiveness of this informatics architecture, spur further innovation and application of bioinformatics to clinical care.