Application of Machine Learning Algorithms to Thiopurine Monitoring in IBD

机器学习算法在 IBD 硫嘌呤监测中的应用

• 批准号: 8516056
• 负责人: Peter D.R. Higgins
• 金额: $ 38.03万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8516056
• 关键词: Address; Advertisements; Algorithms; Architecture; Bioinformatics; Biological Assay; Biological Markers; Blood; Blood Chemical Analysis; Books; Businesses; Caring; Chemistry; Clinic; Clinical; Clinical Data; Clinical Informatics; Clinical Laboratory Information Systems; Collaborations; Computers; Data; Data Analyses; Data Set; Databases; Diagnostic; Dose; Effectiveness; Fraud; Goals; Gold; Healthcare; Healthcare Systems; Immune system; Immunocompromised Host; Immunologic Monitoring; Immunosuppression; Inflammation; Inflammatory Bowel Diseases; Informatics; Internet; Intestines; Investments; Laboratories; Lead; Life; Machine Learning; Medical; Medical Electronics; Medicine; Methods; Michigan; Monitor; National Cancer Institute; Pathology; Patient Care; Patients; Pattern; Pharmaceutical Preparations; Publishing; Recommendation; Secure; Serum; Techniques; Technology; Testing; Therapeutic Index; Therapeutic immunosuppression; United States; United States National Institutes of Health; Universities; base; clinical care; clinical practice; cluster computing; cost; data exchange; digital; dosage; improved; innovation; novel; prognostic; response; success; thiopurine

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.

描述（由申请人提供）：在常规医疗保健期间，以血数，血液化学和其他生物标志物的形式收集大量数据。尽管进行了巨大的投资，但对该数据的解释几乎没有努力。在医学外，大型数据集分析的一场革命是由机器学习技术驱动的，从识别信用卡欺诈到提出账簿购买建议等不同的应用程序中。尽管在NIH路线图计划中生物信息学的突出是显着的，但这些显着的进步对医疗保健的影响很小。该建议的广泛，长期目标是优化，实施，测试和分发机器学习算法，这些算法将利用大型数据集中的模式来提高医学的诊断和预后精度。 我们建议将硫嘌呤治疗期间免疫抑制作用的监测作为炎症性肠病作为示范病例。低治疗指数使得优化硫嘌呤剂量用于炎症性肠病非常重要，而血清代谢产物的测定有限。我们的初步数据表明，机器学习算法可用于实质上提高预后准确性并降低监测硫嘌呤使用的成本。该提议的中心假设是，血液计数和血液化学的模式与硫嘌呤药物有效抑制的血液化学分子有效，可用于指导药物给药。该假设的理由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重大变化。其次，已发表的数据表明，硫嘌呤代谢物测试较少，在指导硫嘌呤的剂量调整方面相当有效。 该申请提出了改进的硫嘌呤监测算法的优化，实施和测试，以及通过国家癌症研究所支持的Liddex（实验室信息数字数据交换）建筑的全国范围内提供优化算法。该提案的具体目的是：（1）使用纵向临床数据和新颖的数学方法来改善现有的算法，以对硫嘌呤治疗进行临床反应，使用肠炎作为黄金标准的客观证据； （2）前瞻性测试硫嘌呤监测算法是否可以准确地分类免疫抑制的IBD患者和不遵守硫嘌呤药物的患者，以及这些算法是否可以前瞻性指导患者的硫嘌呤给药； （3）使用LIDDEX网格体系结构在Web服务器上实现这些修订的算法，以启用全国临床使用，并在Ann Arbor VA IBD诊所中进行现场测试。拟议的研究将直接影响整个美国的患者护理，并通过证明这种信息学结构的有效性，刺激了生物信息学在临床护理中的进一步创新和应用。