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: 8238209
• 负责人: Peter D.R. Higgins
• 金额: $ 36.01万
• 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/8238209
• 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. PUBLIC HEALTH RELEVANCE: This project addresses the NIH Roadmap Initiative goal of the application of innovations in bioinformatics to bedside clinical practice. The proposed studies will directly impact the care of patients with inflammatory bowel disease (IBD) throughout the United States. We expect that the successful demonstration of the effectiveness of the LIDDEx clinical informatics architecture will have a broad impact on clinical care beyond IBD by spurring further innovation and application of bioinformatics to clinical care in a range of medical fields.

描述（由申请人提供）：在常规医疗护理期间，以血细胞计数、血液化学和其他生物标志物的形式收集大量数据。尽管有如此巨大的投资，但对这些数据的解释却很少。在医学之外，机器学习技术在各种应用中推动了大型数据集分析的革命，从识别信用卡欺诈到提出图书购买建议。尽管生物信息学在NIH路线图计划中占据重要地位，但这些显着的进步对医疗保健的影响甚微。该提案的广泛、长期目标是优化、实施、测试和在全国范围内分发机器学习算法，这些算法将利用大型数据集中的模式来提高医学诊断和预后的准确性。 我们建议使用硫嘌呤治疗炎症性肠病期间的免疫抑制监测作为示范案例。低治疗指数使得优化炎症性肠病的巯基嘌呤剂量变得重要，并且血清代谢物的测定益处有限。我们的初步数据表明，机器学习算法可用于大幅提高预后准确性，并降低监测硫嘌呤使用的成本。该建议的中心假设是，血细胞计数和血液化学中存在与巯基嘌呤药物有效免疫抑制相关的模式，可用于指导药物给药。这一假设的基本原理基于两个观察结果。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检测在指导硫嘌呤剂量调整方面相当有效。 该申请提出了一套改进的巯基嘌呤监测算法的优化，实施和测试，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的数学方法，以肠道炎症的客观证据为金标准，改进现有的巯基嘌呤治疗临床反应算法;（2）前瞻性地测试硫嘌呤监测算法是否可以准确地将免疫抑制的IBD患者和不依从硫嘌呤药物的患者分类，以及这些算法是否可以前瞻性地指导患者中硫嘌呤的给药;以及（3）使用LIDDEx网格架构在web服务器上实现这些修改的算法以使得能够在全国范围内临床使用，并且在安阿伯VA IBD诊所中现场测试该实现。拟议的研究将直接影响整个美国的患者护理，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。 公共卫生关系：该项目致力于将生物信息学创新应用于临床实践的NIH路线图倡议目标。拟议的研究将直接影响美国各地炎症性肠病（IBD）患者的护理。我们预计，LIDDEx临床信息学架构的有效性的成功证明将通过刺激生物信息学在一系列医学领域的进一步创新和应用，对IBD以外的临床护理产生广泛的影响。