A New Generation Clinical Decision Support System

关键临床活动涉及决策。对于患者个人和整个社会来说， 良好的医疗保健决策是一项至关重要的任务。这项研究的目的是开发一种新的决策 支持系统，其利用乳腺癌患者的临床特征和基因组谱， 帮助医生整合关于特定患者的信息（诊断亚型、肿瘤分期和 等级、年龄、合并症）来为患者制定治疗计划。 传统的临床数据越来越多地以电子形式提供。空前 丰富的基因组数据可供研究人员作为先进的测序技术的结果， 作为下一代测序。患者特异性基因组数据很可能对大多数患者可用 在可预见的将来。这些数据来源为开发新的 第二代临床决策支持系统，可以实现实质性的进展， 可能然而，这些数据中变量的数量（通常以百万计） 提出了巨大的计算和建模挑战。此外，集成异构信息 在多个临床数据集和基因组数据集中呈现出艰巨的挑战。 乳腺癌是女性中最常见的癌症。各种乳腺癌亚型已经被 定义，其沿着肿瘤分期，预测对治疗的反应和存活，尽管不完全。为 例如，HER 2扩增的乳腺癌是一种预后不良的亚型， HER 2（赫赛汀）极大地改善了这些患者的生存率。虽然赫赛汀用于治疗 在所有HER 2扩增肿瘤患者中，只有部分患者有反应。此外，它是昂贵的，并可能导致心脏 毒性因此，重要的是只给从中受益的患者。研究表明， 与乳腺癌的亚型和预后相关，特定的等位基因组合可以有效地指导 选择有效的治疗方法。拟议中的系统将收集所有这些基因组信息，并将联合收割机 它具有临床信息，因此有望提供准确的分类和治疗选择。 我们将使用以下来源构建所提出的系统的知识库：1）医疗 匹兹堡大学医学中心的档案系统; 2）林恩使用的林恩塞奇数据库 西北纪念医院的Sage综合乳腺中心; 3）来自 癌症基因组图谱项目;和4）梦想7乳腺癌挑战数据。拟设系统可 建立在研究人员使用贝叶斯网络从高维数据中学习的先前结果的基础上 集.我们的多学科团队有着良好的记录，包括NIH的资助，生物医学领域的出版物， 信息学和人工智能，以及开发尖端决策支持系统的经验。

Critical clinical activities involve decision making. For both individual patients and for society at large, making good healthcare decisions is a paramount task. The objective of this research is to develop a novel decision support system that utilizes both the clinical features and the genomic profile of a breast cancer patient to assist the physician in integrating information about a specific patient (diagnostic subtype, tumor stage and grade, age, comorbidities) to make therapeutic plans for the patient. Traditional clinical data are becoming increasingly available in electronic form. Unprecedentedly abundant genomic data are available to researchers as the results of advanced sequencing technologies such as next generation sequencing. Patient-specific genomic data are likely to become available for most patients in the foreseeable future. These sources of data provide significant opportunities for developing new generation clinical decision support systems that can achieve substantial progress over what is currently possible. However, the sheer magnitude of the number of variables in these data (often in the millions) presents formidable computational and modeling challenges. Also, integrating the heterogeneous information in multiple clinical datasets and genomic datasets presents an arduous challenge. Breast cancer is the commonest cancer among women. Various breast cancer subtypes have been defined which, along with tumor stage, predict response to therapy and survival, albeit imperfectly. For example, HER2-amplified breast cancer is a subtype with poor prognosis, and therapy with an antibody to HER2 (Herceptin) has vastly improved the survival of such patients. Although Herceptin is used in the therapy of all patients with HER2-amplified tumors, only some respond. Also, it is expensive and can cause cardiac toxicity. So, it is important to give it only to patients benefiting from it. Studies show thousands of genes are associated with subtype and prognosis of breast cancer, and particular allele combinations may usefully guide the selection of effective treatment. The proposed system will amass all this genomic information and combine it with clinical information and therefore holds promise to provide accurate classification and treatment choices. We will build the knowledge base of the proposed system using the following sources: 1) The Medical Archival Systems at the University of Pittsburgh Medical Center; 2) The Lynn Sage Database used by the Lynn Sage Comprehensive Breast Center at Northwestern Memorial Hospital; 3) The breast cancer data sets from The Cancer Genome Atlas project; and 4) Dream 7 Breast Cancer Challenge Data. The proposed system will build on previous results of the investigators in using Bayesian Network to learn from high-dimensional data sets. Our multidisciplinary team has a track record, including NIH funding, publications in biomedical informatics and artificial intelligence, and experience developing cutting-edge decision support systems.