SBIR Phase I: Robust Medical Data Aggregation to Enable Advanced Approaches to Precision Medicine

SBIR 第一阶段：强大的医疗数据聚合，实现精准医疗的先进方法

• 批准号: 1721343
• 负责人: Ganapati Srinivasa
• 金额: $ 22.49万
• 依托单位: Omics Data Automation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1721343&HistoricalAwards=false
• 关键词: SBIR; Phase; Robust; Medical; Data

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enhance the impact of precision medicine by simultaneously addressing large-scale medical data aggregation and optimized computation that is cost-effective and to extend the utility of medical informatics well beyond current practice. Patient medical information comes in many diverse forms: genomic sequences, medical images, and clinical observations. The integration of these various data sources across patient populations have shown to reveal patterns and similarities among patients, which inform treatment options. With advances in imaging and genomic sequencing technologies, the sheer volume of available information is growing exponentially, straining current computational approaches, and creating an imminent need for scalable data integration. The ability to overcome this data mountain opens the door to support advanced analytics to support precision medicine and provide enhanced services to medical institutions. With these innovations, patients receive faster and more accurate diagnoses and treatments, clinicians deliver verified treatment decisions through patient cohort comparison, hospitals have better standard of care, and society is overall empowered by supporting global treatment options and well informed pharmaceutical development.The proposed project will develop a scalable aggregation and analysis framework to integrate various patient data modalities to inform personalized diagnosis and therapy in precision medicine. Currently, information from different modalities exists in silos, hindering joint analysis and insight. While there has been research trying to leverage machine learning techniques in medical imaging, these efforts have generally focused on a single domain and not been able to integrate facts from other domains. This project will aggregate features from genomics, imaging and clinical characterization of patients into scalable databases and then use a distributed, parallel framework to enable efficient analytics on the resultant joint representation. The resulting platform will enable identification of cohorts based on both genotypes and phenotypes and empower powerful machine learning analyses to inform clinical decision systems or identification of new personalized therapies.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力是通过同时解决大规模医疗数据聚合和具有成本效益的优化计算来增强精准医疗的影响，并扩展医疗的效用信息学远远超出了当前的实践。患者医疗信息有多种形式：基因组序列、医学图像和临床观察。患者群体中这些不同数据源的整合已显示出患者之间的模式和相似性，从而为治疗选择提供信息。随着成像和基因组测序技术的进步，可用信息的数量呈指数级增长，对当前的计算方法造成压力，并迫切需要可扩展的数据集成。克服数据山的能力为支持高级分析打开了大门，以支持精准医疗并为医疗机构提供增强的服务。通过这些创新，患者可以获得更快、更准确的诊断和治疗，临床医生通过患者队列比较提供经过验证的治疗决策，医院拥有更好的护理标准，并且通过支持全球治疗方案和消息灵通的药物开发来全面增强社会的能力。拟议的项目将开发一个可扩展的聚合和分析框架，以集成各种患者数据模式，为精准医学中的个性化诊断和治疗提供信息。目前，来自不同模式的信息存在各自为政的情况，阻碍了联合分析和洞察。虽然已经有研究试图在医学成像中利用机器学习技术，但这些努力通常集中在单个领域，而无法整合其他领域的事实。该项目将把患者的基因组学、成像和临床特征的特征聚合到可扩展的数据库中，然后使用分布式并行框架来对由此产生的联合表示进行有效分析。由此产生的平台将能够根据基因型和表型识别群体，并支持强大的机器学习分析，为临床决策系统或新的个性化疗法的识别提供信息。