Data Science Applications in Communication andSwallowing Disorders

项目摘要/摘要 电子病历、大型数据注册表和易于访问、受保护的服务器的出现 导致数字信息爆炸，对改善患者状况具有潜在的高临床影响 管理和结果。捕获范围内标准化信息的大数据仓库 临床实践允许训练有素的科学家不仅从事传统的假设检验，而且还 发现新的假设，完善现有的理论，并将新的发现应用于健康评估和 干预措施。尽管这些数据平台具有可访问性和潜在影响，但临床医生科学家 传统的指导性实验结合了相对较小的样本量和来自个人的数据 实验室，而且没有接受过大数据分析方面的培训，也没有接受过聘用合适的团队科学家的培训 在这个领域工作，如计算机科学家、生物统计学家和工程师。这件事的首要目标是 建议是指导早期以患者为导向的沟通，并在大数据分析和 指导并让早期数据科学学者参与交流和吞噬研究。私生党提议 K24更新提案中的四个主要指导和研究目标：1.培养早期干部 交流和接受科学家在数据科学方法，包括机器学习，由专家， 跨学科、协作的数据科学团队，2.吸引和介绍来自以下领域的早期职业数据科学家 从生物统计学、计算机科学和工程到通信和吞噬科学，以及 各自的数据集，以促进跨学科数据科学团队和研究生产力，3.应用 新的数据科学方法用于确定吞咽障碍的表型和严重程度分类 已知的与吞咽困难有关的营养和健康并发症的高风险患者组，以及4. 开发机器学习应用的新研究领域，以提高生理学的可靠性 吞咽评估。职业发展和研究计划的数据科学主题直接与 NIDCD的数据科学战略计划将其使命列为：存储、管理、标准化和 出版生物医学研究产生的海量数据。NIDCD认识到可访问的、 良好组织、安全和高效运行的数据资源是现代科学查询…的关键和 通过最大化通过NIH资助的努力产生的数据的价值，生物医学发现的步伐 医学上的突破可以加快，以改善健康状况。

PROJECT SUMMARY/ABSTRACT The emergence of electronic medical records, large data registries and readily accessible, protected servers have resulted in an explosion of digital information with potentially high clinical impact for improving patient management and outcomes. Big data warehouses that capture standardized information within the scope of clinical practices allow trained scientists to not only engage in traditional hypothesis testing, but to also uncover new hypotheses, refine existing theories and apply new discoveries to health assessments and interventions. Despite the accessibility and potential impact of these data platforms, clinician scientists have traditionally directed experiments that incorporate relatively small sample sizes and data from individual laboratories, and have not been trained in big data analytics or in engaging appropriate team scientists who work in this space, such as computer scientists, biostatisticians and engineers. The overarching goal of this proposal is to mentor early patient oriented communication and swallowing scientists in big data analytics and to mentor and involve early data science scholars in communication and swallowing research. The PI proposes four primary mentorship and research goals in this K24 renewal proposal: 1. Train a cadre of early stage communication and swallowing scientists in data science methods, including machine learning, by an expert, interdisciplinary, collaborative data science team, 2. Engage and introduce early career data scientists from fields of biostatistics, computer science and engineering to communication and swallowing sciences, and respective data sets, toward facilitating interdisciplinary data science teams and research productivity, 3. Apply novel data science methods to identify phenotypes of swallowing impairment and severity classifications in patient groups known to be at high risk for nutritional and health complications related to dysphagia, and 4. Develop a new area of research in machine learning applications toward improving reliability of physiologic swallowing assessment. The data science theme of the career development and research plan directly align with NIDCD's Strategic Plan for Data Science which lists as its mission: Storing, managing, standardizing and publishing the vast amounts of data produced by biomedical research. NIDCD recognizes that accessible, well-organized, secure and efficiently operated data resources are critical to modern scientific inquiry…and by maximizing the value of data generated through NIH-funded efforts, the pace of biomedical discoveries and medical breakthroughs for better health outcomes can be accelerated.