Data Science Applications in Communication andSwallowing Disorders

数据科学在沟通和吞咽障碍中的应用

• 批准号: 10322652
• 负责人: BONNIE J MARTIN-HARRIS
• 金额: $ 18.32万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322652
• 关键词: Algorithms; Area; Award; Barium swallow; Big Data; Big Data Methods; Bioinformatics; Biological; Biomedical Research; Biometry; Chronic Obstructive Pulmonary Disease; Classification; Clinical; Communication; Communication impairment; Computer Vision Systems; Computerized Medical Record; Computers; Data; Data Analyses; Data Science; Data Scientist; Data Set; Deglutition; Deglutition Disorders; Dementia; Development; Diagnosis; Disease; Economics; Electronic Health Record; Engineering; Explosion; Funding; Geography; Goals; Grant; Head and Neck Cancer; Health; Hearing; Impairment; Individual; Instruction; Intervention; Laboratories; Machine Learning; Medical; Medical Records; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Methods; Mid-Career Clinical Scientist Award (K24); Mission; Modernization; National Institute on Deafness and Other Communication Disorders; Nutritional; Outcome; Parkinson Disease; Patient Care; Patient Care Planning; Patients; Pattern; Phenotype; Physiological; Productivity; Publishing; Registries; Reproducibility; Research; Research Personnel; Research Project Grants; Sample Size; Science; Scientific Inquiry; Scientist; Secure; Severities; Speech; Standardization; Statistical Models; Strategic Planning; Stroke; Supervision; Techniques; Testing; Training; United States National Institutes of Health; Work; base; career; career development; clinical practice; clinically relevant; computer science; computerized; data registry; data resource; data warehouse; digital; experimental study; health assessment; high risk; human error; impression; improved; improved outcome; learning algorithm; novel; patient oriented; programs; research and development; skills; statistical learning; theories; uptake

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.

项目摘要/摘要 电子病历，大数据注册表和容易访问的受保护服务器的出现具有 导致数字信息的爆炸激增，对改善患者有潜在的临床影响很高 管理和结果。大数据仓库在范围内捕获标准信息 临床实践使训练有素的科学家不仅可以进行传统的假设检验，还可以 发现新的假设，完善现有理论并将新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，但临床科学家还是 传统上定向实验，这些实验包含了相对较小的样本量和个人的数据 实验室，尚未接受过大数据分析或参与适当的团队科学家的培训 在这个领域的工作，例如计算机科学家，生物统计学家和工程师。总体目标 建议是在大数据分析和 进行精神​​，并参与早期数据科学学者在沟通和吞咽研究中。 PI建议 该K24更新提案中的四个主要心态和研究目标：1。培训早期干部 专家，包括机器学习在内的数据科学方法中的沟通和吞咽科学家 跨学科，合作数据科学团队，2。参与并介绍早期职业数据科学家 生物统计学，计算机科学和工程学领域，用于沟通和吞咽科学，以及 各自的数据集，旨在促进跨学科数据科学团队和研究生产力，3。 新的数据科学方法，以识别吞咽障碍和严重性分类的表型 已知患者群体面临与吞咽困难有关的营养和健康并发症的高风险，4。 在机器学习应用中开发一个新的研究领域，以提高生理的可靠性 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列为其使命：存储，管理，标准化和 发布生物医学研究产生的大量数据。 NIDCD认识到可访问， 组织良好，安全且有效运行的数据资源对现代科学探究至关重要…… 通过最大化通过NIH资助的努力生成的数据的价值，生物医学发现的空间 并且可以加快医疗突破以提高健康状况。