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.
The emergence of electronic health records exposes clinicians to massive amounts of information about the millions of patients who suffer from communication and swallowing disorders, yet most clinical scientists do not have the training or skill to apply meaning to the data toward improving patient care. The overarching mentorship goal of this proposal is to train early, patient-oriented communication and swallowing scientists in big data analyses, including computer machine learning approaches. The research project will uncover distinct patterns and severity of swallowing impairments in large groups of patients with high risk medical diagnoses, which will have high impact on patient care planning and identification of treatments that directly target these impairments for improved outcomes.
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|O'Rourke, A; Humphries, K; Lazar, A et al. (2017) The pharyngeal contractile integral is a useful indicator of pharyngeal swallowing impairment. Neurogastroenterol Motil 29:|
|McGrattan, Katlyn Elizabeth; McGhee, Heather; DeToma, Allan et al. (2017) Dysphagia in infants with single ventricle anatomy following stage 1 palliation: Physiologic correlates and response to treatment. Congenit Heart Dis 12:382-388|
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