Studying pseudogout using natural language processing and novel imaging approaches

使用自然语言处理和新颖的成像方法研究假性痛风

• 批准号: 10292824
• 负责人: Sara K. Tedeschi
• 金额: $ 5.4万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10292824
• 关键词: Affect; Algorithms; American; Arthritis; Award; Bioinformatics; Biological Markers; Clinical Research; Clinical Sciences; Cohort Studies; Complement; Computerized Medical Record; Data; Data Analyses; Data Set; Development; Diagnosis; Environment; Equipment; Flare; Foundations; Funding; Future; Goals; Grant; Healthcare; Hospitals; Hypersensitivity; Immunology; Infrastructure; Intervention; Knowledge; Lead; Link; Machine Learning; Manuscripts; Medicare claim; Medicine; Mentored Patient-Oriented Research Career Development Award; Mentors; Methods; Morbidity - disease rate; Natural Language Processing; Outcome; Outcome Measure; Pain; Parents; Performance; Physicians; Prevention strategy; Productivity; Pseudogout; Public Health Schools; Research; Research Personnel; Resources; Rheumatology; Risk Factors; Roentgen Rays; Serum; Testing; Time; Training; Ultrasonography; United States National Institutes of Health; Wages; Woman; Work; X-Ray Computed Tomography; cardiovascular disorder risk; career; career development; crystallinity; experience; imaging approach; imaging modality; instructor; medical schools; musculoskeletal imaging; musculoskeletal ultrasound; novel; patient oriented; predictive modeling; prevent; prognostic; prospective; ranpirnase; recruit; research and development; stem

PROJECT SUMMARY/ABSTRACT (FROM FUNDED PARENT K23 APPLICATION) Candidate: Dr. Tedeschi is an Instructor in Medicine at Harvard Medical School (HMS) and Associate Physician in the Division of Rheumatology, Immunology and Allergy’s Section of Clinical Sciences (SCS) at Brigham and Women’s Hospital (BWH). She received an MPH from the Harvard T.H. Chan School of Public Health (HSPH). Her 10 first-author manuscripts, two BWH grants, and foundation award exemplify her productivity and commitment to research. She has assembled an experienced team of mentors and collaborators, led by Dr. Daniel Solomon (primary mentor) and Drs. Katherine Liao and Karen Costenbader (co-mentors), to guide her training in natural language processing and machine learning approaches for clinical research, analysis of linked electronic medical record (EMR) and Medicare claims data, and interpretation of advanced imaging modalities. Focused coursework at HSPH and HMS will complement the experience she gains through her proposed studies of pseudogout risk factors and long-term outcomes. Training in dual- energy CT and ultrasound interpretation for crystalline arthritis will be obtained via one-on-one sessions. Her long-term career goal is to become an independent patient-oriented investigator focused on pseudogout. Environment: Dr. Tedeschi has a commitment from her Division for >75% protected time for research and career development activities during the K23 award period. Support from the Division and her primary mentor’s research funds will supplement her salary and project-related expenses. The SCS, a collaborative clinical research group in the Division of Rheumatology, has extensive infrastructure including the VERITY Bioinformatics Core (NIH-P30-AR072577, PI: Solomon) that will provide resources and expertise for the proposed studies. In addition, the BWH Arthritis Center is one of the largest nationally, facilitating subject recruitment, and the BWH Division of Musculoskeletal Imaging has state-of-the-art equipment and expertise applying dual-energy CT in crystalline arthritis. Coursework at HSPH and HMS, adjacent to BWH, will provide training necessary for Dr. Tedeschi’s development into an independent investigator. Research: Dr. Tedeschi’s long-term objective is to prevent and reduce morbidity from pseudogout, an understudied, painful crystalline arthritis that affects 8-10 million Americans. She will use natural language processing and machine learning approaches to enhance an algorithm for identifying pseudogout in EMR data. She will study risk factors for and long-term outcomes in pseudogout, harnessing vast amounts of information contained in Partners HealthCare EMR data and Medicare claims data, and will gain experience working with linked datasets. Dr. Tedeschi will recruit subjects with pseudogout and other types of mono- and oligoarthritis to test and compare the performance of dual-energy CT scanning, musculoskeletal ultrasound, and x-ray for identifying pseudogout. Her proposed K23 projects will lead to manuscripts and data to be leveraged in an R01 application focused on pseudogout during the award period, leading to independence as a patient-oriented investigator.

项目摘要/摘要（来自获资助家长k23申请书）