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Critical Care Informatics

重症监护信息学

基本信息

批准号：
10772272
负责人：
Leo Anthony G Celi
金额：
$ 39.75万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10772272
关键词：
Address Adoption Affect Algorithms Artificial Intelligence Award Behavioral Sciences COVID-19 Caring Classification Clinical Clinical Data Clinical Research Clip Code Community Health Critical Care Data Data Element Data Science Data Scientist Data Set Data Sources Databases Detection Development Discrimination Documentation Education Electronic Health Record Engineering Equity Ethics Ethnic Origin Face Funding Grant Guidelines Health Health Sciences Healthcare Hospitals Human Image Informatics Intensive Care Intensive Care Units Knowledge Koreans Laboratories Learning Link Machine Learning Maps Masks Medical Medical Imaging Medicine Methods Modality Modeling National Institute of Biomedical Imaging and Bioengineering Ontology Outcome Paper Patients Physiology Portuguese Process Professional Organizations Public Health Public Health Applications Research Public Health Informatics Publications Publishing Qualitative Research Quality of Care Race Research Research Personnel Resources Shapes Social Sciences Societies Source Students Techniques Test Result Testing Textbooks Thoracic Radiography Translating United States Universities University Hospitals Writing algorithmic bias base clinical care clinical center clinical predictors data science education design eHealth global health health care settings health data health disparity health equity implementation framework improved mHealth machine learning algorithm machine learning method model development mortality multidisciplinary novel diagnostics novel therapeutic intervention online course outcome disparities outreach pandemic disease population based population health prevent radiological imaging research study secondary analysis social health determinants sociodemographics success

项目摘要

Abstract This is a renewal application for the Critical Care Informatics grant (NIBIB R01 EB017205) that was awarded to the Laboratory for Computational Physiology (LCP) in 2014. This grant has supported the development of the Medical Information Mart for Intensive Care (MIMIC) research database, which is a de-identified database for research and health data science education around the world. We aim to address the foremost issues in machine learning in healthcare today, focusing on health disparities, algorithmic bias, and understanding the barriers to effective and equitable implementation of algorithmic models. Our proposal will enrich MIMIC with new data types and sources, including publicly available population health datasets, advance progress on a federated critical care dataset, and add a new module with COVID-19 specific ontology and codes. Our research will build on prior findings showing the pervasiveness of hidden socio-demographic bias in data sources including clinical data, medical images, and narrative patient documentation. Health care data science ultimately exists for the purpose of improving human health. Yet, extremely few models published in research papers have impacted clinical care due to challenges in implementation. In layman’s terms, this means knowledge gained about tests and treatments leads to the best possible outcome for every patient in the intensive care unit (ICU) regardless of demographic. We will conduct a rigorous qualitative research study to better understand the barriers faced by key stakeholders - clinicians and data scientists - in the development and implementation of equity-centered artificial intelligence. This information will be used to develop guidelines to integrate with implementation science frameworks to support the effective implementation of equitable AI in clinical settings. With these aims, MIMIC will significantly expand the relevance of this research resource to a greater diversity of investigators including those in the social and behavioral sciences and public health and continue to be a resource for clinical research and increasingly sophisticated model development, advancing our understanding of critical issues of fairness and equity in healthcare, data science, and the broader society.

摘要