AIMC Topic: Data Science
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Experimenting with Generative Adversarial Networks to Expand Sparse Physiological Time-Series Data.
Studies in health technology and informatics
Jun 23, 2020
Machine Learning research and its application have gained enormous relevance in recent years. Their usage in medical settings could support patients, increase patient safety and assist health professionals in various tasks. However, medical data is o...
Big Data, Big Impact: The Potential for Data Science in Neurosurgery.
World neurosurgery
Jun 1, 2020
Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.
Journal of the American Medical Informatics Association : JAMIA
Jun 1, 2020
OBJECTIVE: Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We dev...
Connecting Data to Value: An Operating Model for Healthcare Advanced Analytics.
Healthcare quarterly (Toronto, Ont.)
Apr 1, 2020
Artificial intelligence offers the promise to revolutionize the way healthcare is delivered in the future. To capitalize on the value of advanced analytics and artificial intelligence, organizations must focus on building organizational capabilities....
Avoiding common pitfalls in machine learning omic data science.
Nature materials
May 1, 2019
ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2.
Journal of animal science
Apr 29, 2019
This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate r...
The Role of the ACR Data Science Institute in Advancing Health Equity in Radiology.
Journal of the American College of Radiology : JACR
Apr 1, 2019
Commercially available artificial intelligence (AI) algorithms outside of health care have been shown to be susceptible to ethnic, gender, and social bias, which has important implications in the development of AI algorithms in health care and the ra...
AI, Machine Learning, and Ethics in Health Care.
The Journal of legal medicine
Jan 1, 2019
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Ripe for Disruption? Adopting Nurse-Led Data Science and Artificial Intelligence to Predict and Reduce Hospital-Acquired Outcomes in the Learning Health System.
Nursing administration quarterly
Jan 1, 2019
Nurse leaders are dually responsible for resource stewardship and the delivery of high-quality care. However, methods to identify patient risk for hospital-acquired conditions are often outdated and crude. Although hospitals and health systems have b...
Data science and artificial intelligence to improve clinical practice and research.
Journal of the American Medical Informatics Association : JAMIA
Oct 1, 2018
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