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Last Posted: Aug 16, 2022
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The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU
JD Moorman, NPJ Digital Medicine, March 31, 2022

In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made real, for the first time that we know of, the promise that early detection of illness would allow earlier and more effective intervention and improved patient outcomes.

Artificial Intelligence in Medicine and Public Health: Prospects and Challenges Beyond the Pandemic
D Rasooly et al, CDC Blog Post, March 1, 2022 Brand

A recent Nature Medicine article discusses promising uses of artificial intelligence in medicine, particularly in medical imaging and big data integration, and considers technical and ethical challenges for their applications in improving human health. Here is a quick summary of the review and the implications for population health.

Harnessing big data to characterize immune-related adverse events
Y Jing et al, Nat Rev Clin Oncology, January 2022

We summarize the advantages and shortcomings of different sources of ‘big data’ for the study of irAEs and highlight progress made using such data to identify biomarkers of irAE risk, evaluate associations between irAEs and therapeutic efficacy, and characterize the effects of demographic and anthropometric factors on irAE risk. Harnessing big data will accelerate research on irAEs and provide key insights that will improve the clinical management of patients receiving ICIs.

Using big data and mobile health to manage diarrheal disease in children in low-income and middle-income countries: societal barriers and ethical implications
KH Keddy et al, Lancet Inf Dis, December 13, 2021

Diarrhea is an important cause of morbidity and mortality in children from low-income and middle-income countries (LMICs), despite advances in the management of this condition. Understanding of the causes of diarrhea in children in LMICs has advanced owing to large multinational studies and big data analytics computing the disease burden, identifying the important variables that have contributed to reducing this burden. The advent of the mobile phone has further enabled the management of childhood diarrhea by providing both clinical support to health-care workers/

Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.