PLOS Medicine has published a special issue on machine learning in health and biomedicine. Below are titles. The full issue is available here: https://collections.plos.org/mlforhealth
Editorial: Advancing the beneficial use of machine learning in health care and medicine: Toward a community understanding
Linda Nevin, on behalf of the PLOS Medicine Editors
Deep learning and artificial intelligence in radiology: Current applications and future directions
Koichiro Yasaka, Osamu Abe
Koichiro Yasaka and Osamu Abe discuss recent developments at the forefront of deployment of deep learning within radiology, including six research studies in the current PLOS Collection on Machine Learning in Health and Biomedicine. Part of the Special Issue on Machine Learning in Health and Biomedicine.
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Ahmed Hosny, Chintan Parmar, Thibaud P. Coroller, Patrick Grossmann, Roman Zeleznik, Avnish Kumar, Johan Bussink, Robert J. Gillies, Raymond H. Mak, Hugo J. W. L. Aerts
Hugo Aerts and colleagues evaluate the ability of deep learning networks to extract relevant features from computed tomography lung cancer images and stratify patients into low and high mortality risk groups. Part of the Special Issue on Machine Learning in Health and Biomedicine.
Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study
Shane Nanayakkara, Sam Fogarty, Michael Tremeer, Kelvin Ross, Brent Richards, Christoph Bergmeir, Sheng Xu, Dion Stub, Karen Smith, Mark Tacey, Danny Liew, David Pilcher, David M. Kaye
In their study, Shane Nanayakkara and colleagues find that machine learning-based models enhance predictive discrimination for mortality following cardiac arrest compared to existing approaches. Part of the Special Issue on Machine Learning in Health and Biomedicine.
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HIFA profile: Neil Pakenham-Walsh is coordinator of the HIFA global health campaign (Healthcare Information For All - www.hifa.org ), a global community with more than 18,000 members in 177 countries, interacting on six global forums in four languages. Twitter: @hifa_org FB: facebook.com/HIFAdotORG /orcid.org/0000-0001-9557-1487 email@example.com