Results 211 to 220 of about 67,619 (257)

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis. [PDF]

BioData Min
Hao L, Wang Z, Zhu Y, Wang X, Zhu M, Mahemuti K, Guan Y.   +6 more
europepmc   +1 more source

Interpretable machine learning for thermoelectric materials design with Kolmogorov-Arnold networks. [PDF]

Sci Rep
Fronzi M, Ford MJ, Nayal KS, Isayev O, Stampfl C.   +4 more
europepmc   +1 more source

Interpretable machine learning prognostication of gastroenteropancreatic neuroendocrine tumors across Chinese and United States cohorts. [PDF]

Front Oncol
Fan Y, Yang Y, Wang K, Zhang T, Dong X, Shi Y, Wang M, Li E, Wu Y.   +8 more
europepmc   +1 more source

Development and validation of a predictive model for myocardial injury in children with pneumonia based on interpretable machine learning. [PDF]

Front Pediatr
Zhang S, Zhang J.
europepmc   +1 more source

Interpretable machine learning for coeliac disease diagnosis: quantitative morphometry of duodenal biopsies

Bryant R, Diaz JR, Scott AG, Sagdeo AA, Jenkins GZ, Richardson RA, Chan JYH, Arends MJ, Soilleux EJ, Jaeckle F.   +9 more
europepmc   +1 more source

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Interpretable Machine Learning in Healthcare

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2018
This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of
Muhammad Aurangzeb Ahmad, Ankur Teredesai, Carly Eckert   +2 more
openaire   +2 more sources

Interpretable machine learning with reject option

at - Automatisierungstechnik, 2018
Abstract Classification by means of machine learning models constitutes one relevant technology in process automation and predictive maintenance. However, common techniques such as deep networks or random forests suffer from their black box characteristics and possible adversarial examples.
Brinkrolf, Johannes, Hammer, Barbara
openaire   +3 more sources