Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits.
Patterson, Brian W; Engstrom, Collin J; Sah, Varun; Smith, Maureen A; Mendonça, Eneida A; Pulia, Michael S; Repplinger, Michael D; Hamedani, Azita G; Page, David; Shah, Manish N.
; 57(7): 560-566, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31157707
Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm.
TraPy-MAC: Traffic Priority Aware Medium Access Control Protocol for Wireless Body Area Network.
Diabetes classification model based on boosting algorithms.
ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.
Multisensory System for the Detection and Localization of Peripheral Subcutaneous Veins.
An ontology-based similarity measure for biomedical data-application to radiology reports.
CC-DTW: An Accurate Indoor Fingerprinting Localization Using Calibrated Channel State Information and Modified Dynamic Time Warping.
Reliable ABC model choice via random forests.
Content patterns in topic-based overlapping communities.
Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.