PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Healthcare10.3390/healthcare100609662022106966A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care UnitsKaouter Karboub, Mohamed Tabaahttps://www.mdpi.com/2227-9032/10/6/966/pdf
Frontiers in Neurology10.3389/fneur.2020.610531202111Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care UnitsXiming Nie, Yuan Cai, Jingyi Liu, Xiran Liu, Jiahui Zhao, Zhonghua Yang, Miao Wen, Liping Liuhttps://www.frontiersin.org/articles/10.3389/fneur.2020.610531/full
Healthcare Informatics Research10.4258/hir.2022.28.4.3642022284364-375Machine Learning Model for the Prediction of Hemorrhage in Intensive Care UnitsSora Kang, Chul Park, Jinseok Lee, Dukyong Yoonhttp://e-hir.org/upload/pdf/hir-2022-28-4-364.pdf, http://e-hir.org/journal/view.php?doi=10.4258/hir.2022.28.4.364, http://e-hir.org/upload/pdf/hir-2022-28-4-364.pdf
Frontiers in Physiology10.3389/fphys.2022.921884202213Early prediction of hypothermia in pediatric intensive care units using machine learningPradeep Singh, Aditya Nagori, Rakesh Lodha, Tavpritesh Sethihttps://www.frontiersin.org/articles/10.3389/fphys.2022.921884/full
10.21203/rs.3.rs-798902/v12021Machine Learning Classifier Models Can Identify Delirium in Intensive Care UnitsAnmin Hu, Hui-Ping Li, Zhen Li, Zhongjun Zhang, Xiong-Xiong Zhonghttps://www.researchsquare.com/article/rs-798902/v1, https://www.researchsquare.com/article/rs-798902/v1.html
Frontiers in Medicine10.3389/fmed.2022.83738220229Prediction Models for Sepsis-Associated Thrombocytopenia Risk in Intensive Care Units Based on a Machine Learning AlgorithmXuandong Jiang, Yun Wang, Yuting Pan, Weimin Zhanghttps://www.frontiersin.org/articles/10.3389/fmed.2022.837382/full
Intensive Care Medicine10.1007/s00134-022-06922-82022491119-120Building a better machine learning model of extubation for neurocritical care patientsShohei Onohttps://link.springer.com/content/pdf/10.1007/s00134-022-06922-8.pdf, https://link.springer.com/article/10.1007/s00134-022-06922-8/fulltext.html, https://link.springer.com/content/pdf/10.1007/s00134-022-06922-8.pdf
Journal of Personalized Medicine10.3390/jpm12111901202212111901The Clinical Application of Machine Learning-Based Models for Early Prediction of Hemorrhage in Trauma Intensive Care UnitsShih-Wei Lee, His-Chun Kung, Jen-Fu Huang, Chih-Po Hsu, Chia-Cheng Wang, Yu-Tung Wu, Ming-Shien Wen, Chi-Tung Cheng, Chien-Hung Liaohttps://www.mdpi.com/2075-4426/12/11/1901/pdf
Journal of Global Antimicrobial Resistance10.1016/j.jgar.2022.03.019202229225-231Early prediction of carbapenem-resistant Gram-negative bacterial carriage in intensive care units using machine learningQiqiang Liang, Qinyu Zhao, Xin Xu, Yu Zhou, Man Huanghttps://api.elsevier.com/content/article/PII:S2213716522000741?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S2213716522000741?httpAccept=text/plain
10.21203/rs.3.rs-3298895/v12023Development and Validation of an Interpretable Machine Learning Model for the Prediction of Intubation in the Intensive Care UnitJianyuan Liu, Xiangjie Duan, Minjie Duan, Yu Jiang, Wei Mao, Lilin Wang, Gang Liuhttps://www.researchsquare.com/article/rs-3298895/v1, https://www.researchsquare.com/article/rs-3298895/v1.html