Zobrazeno 1 - 10
of 7 347
pro vyhledávání: '"Machine Learning model"'
Autor:
Li Chen, Yuanbo Hu, Yu Li, Bingyu Zhang, Jiale Wang, Mengmeng Deng, Jinlian Zhang, Wenyao Zhu, Hao Gu, Lingyu Zhang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-23 (2024)
Abstract Hepatocellular carcinoma (HCC) is a highly aggressive malignancy with increasing global prevalence and is one of the leading causes of cancer-related mortality in the human population. Developing robust clinical prediction models and prognos
Externí odkaz:
https://doaj.org/article/287e526dbafc40b58d73bc1d9744d491
Publikováno v:
Ibrain, Vol 10, Iss 3, Pp 323-344 (2024)
Abstract This study aims to explore the expression profile of PANoptosis‐related genes (PRGs) and immune infiltration in Alzheimer's disease (AD). Based on the Gene Expression Omnibus database, this study investigated the differentially expressed P
Externí odkaz:
https://doaj.org/article/27b33d0f0635484cb1979f1d5322d515
Autor:
Qing ZHANG, Yi HE, Xueye CHEN, Binghai GAO, Lifeng ZHANG, Zhanao ZHAO, Jiangang LU, Yalei ZHANG
Publikováno v:
Zhongguo dizhi zaihai yu fangzhi xuebao, Vol 35, Iss 4, Pp 146-162 (2024)
Convolutional neural network (CNN) models are widely used in landslide susceptibility assessment due to their powerful feature extraction capabilities, and traditional CNN is no longer able to meet the requirements. Therefore, this paper proposes a m
Externí odkaz:
https://doaj.org/article/9cefe83e81254371a909a783eb93677f
Publikováno v:
Discover Sustainability, Vol 5, Iss 1, Pp 1-14 (2024)
Abstract We examine the effectiveness of green innovation on CO2 emissions in the top twelve polluting nations—China, the US, India, Russia, Japan, South Korea, Canada, Mexico, Turkey, Italy, Poland, and the UK—from 1996 to 2020. Using panel data
Externí odkaz:
https://doaj.org/article/0a703991f3ae4255977f8eb90951440f
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-20 (2024)
Abstract Objective This study was designed to develop and validate a robust predictive model for one-year mortality in elderly coronary heart disease (CHD) patients with anemia using machine learning methods. Methods Demographics, tests, comorbiditie
Externí odkaz:
https://doaj.org/article/c6a759e936c54b41989c82759bf70238
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)
Abstract There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this
Externí odkaz:
https://doaj.org/article/4c8eca202623400693717930be6a2074
Autor:
Kenichi Nakajima, Tomoaki Nakata, Takahiro Doi, Derk O. Verschure, Viviana Frantellizzi, Maria Silvia De Feo, Hayato Tada, Hein J. Verberne
Publikováno v:
EJNMMI Research, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Background 123I-meta-iodobenzylguanidine (mIBG) has been applied to patients with chronic heart failure (CHF). However, the relationship between 123I-mIBG activity and lethal arrhythmic events (ArE) is not well defined. This study aimed to d
Externí odkaz:
https://doaj.org/article/1de012c44a2249698c292a75aeabd799
Autor:
Rui Zhao, Lin Gu, Xiquan Ke, Xiaojing Deng, Dapeng Li, Zhenzeng Ma, Qizhi Wang, Hailun Zheng, Yong Yang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract The risk of cholangitis after ERCP implantation in malignant obstructive jaundice patients remains unknown. To develop models based on artificial intelligence methods to predict cholangitis risk more accurately, according to patients after s
Externí odkaz:
https://doaj.org/article/beda6d80c95d44d187d95f3aac6658a4
Autor:
Jian‐Ming Liao, Yu‐Hsuan Chen, Hsuan‐Wei Lee, Bo‐Cheng Guo, Po‐Cheng Su, Lun‐Hong Wang, Nagannagari Masi Reddy, Aswani Yella, Zhao‐Jie Zhang, Chuan‐Yung Chang, Chia‐Yuan Chen, Shaik M Zakeeruddin, Hui‐Hsu Gavin Tsai, Chen‐Yu Yeh, Michael Grätzel
Publikováno v:
Advanced Science, Vol 11, Iss 43, Pp n/a-n/a (2024)
Abstract Accurately predicting the power conversion efficiency (PCE) in dye‐sensitized solar cells (DSSCs) represents a crucial challenge, one that is pivotal for the high throughput rational design and screening of promising dye sensitizers. This
Externí odkaz:
https://doaj.org/article/080dc61c38d54a20ad42fc9b254f85f7
Publikováno v:
Frontiers in Pharmacology, Vol 15 (2024)
BackgroundDue to its complex pathogenesis, the assessment of cancer-associated disseminated intravascular coagulation (DIC) is challenging. We aimed to develop a machine learning (ML) model to predict overt DIC in critically ill colorectal cancer (CR
Externí odkaz:
https://doaj.org/article/94688bf0ce974884965b82f249fcb267