Results 271 to 280 of about 1,235,584 (320)

Network meta-analysis of the efficacy of pharmacological treatments for post-stroke cognitive impairment and vascular cognitive impairment. [PDF]

Front Neurol
Li W, Liu X, Gao C, Li W, Liao X.
europepmc   +1 more source

Enhancing Large Language Models for Improved Accuracy and Safety in Medical Question Answering: Comparative Study.

JMIR Med Educ
Wang D, Ye J, Li J, Liang J, Zhang Q, Hu Q, Pan C, Wang D, Liu Z, Shi W, Guo M, Li F, Du W, Zheng YF.   +13 more
europepmc   +1 more source

Concerns on classification, statistical methods, and interpretation in NMA on acupuncture for endometriosis. [PDF]

Arch Gynecol Obstet
Huang W, Du X.
europepmc   +1 more source

Assessing the Limitations of Large Language Models in Clinical Practice Guideline-Concordant Treatment Decision-Making on Real-World Data: Retrospective Study. [PDF]

JMIRx Med
Roeschl T, Hoffmann M, Hashemi D, Rarreck F, Hinrichs N, Trippel TD, Gröschel MI, Unbehaun A, Klein C, Kempfert J, Dreger H, O'Brien B, Hindricks G, Balzer F, Falk V, Meyer A.   +15 more
europepmc   +1 more source

Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review and Meta-Analysis.

JMIR Med Inform
Sun Y, Li B, Ju C, Hu L, Sun H, An J, Kim TH, Bu Z, Shi Z, Liu J, Liu Z.   +10 more
europepmc   +1 more source

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TI2Net: Temporal Identity Inconsistency Network for Deepfake Detection

IEEE Workshop/Winter Conference on Applications of Computer Vision, 2023
In this paper, we propose the Temporal Identity Inconsistency Network (TI2Net), a Deepfake detector that focuses on temporal identity inconsistency. Specifically, TI2Net recognizes fake videos by capturing the dissimilarities of human faces among video ...
Baoping Liu, Bo Liu, Ming Ding, Tianqing Zhu, Xin Yu   +4 more
semanticscholar   +1 more source

FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance

Industrial Conference on Data Mining, 2021
The objective of fraud detection is to distinguish fraudsters from normal users. In graph/network environments, both fraudsters and normal users are modeled as nodes, and the connections between those nodes are represented as edges.
Ge Zhang, Jia Wu, Jian Yang, A. Beheshti, Shan Xue, Chuan Zhou, Quan.Z Sheng   +6 more
semanticscholar   +1 more source