TriGCN: Graph Convolution Network Based on Tripartite Graph for Personalized Medicine Recommendation System DOI Creative Commons
Huan Zhou,

Sisi Liao,

F. Richard Guo

и другие.

Systems, Год журнала: 2024, Номер 12(10), С. 398 - 398

Опубликована: Сен. 26, 2024

Intelligent medical systems have great potential to play an important role in people’s daily lives, as they can provide disease and medicine information immediately for both doctors patients. Graph-structured data are attracting more attention the artificial intelligence sector. Combining graph-structured with a set, tripartite graph convolutional network named TriGCN is proposed. This model able connect or patient, disease, nodes, propagate from layer layer, update node features at same time. After this, calibrated label ranking used give personalized recommendation lists The approach has performance, outperforming other machine learning methods. Thus, this be applied reality will contributions public health future.

Язык: Английский

A cross-institutional database of operational risk external loss events in Chinese banking sector 1986–2023 DOI Creative Commons
Xiaoqian Zhu, Yanpeng Chang, Jianping Li

и другие.

Scientific Data, Год журнала: 2024, Номер 11(1)

Опубликована: Авг. 28, 2024

Nowadays the collection of operational risk data worldwide highly relies on human labor, leading to slow updates, inconsistency, and limited quantity. There remains a substantial shortage publicly accessible databases for analysis. This study proposes new framework by aggregating text mining methods replace exhausting manual process. The news about can be automatically collected from web page, then its content is analyzed key information extracted. Finally, Public-Chinese Operational Loss Data (P-COLD) database financial institutions constructed expanded. Each record contains 12 information, such as occurrence time, loss amount, business lines, offering more thorough description events. With 3,723 records 1986 2023, P-COLD has become one largest most comprehensive external in China. We anticipate will contribute advancements capital calculations, dependence analysis, institutional internal controls.

Язык: Английский

Процитировано

5
Dynamic recommender system for chronic disease-focused online health community DOI

Junruo Gao,

Yuan Zhao,

Dongming Yang

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 258, С. 125086 - 125086

Опубликована: Авг. 24, 2024

Язык: Английский

Процитировано

1
TriGCN: Graph Convolution Network Based on Tripartite Graph for Personalized Medicine Recommendation System DOI Creative Commons
Huan Zhou,

Sisi Liao,

F. Richard Guo

и другие.

Systems, Год журнала: 2024, Номер 12(10), С. 398 - 398

Опубликована: Сен. 26, 2024

Intelligent medical systems have great potential to play an important role in people’s daily lives, as they can provide disease and medicine information immediately for both doctors patients. Graph-structured data are attracting more attention the artificial intelligence sector. Combining graph-structured with a set, tripartite graph convolutional network named TriGCN is proposed. This model able connect or patient, disease, nodes, propagate from layer layer, update node features at same time. After this, calibrated label ranking used give personalized recommendation lists The approach has performance, outperforming other machine learning methods. Thus, this be applied reality will contributions public health future.

Язык: Английский

Процитировано

0