Knowledge Graph Construction and Decision Support Towards Transformer Fault Maintenance

Author(s):  
Xiaoying Liu ◽  
Hongwei Wang
Author(s):  
Boris Villazon-Terrazas ◽  
Nuria Garcia-Santa ◽  
Beatriz San Miguel ◽  
Angel del Rey-Mejías ◽  
Juan Carlos Muria ◽  
...  

Fujitsu HIKARI is an artificial intelligence solution to assist clinicians in medical decision making, developed in the context of a joint collaboration project between Fujitsu Laboratories of Europe and Hospital Clínico San Carlos. This decision support system leverages on data analytics combined with healthcare semantic information to provide health estimations for patients, improving care quality and personalized treatment. Fujitsu HIKARI stands on the shoulders of biomedical knowledge, which includes (i) theoretical knowledge extracted from scientific literature, domain expert knowledge, and health standards; and (ii) empirical knowledge extracted from real patient electronic health records. The theoretical knowledge combines a theoretical knowledge graph (TKG) and a biomedical document repository (BDR). The empirical knowledge is encoded in an empirical knowledge graph (EKG). One of the main functionalities of Fujitsu HIKARI is the patient mental health risks assessment, which is based on the exploitation of its underlying Biomedical Knowledge.


Author(s):  
Boris Villazon-Terrazas ◽  
Nuria Garcia-Santa ◽  
Beatriz San Miguel ◽  
Angel del Rey-Mejías ◽  
Juan Carlos Muria ◽  
...  

Fujitsu HIKARI is an artificial intelligence solution to assist clinicians in medical decision making, developed in the context of a joint collaboration project between Fujitsu Laboratories of Europe and Hospital Clínico San Carlos. This decision support system leverages on data analytics combined with healthcare semantic information to provide health estimations for patients, improving care quality and personalized treatment. Fujitsu HIKARI stands on the shoulders of biomedical knowledge, which includes (i) theoretical knowledge extracted from scientific literature, domain expert knowledge, and health standards; and (ii) empirical knowledge extracted from real patient electronic health records. The theoretical knowledge combines a theoretical knowledge graph (TKG) and a biomedical document repository (BDR). The empirical knowledge is encoded in an empirical knowledge graph (EKG). One of the main functionalities of Fujitsu HIKARI is the patient mental health risks assessment, which is based on the exploitation of its underlying Biomedical Knowledge.


2021 ◽  
Vol 24 ◽  
pp. S54
Author(s):  
N. Hong ◽  
W. Lin ◽  
X. Li ◽  
Q. Zhang ◽  
Y. Yang ◽  
...  

Author(s):  
Enayat Rajabi ◽  
Kobra Etminani

The decisions derived from AI-based clinical decision support systems should be explainable and transparent so that the healthcare professionals can understand the rationale behind the predictions. To improve the explanations, knowledge graphs are a well-suited choice to be integrated into eXplainable AI. In this paper, we introduce a knowledge graph-based explainable framework for AI-based clinical decision support systems to increase their level of explainability.


2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK

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