The patients with liver disease present for various surgical interventions. Surgery may lead to complications in a significant proportion of these patients. These complications may result in considerable morbidity and...The patients with liver disease present for various surgical interventions. Surgery may lead to complications in a significant proportion of these patients. These complications may result in considerable morbidity and mortality. Preoperative assessment can predict survival to some extent in patients with liver disease undergoing surgical procedures. A review of literature suggests nature and the type of surgery in these patients determines the peri-operative morbidity and mortality. Optimization of premorbid factors may help to reduce perioperative mortality and morbidity. The purpose of this review is to discuss the effect of liver disease on perioperative outcome; to understand various risk scoring systems and their prognostic significance; to delineate different preoperative variables implicated in postoperative complications and morbidity; to establish the effect of nature and type of surgery on postoperative outcome in patients with liver disease and to discuss optimal anaesthesia strategy in patients with liver disease.展开更多
The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and ...The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.展开更多
文摘The patients with liver disease present for various surgical interventions. Surgery may lead to complications in a significant proportion of these patients. These complications may result in considerable morbidity and mortality. Preoperative assessment can predict survival to some extent in patients with liver disease undergoing surgical procedures. A review of literature suggests nature and the type of surgery in these patients determines the peri-operative morbidity and mortality. Optimization of premorbid factors may help to reduce perioperative mortality and morbidity. The purpose of this review is to discuss the effect of liver disease on perioperative outcome; to understand various risk scoring systems and their prognostic significance; to delineate different preoperative variables implicated in postoperative complications and morbidity; to establish the effect of nature and type of surgery on postoperative outcome in patients with liver disease and to discuss optimal anaesthesia strategy in patients with liver disease.
基金supported by theCONAHCYT(Consejo Nacional deHumanidades,Ciencias y Tecnologias).
文摘The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.
