Scholarlink

Introduction <p>Hepatic encephalopathy (HE) is defined as a specific type of cerebral dysfunction that encompasses a wide range of cognitive, psychomotor, and psychiatric disturbances. The burgeoning field of Artificial Intelligence (AI), particularly Machine Learning (ML), offers promising avenues for early detection and enhanced control of HE. This scoping review aims to provide a consolidated overview of AI’s role in the diagnosis and management of HE, thereby informing and guiding future research endeavors in this domain.</p> Methods <p>We followed Arksey and O’Malley’s methodological framework to perform this scoping review, using PubMed, Web of Science, Scopus, ScienceDirect, and IEEE databases to find relevant articles. We also utilized the PRISMA standard to report our review in a standardized manner. Studies that focused on the applications of AI or ML techniques in relation to the prediction or diagnosis of HE disease were included.</p> Results <p>Out of the 231 articles identified, 20 were ultimately included in this scoping review. The integration of artificial neural networks and expert systems represented an early and pioneering approach in applying AI to HE. Among supervised learning algorithms, Support Vector Machine emerged as the most frequently employed technique in HE research, based on our review of the selected studies. Notably, the primary application of AI in HE studies has been predictive modeling (<i>n</i> = 14), followed by five studies focused on classifying HE stages and one study analyzing patient survival using AI methodologies.</p> Conclusions <p>This scoping review highlights the growing use of AI and ML diagnostic models and predictive tools utilizing various data types. These advancements have the potential to positively impact patient outcomes. Future research should focus on validating and implementing these AI models in clinical settings to assess their real-world effectiveness in improving patient care.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning techniques in hepatic encephalopathy: a scoping review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月1日

查看全文
  • Fatemeh Kiani,
  • Farkhondeh Asadi,
  • Azamossadat Hosseini,
  • Shahabedin Rahmatizadeh,
  • Farhang Hosseini,
  • Behzad Kiani

摘要

Introduction

Hepatic encephalopathy (HE) is defined as a specific type of cerebral dysfunction that encompasses a wide range of cognitive, psychomotor, and psychiatric disturbances. The burgeoning field of Artificial Intelligence (AI), particularly Machine Learning (ML), offers promising avenues for early detection and enhanced control of HE. This scoping review aims to provide a consolidated overview of AI’s role in the diagnosis and management of HE, thereby informing and guiding future research endeavors in this domain.

Methods

We followed Arksey and O’Malley’s methodological framework to perform this scoping review, using PubMed, Web of Science, Scopus, ScienceDirect, and IEEE databases to find relevant articles. We also utilized the PRISMA standard to report our review in a standardized manner. Studies that focused on the applications of AI or ML techniques in relation to the prediction or diagnosis of HE disease were included.

Results

Out of the 231 articles identified, 20 were ultimately included in this scoping review. The integration of artificial neural networks and expert systems represented an early and pioneering approach in applying AI to HE. Among supervised learning algorithms, Support Vector Machine emerged as the most frequently employed technique in HE research, based on our review of the selected studies. Notably, the primary application of AI in HE studies has been predictive modeling (n = 14), followed by five studies focused on classifying HE stages and one study analyzing patient survival using AI methodologies.

Conclusions

This scoping review highlights the growing use of AI and ML diagnostic models and predictive tools utilizing various data types. These advancements have the potential to positively impact patient outcomes. Future research should focus on validating and implementing these AI models in clinical settings to assess their real-world effectiveness in improving patient care.