Amirkhani Abdollah, Papageorgiou Elpiniki I, Mohseni Akram, Mosavi Mohammad R
Dept. of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
Dept. of Computer Engineering, Technological Educational Institute of Central Greece, Lamia 35100, Greece.
Comput Methods Programs Biomed. 2017 Apr;142:129-145. doi: 10.1016/j.cmpb.2017.02.021. Epub 2017 Feb 22.
A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems. This review study is conducted to identify different FCM structures used in MDSS designs. The best structure for each medical application can be introduced by studying the properties of FCM structures.
This paper surveys the most important decision- making methods and applications of FCMs in the medical field in recent years. To investigate the efficiency and capability of different FCM models in designing MDSSs, medical applications are categorized into four key areas: decision-making, diagnosis, prediction, and classification. Also, various diagnosis and decision support problems addressed by FCMs in recent years are reviewed with the goal of introducing different types of FCMs and determining their contribution to the improvements made in the fields of medical diagnosis and treatment.
In this survey, a general trend for future studies in this field is provided by analyzing various FCM structures used for medical purposes, and the results from each category.
Due to the unique specifications of FCMs in integrating human knowledge and experience with computer-aided techniques, they are among practical instruments for MDSS design. In the not too distant future, they will have a significant role in medical sciences.
由于医生经验不足、需分析的数据量巨大以及难以获取既往患者的病历，导致了高比例的医疗差错。而使用计算机辅助技术可以减少这些差错。因此，设计更高效的医疗决策支持系统（MDSS）以协助医生进行决策至关重要。通过结合模糊逻辑和神经网络的特性，模糊认知图（FCM）是用于复杂系统建模的最新、最有效且最强大的人工智能技术之一。本综述研究旨在识别MDSS设计中使用的不同FCM结构。通过研究FCM结构的特性，可以为每种医疗应用引入最佳结构。
本文综述了近年来FCM在医学领域最重要的决策方法和应用。为了研究不同FCM模型在设计MDSS方面的效率和能力，将医疗应用分为四个关键领域：决策、诊断、预测和分类。此外，还回顾了近年来FCM解决的各种诊断和决策支持问题，目的是介绍不同类型的FCM，并确定它们对医学诊断和治疗领域改进的贡献。
在本次综述中，通过分析用于医学目的的各种FCM结构以及每个类别的结果，为该领域未来的研究提供了一个总体趋势。
由于FCM在将人类知识和经验与计算机辅助技术相结合方面具有独特的特性，它们是MDSS设计的实用工具之一。在不久的将来，它们将在医学科学中发挥重要作用。
