Case based reasoning framework for COVID-19 diagnosis

被引：0

作者：

Smiti A. ^{[1
]}

Nssibi M. ^{[2
]}

机构：

[1] LARODEC, Universitéde Tunis, Institut Supérieur de Gestion de Tunis, 41 Avenue de la liberté, citéBouchoucha, Le Bardo

[2] Ecole Supérieure d'Economie Numérique, Universitéde Manouba, Technopole de la Manouba, Manouba

来源：

Ingenierie des Systemes d'Information | 2020年 / 25卷 / 04期

关键词：

Case based reasoning; Classification; Clustering; COVID-19; pandemic; Diagnosis; Machine learning; Prediction;

D O I：

10.18280/isi.250409

中图分类号：

学科分类号：

摘要：

The expanding area of Artificial Intelligence is playing a vital role in healthcare practices and research, and as medical field is rich in data can become difficult to interpret, the AI techniques present the preeminent solution to enhance the medical field achievements, thus as novel epidemiology and pathogens presents a critical and emerging issue for global health, the aim of the work presented in this paper is to structure a CBR framework that aid in the patients diagnosis of novel epidemiology presence, the novel pandemic Corona-virus disease (COVID19). The objective of this study is to highlight the Case Based Reasoning (CBR) AI method which is one of the most successful applied methods in the medical field, used for analysis, prediction, diagnosis, and recommendation treatment. This study proposes a CBR conceptual framework for COVID-19 disease prediction, able to aid in the diagnosis, to provide self-health assistant and to guide people in self testing and checking. © 2020 International Information and Engineering Technology Association. All rights reserved.

引用

页码：469 / 474

页数：5

共 14 条

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