Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Apr 16, 2020
Date Accepted: May 11, 2020

The final, peer-reviewed published version of this preprint can be found here:

An Artificial Intelligence Fusion Model for Cardiac Emergency Decision Making: Application and Robustness Analysis

Gong L, Yang J, Zhang X, Li L

An Artificial Intelligence Fusion Model for Cardiac Emergency Decision Making: Application and Robustness Analysis

JMIR Med Inform 2020;8(7):e19428

DOI: 10.2196/19428

PMID: 32716305

PMCID: 7418004

Application and Robustness Analysis of Artificial Intelligence Fusion Model in Cardiac Emergency Decision

  • Liheng Gong; 
  • Jingjing Yang; 
  • Xiao Zhang; 
  • Ling Li

ABSTRACT

Background:

In the process of cardiac emergency medical treatment, how to reduce the incidence of avoidable adverse events from the perspective of management science, ensure the safety of patients, and generally improve the quality and efficiency of medical treatment have been important research topics in the theoretical and practical circles.

Objective:

article studies the robustness of the decision-making reasoning process from the overall perspective of the cardiac emergency medical system. The main work and innovation are as follows: The principle of robustness is introduced into the study of the quality and efficiency of cardiac emergency decision-making. Aiming at the problem of low reasoning efficiency and accuracy in cardiac emergency decision-making, the concept of robustness for complex medical decision-making was first proposed.

Methods:

The key bottlenecks such as anti-interference ability, fault tolerance, and redundancy are studied. The rules of knowledge acquisition and transfer in the decision-making process are systematically analysed to reveal the core role of knowledge reasoning.

Results:

The robustness threshold method is adopted to construct the robustness criterion group of the system, and the fusion and coordination mechanism is realized through information entropy, information gain and mutual information methods.

Conclusions:

A set of fusion models and robust threshold methods such as R2CMIFS model and RTCRF model are proposed, which enriches the theoretical research on robustness in this field.


 Citation

Please cite as:

Gong L, Yang J, Zhang X, Li L

An Artificial Intelligence Fusion Model for Cardiac Emergency Decision Making: Application and Robustness Analysis

JMIR Med Inform 2020;8(7):e19428

DOI: 10.2196/19428

PMID: 32716305

PMCID: 7418004

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.