An Incremental Explanation of Inference in Hybrid Bayesian Networks for Increasing Model Trustworthiness and Supporting Clinical Decision Making

03/05/2020
by   Evangelia Kyrimi, et al.
0

Various AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Key to this is if its underlying reasoning can be explained. A Bayesian network (BN) model has the advantage that it is not a black-box and its reasoning can be explained. In this paper, we propose an incremental explanation of inference that can be applied to hybrid BNs, i.e. those that contain both discrete and continuous nodes. The key questions that we answer are: (1) which important evidence supports or contradicts the prediction, and (2) through which intermediate variables does the information flow. The explanation is illustrated using a real clinical case study. A small evaluation study is also conducted.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

06/19/2020

Does Explainable Artificial Intelligence Improve Human Decision-Making?

Explainable AI provides insight into the "why" for model predictions, of...
01/28/2021

A Taxonomy of Explainable Bayesian Networks

Artificial Intelligence (AI), and in particular, the explainability ther...
12/12/2019

Learning and Optimization with Bayesian Hybrid Models

Bayesian hybrid models fuse physics-based insights with machine learning...
01/07/2020

Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making

Today, AI is being increasingly used to help human experts make decision...
03/27/2013

Explanation of Probabilistic Inference for Decision Support Systems

An automated explanation facility for Bayesian conditioning aimed at imp...
07/01/2020

Medical idioms for clinical Bayesian network development

Bayesian Networks (BNs) are graphical probabilistic models that have pro...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.