Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data.

ABSTRACT:

Introduction

The COVID-19 pandemic had a global impact and created an unprecedented emergency in healthcare and other related frontline sectors. Various Artificial-Intelligence-based models were developed to effectively manage medical resources and identify patients at high risk. However, many of these AI models were limited in their practical high-risk applicability due to their "black-box" nature, i.e., lack of interpretability of the model. To tackle this problem, Explainable Artificial Intelligence (XAI) was introduced, aiming to explore the "black box" behavior of machine learning models and offer definitive and interpretable evidence. XAI provides interpretable analysis in a human-compliant way, thus boosting our confidence in the successful implementation of AI systems in th

SUBMITTER: Nambiar A 

PROVIDER: S-EPMC10726049 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature