Intelligent knowledge consolidation: From data to wisdom

Knowledge based systems have accomplished remarkable achievements in assisting evidence based decision making for complex problems. However, machine learning-driven, intelligent systems of today are dependent on the underlying knowledge model, which is acquired from domain experts, or the available datasets in a structured or unstructured format. Most of the existing literature utilized a single modal, while very few have combined multi-modalities (mainly two) for knowledge acquisition. In order to achieve a strong Artificial Intelligence, multi-domain and multi-modal knowledge acquisition, and consolidation is required. This paper presents the research work, driving the realization of such a comprehensive framework, in the field of healthcare. Using area specific, state-of-the-art machine learning techniques, we first extract knowledge from structured and unstructured data, which is consolidated with expert knowledge and managed through ripple down rules. Our presented technique shows an accuracy of 92.05%, which is much higher than single modal deep learning at 78.20%, naive bayes at 69.70%, logistic regression at 61.20%, expert driven knowledge at 86.02%, and naive knowledge combination at 70.86%. Thus, through the application of our proposed technique, we provide the foundations for an accurate and evolvable knowledge-base, that can greatly enhance decision making in the healthcare domain. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper presents research work in the field of healthcare to achieve a comprehensive framework using state-of-the-art machine learning techniques to extract knowledge from structured and unstructured data and integrate it with expert knowledge. The technique proposed shows higher accuracy compared to other methods, laying the foundation for an accurate and evolvable knowledge base that greatly enhances decision-making in the healthcare domain.