Unraveling the Hepatitis B Cure

A Hybrid AI Approach for Capturing Knowledge about the Immune System's Impact

authored by

Shahi Dost, Ariam Rivas, Hanan Begali, Annett Ziegler, Elimira Aliabadi, Markus Cornberg, Anke Rm Kraft, Maria Esther Vidal

Abstract

Chronic hepatitis B virus (HBV) infection is still a global health problem, with over 296 million chronically HBV-infected individuals worldwide. The merging data about clinical parameters, immune phenotyping data, and genetic information, together with AI models reliant on this integrated information, holds promise in effectively predicting the likelihood of functional cure in HBV-infected patients. Yet, the limited size of multidimensional datasets and characteristic of HBV cases poses a challenge for machine learning (ML) systems that typically require substantial data for pattern recognition. This paper addresses this challenge by introducing HyAI, a hybrid AI framework. HyAI employs knowledge graphs (KGs) and inductive learning to unearth meaningful patterns. HyAI relies on KG embedding models to learn a numerical representation of the HyAI KG in a k-dimensional vector space. Through community detection methods, closely related HBV patients are clustered using similarity metrics formulated from the acquired embeddings. HyAI is studied in a population of HBV patients integrated with multidimensional datasets. Our empirical analysis shows that HyAI uncovers immune markers that, together with clinical and demographic parameters, correspond to good predictors for forecasting the cure of chronic HBV infection.

Organisation(s)

L3S Research Centre

External Organisation(s)

Hannover Medical School (MHH)
TWINCORE Zentrum für Experimentelle und Klinische Infektionsforschung GmbH
German National Library of Science and Technology (TIB)

Type

Conference contribution

Pages

241-249

No. of pages

9

Publication date

05.12.2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computational Theory and Mathematics, Computer Science Applications, Information Systems, Software

Sustainable Development Goals

SDG 3 - Good Health and Well-being

Electronic version(s)

https://doi.org/10.1145/3587259.3627558 (Access: Closed)