Unraveling the Hepatitis B Cure

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

verfasst von

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.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Medizinische Hochschule Hannover (MHH)
TWINCORE Zentrum für Experimentelle und Klinische Infektionsforschung GmbH
Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek

Typ

Aufsatz in Konferenzband

Seiten

241-249

Anzahl der Seiten

9

Publikationsdatum

05.12.2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Theoretische Informatik und Mathematik, Angewandte Informatik, Information systems, Software

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1145/3587259.3627558 (Zugang: Geschlossen)