%0 Conference Paper %1 Dost2023 %A Dost, Shahi %A Rivas, Ariam %A Begali, Hanan %A Ziegler, Annett %A Aliabadi, Elimira %A Cornberg, Markus %A Kraft, Anke Rm %A Vidal, Maria-Esther %B Proceedings of the 12th Knowledge Capture Conference 2023 %C New York, NY, USA %D 2023 %I Association for Computing Machinery %K myown %P 241–249 %R 10.1145/3587259.3627558 %T Unraveling the Hepatitis B Cure: A Hybrid AI Approach for Capturing Knowledge about the Immune System's Impact %U https://doi.org/10.1145/3587259.3627558 %X 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. %@ 9798400701412

@inproceedings{Dost2023, 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.}, added-at = {2024-02-15T17:45:47.000+0100}, address = {New York, NY, USA}, author = {Dost, Shahi and Rivas, Ariam and Begali, Hanan and Ziegler, Annett and Aliabadi, Elimira and Cornberg, Markus and Kraft, Anke Rm and Vidal, Maria-Esther}, biburl = {https://www.bibsonomy.org/bibtex/25cea551dd8e21c76deb2063ded81262f/gabydler}, booktitle = {Proceedings of the 12th Knowledge Capture Conference 2023}, day = 5, doi = {10.1145/3587259.3627558}, interhash = {1783676f9d55d2cf477288e00d35703f}, intrahash = {5cea551dd8e21c76deb2063ded81262f}, isbn = {9798400701412}, keywords = {myown}, location = {<conf-loc>, <city>Pensacola</city>, <state>FL</state>, <country>USA</country>, </conf-loc>}, month = {12}, pages = {241–249}, publisher = {Association for Computing Machinery}, series = {K-CAP '23}, timestamp = {2024-02-15T17:45:47.000+0100}, title = {Unraveling the Hepatitis B Cure: A Hybrid AI Approach for Capturing Knowledge about the Immune System's Impact}, url = {https://doi.org/10.1145/3587259.3627558}, year = 2023 }