Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | K-CAP '23 |
| Untertitel | Proceedings of the 12th Knowledge Capture Conference 2023 |
| Seiten | 241-249 |
| Seitenumfang | 9 |
| ISBN (elektronisch) | 9798400701412 |
| Publikationsstatus | Veröffentlicht - 5 Dez. 2023 |
| Veranstaltung | 12th ACM International Conference on Knowledge Capture, K-CAP 2023 - Pensacola, USA / Vereinigte Staaten Dauer: 5 Dez. 2023 → 7 Dez. 2023 |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Theoretische Informatik und Mathematik
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Software
Ziele für nachhaltige Entwicklung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
K-CAP '23: Proceedings of the 12th Knowledge Capture Conference 2023. 2023. S. 241-249.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Unraveling the Hepatitis B Cure
T2 - 12th ACM International Conference on Knowledge Capture, K-CAP 2023
AU - Dost, Shahi
AU - Rivas, Ariam
AU - Begali, Hanan
AU - Ziegler, Annett
AU - Aliabadi, Elimira
AU - Cornberg, Markus
AU - Kraft, Anke Rm
AU - Vidal, Maria Esther
N1 - Funding Information: This work is part of the ImProVIT project funded by Niedersachsen Vorab (project ZN3438) by the Lower Saxony Ministry of Research and Culture and the Volkswagen Foundation. Maria-Esther Vidal is partially supported by Leibniz Association, program "Leibniz Best Minds: Programme for Women Professors", project TrustKG-Transforming Data in Trustable Insights; Grant P99/2020.
PY - 2023/12/5
Y1 - 2023/12/5
N2 - 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.
AB - 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.
KW - Community Detection
KW - Hepatitis B Virus Infection
KW - Inductive Learning
KW - Knowledge Graph Embedding
KW - Knowledge Graphs
UR - http://www.scopus.com/inward/record.url?scp=85180367974&partnerID=8YFLogxK
U2 - 10.1145/3587259.3627558
DO - 10.1145/3587259.3627558
M3 - Conference contribution
AN - SCOPUS:85180367974
SP - 241
EP - 249
BT - K-CAP '23
Y2 - 5 December 2023 through 7 December 2023
ER -