Using Modular Ontologies to Capture Causal Knowledge contained in Bayesian Networks-Reference-Cited by-同舟云学术

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Using Modular Ontologies to Capture Causal Knowledge contained in Bayesian Networks

Published:2017-07-31 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017
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Author:

Hu Hengyi¹,Elrafey Amr Hassam²,Kerschberg Larry³

Affiliation:

1. Department of Information Sciences and Technology, George Mason University, Fairfax, VA

2. Department of Health Administration and Policy, George Mason University, Fairfax, VA

3. Department of Computer Science, George Mason University, Fairfax, VA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3110025.3110136

Reference20 articles.

1. Using observed sequence to orient causal networks

2. Guidelines for investigating causality of sequence variants in human disease

3. Causal Inference in the Age of Decision Medicine;Yazdani A.;J. Data Min. Genomics Proteomics,2015

4. Learning Causal Bayesian Network Structures From Experimental Data

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1. A Custom GPT Health Recommender System Informed by Causal Bayesian Networks and Authoritative Ontologies (Preprint);2024-06-15

2. Improving Causal Bayesian Networks Using Expertise in Authoritative Medical Ontologies;ACM Transactions on Computing for Healthcare;2023-10-13

3. Capturing Causal Knowledge from Authoritative Medical Ontologies;2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC);2020-07

4. A Causal Explanatory Model of Bayesian-belief Networks for Analysing the Risks of Opening Data;Lecture Notes in Business Information Processing;2018

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