A novel ensemble learning paradigm for medical diagnosis with imbalanced data
N Liu, X Li, E Qi, M Xu, L Li, B Gao - IEEE Access, 2020 - ieeexplore.ieee.org
With the help of machine learning (ML) techniques, the possible errors made by the
pathologists and physicians, such as those caused by inexperience, fatigue, stress and so …
pathologists and physicians, such as those caused by inexperience, fatigue, stress and so …
An Overview of Algorithms for Myocardial Infarction Diagnostics using ECG Signals: Advances and Challenges
C Han, Y Zhou, W Que, Z Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Myocardial infarction (MI) boasts the highest mortality rate, striking suddenly and often
stealthily. Conducting an electrocardiogram (ECG) examination stands as the most …
stealthily. Conducting an electrocardiogram (ECG) examination stands as the most …
Efficient feature selection and ML algorithm for accurate diagnostics
Abstract Machine learning algorithms have been deployed in numerous optimization,
prediction and classification problems. This has endeared them for application in fields such …
prediction and classification problems. This has endeared them for application in fields such …
[HTML][HTML] Improving chronic disease management for children with knowledge graphs and artificial intelligence
Chronic diseases for children pose serious challenges from a health management
perspective. When not implemented in a well-designed manner, an inefficient management …
perspective. When not implemented in a well-designed manner, an inefficient management …
An interpretable knowledge-based decision support system and its applications in pregnancy diagnosis
This paper aims to propose an interpretable knowledge-based decision support system
(IKBDSS) that will assist physicians to predict the risk level of a disease. Our system enables …
(IKBDSS) that will assist physicians to predict the risk level of a disease. Our system enables …
A diagnostic prediction framework on auxiliary medical system for breast cancer in develo** countries
Due to the complexity of the tumor and a large amount of patient information, an intelligent
system is used to filter and extract hidden information, which will be beneficial to make …
system is used to filter and extract hidden information, which will be beneficial to make …
Explainable artificial intelligence (XAI) in medical decision support systems (MDSS): applicability, prospects, legal implications, and challenges
The healthcare sector is very interested in machine learning (ML) and artificial intelligence
(AI). Nevertheless, applying AI applications in scientific contexts is difficult because of the …
(AI). Nevertheless, applying AI applications in scientific contexts is difficult because of the …
Medical knowledge embedding based on recursive neural network for multi-disease diagnosis
The representation of knowledge based on first-order logic captures the richness of natural
language and supports multiple probabilistic inference models. Although symbolic …
language and supports multiple probabilistic inference models. Although symbolic …
Learning an expandable EMR-based medical knowledge network to enhance clinical diagnosis
Electronic medical records (EMRs) contain a wealth of knowledge that can be used to assist
doctors in making clinical decisions like disease diagnosis. Constructing a medical …
doctors in making clinical decisions like disease diagnosis. Constructing a medical …
Gated Tree-based Graph Attention Network (GTGAT) for medical knowledge graph reasoning
Abstract Knowledge graph (KG) is a multi-relational data that has proven valuable for many
tasks including decision making and semantic search. In this paper, we present GTGAT …
tasks including decision making and semantic search. In this paper, we present GTGAT …