A novel explainable machine learning approach for EEG-based brain-computer interface systems
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-05624-w
Abstract: Electroencephalographic (EEG) recordings can be of great help in decoding the open/close hand’s motion preparation. To this end, cortical EEG source signals in the motor cortex (evaluated in the 1-s window preceding movement onset) are… read more here.
Keywords: hand; explainable machine; learning approach; machine learning ... See more keywords
Differentiation of intestinal tuberculosis and Crohn’s disease through an explainable machine learning method
Sign Up to like & getrecommendations! Published in 2022 at "Scientific Reports"
DOI: 10.1038/s41598-022-05571-7
Abstract: Differentiation between Crohn’s disease and intestinal tuberculosis is difficult but crucial for medical decisions. This study aims to develop an effective framework to distinguish these two diseases through an explainable machine learning (ML) model. After… read more here.
Keywords: machine; crohn disease; differentiation; machine learning ... See more keywords
Explainable machine learning by SEE-Net: closing the gap between interpretable models and DNNs
Sign Up to like & getrecommendations! Published in 2024 at "Scientific Reports"
DOI: 10.1038/s41598-024-77507-2
Abstract: Deep Neural Networks (DNNs) have achieved remarkable accuracy for numerous applications, yet their complexity often renders the explanation of predictions a challenging task. This complexity contrasts with easily interpretable statistical models, which, however, often suffer… read more here.
Keywords: learning see; machine learning; gap; see net ... See more keywords
Predicting onward care needs at admission to reduce discharge delay using explainable machine learning
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-00825-6
Abstract: Early identification of patients who require onward referral to social care can prevent delays to discharge from hospital. We introduce an explainable machine learning (ML) model to identify potential social care needs at the first… read more here.
Keywords: care; machine learning; model; care needs ... See more keywords
Explainable machine learning framework for predicting cobalt ion removal by natural hematite
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-18981-0
Abstract: Cobalt contamination in aquatic systems presents considerable environmental and public health concerns. A hybrid artificial intelligence framework was developed for accurate modeling, and this study investigated natural hematite (α-Fe2O3) as an economical and sustainable adsorbent… read more here.
Keywords: framework; natural hematite; removal; contact time ... See more keywords
Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury
Sign Up to like & getrecommendations! Published in 2023 at "Renal Failure"
DOI: 10.1080/0886022x.2022.2151468
Abstract: Abstract Background Although current guidelines didn’t support the routine use of furosemide in oliguric acute kidney injury (AKI) management, some patients may benefit from furosemide administration at an early stage. We aimed to develop an… read more here.
Keywords: acute kidney; kidney injury; explainable machine; machine learning ... See more keywords
Investigation of hit-and-run crash severity through explainable machine learning
Sign Up to like & getrecommendations! Published in 2025 at "Transportation Letters"
DOI: 10.1080/19427867.2025.2488976
Abstract: ABSTRACT This study investigates factors influencing the severity of hit-and-run crashes using explainable machine learning techniques. A 5-year dataset from Victoria, Australia, was analyzed with CatBoost algorithms and SHAP values to highlight key severity factors.… read more here.
Keywords: crash; machine learning; severity; hit run ... See more keywords
Visualizing functional network connectivity differences using an explainable machine-learning method
Sign Up to like & getrecommendations! Published in 2024 at "Physiological Measurement"
DOI: 10.1088/1361-6579/adce52
Abstract: Objective. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statistical learning approaches to… read more here.
Keywords: functional network; machine learning; network; network connectivity ... See more keywords
EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models
Sign Up to like & getrecommendations! Published in 2025 at "Briefings in Bioinformatics"
DOI: 10.1093/bib/bbaf414
Abstract: Abstract Phenotypic variation results from the combination of genotype, the environment, and their interaction. The ability to quantify the relative contributions of genetic and environmental factors to complex traits can help in breeding crops with… read more here.
Keywords: genotype environment; environment; environment interactions; machine learning ... See more keywords
Using explainable machine learning to uncover the kinase–substrate interaction landscape
Sign Up to like & getrecommendations! Published in 2024 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btae033
Abstract: Abstract Motivation Phosphorylation, a post-translational modification regulated by protein kinase enzymes, plays an essential role in almost all cellular processes. Understanding how each of the nearly 500 human protein kinases selectively phosphorylates their substrates is… read more here.
Keywords: kinase substrate; machine learning; model; kinase ... See more keywords
Explainable Machine Learning Models for Glioma Subtype Classification and Survival Prediction
Sign Up to like & getrecommendations! Published in 2025 at "Cancers"
DOI: 10.1101/2025.06.24.661085
Abstract: Gliomas are complex and heterogeneous brain tumors characterized by an unfavorable clinical course and a fatal prognosis, which can be improved by an early determination of tumor kind. Here, we develop explainable machine learning (ML)… read more here.
Keywords: survival; learning models; machine learning; machine ... See more keywords