Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data

Nambiar, Athira; S, Harikrishnaa; S, Sharanprasath

doi:10.3389/frai.2023.1272506

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…To address this, methods such as SHAP (Shapley additive explanation) and LIME (local interpretable model-agnostic explanation) have been developed. These techniques are designed to clarify how each feature contributes to the overall prediction in black box models [34]. Furthermore, we compared the important features from SVM, logR, RF, and XGB as presented in Table 2.…”

Section: Important Featuresmentioning

confidence: 99%

Beyond Amyloid: A Machine Learning-Driven Approach Reveals Properties of Potent GSK-3β Inhibitors Targeting Neurofibrillary Tangles

Nwadiugwu,

Onwuekwe,

Ezeanolue

et al. 2024

IJMS

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Current treatments for Alzheimer’s disease (AD) focus on slowing memory and cognitive decline, but none offer curative outcomes. This study aims to explore and curate the common properties of active, drug-like molecules that modulate glycogen synthase kinase 3β (GSK-3β), a well-documented kinase with increased activity in tau hyperphosphorylation and neurofibrillary tangles—hallmarks of AD pathology. Leveraging quantitative structure–activity relationship (QSAR) data from the PubChem and ChEMBL databases, we employed seven machine learning models: logistic regression (LogR), k-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), neural networks (NNs), and ensemble majority voting. Our goal was to correctly predict active and inactive compounds that inhibit GSK-3β activity and identify their key properties. Among the six individual models, the NN demonstrated the highest performance with a 79% AUC-ROC on unbalanced external validation data, while the SVM model was superior in accurately classifying the compounds. The SVM and RF models surpassed NN in terms of Kappa values, and the ensemble majority voting model demonstrated slightly better accuracy to the NN on the external validation data. Feature importance analysis revealed that hydrogen bonds, phenol groups, and specific electronic characteristics are important features of molecular descriptors that positively correlate with active GSK-3β inhibition. Conversely, structural features like imidazole rings, sulfides, and methoxy groups showed a negative correlation. Our study highlights the significance of structural, electronic, and physicochemical descriptors in screening active candidates against GSK-3β. These predictive features could prove useful in therapeutic strategies to understand the important properties of GSK-3β candidate inhibitors that may potentially benefit non-amyloid-based AD treatments targeting neurofibrillary tangles.

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Section: Important Featuresmentioning

confidence: 99%

Beyond Amyloid: A Machine Learning-Driven Approach Reveals Properties of Potent GSK-3β Inhibitors Targeting Neurofibrillary Tangles

Nwadiugwu,

Onwuekwe,

Ezeanolue

et al. 2024

IJMS

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“…However, model-agnostic methods are modelindependent and can be applied to any model or algorithm. As a CDSS tool that reduces the model dependency, a COVID-19 symptom severity classifier that utilizes different machine learning models to identify high-risk patients for COVID-19 has been proposed [95].…”

Section: Xai-based Cdssmentioning

confidence: 99%

XAI-based Clinical Decision Support System: A Systematic Review

Kim,

Kim

et al. 2024

Preprint

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With increasing electronic medical data and the development of artificial intelligence, Clinical Decision Support Systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization process. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively decide; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSS provides a valid rationale and explainable results. It ensures trustworthiness and transparency by showing the recommendation and prediction results process through explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduce resources, datasets, and models that can be utilized; and provides a foundation model to support decision-making in various disease domains. Finally, we propose future directions for CDSS technology and highlight societal issues that need to be addressed to emphasize the potential of CDSS in the future.

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A Novel Survey on Image Classification Models for Explainable Predictions using Computer Vision

Koushik,

Reddy,

Rithvik

et al. 2024

2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)

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Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data

Cited by 5 publications

References 41 publications

Beyond Amyloid: A Machine Learning-Driven Approach Reveals Properties of Potent GSK-3β Inhibitors Targeting Neurofibrillary Tangles

Beyond Amyloid: A Machine Learning-Driven Approach Reveals Properties of Potent GSK-3β Inhibitors Targeting Neurofibrillary Tangles

XAI-based Clinical Decision Support System: A Systematic Review

A Novel Survey on Image Classification Models for Explainable Predictions using Computer Vision

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