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Steps in Outbreak Investigation

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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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AI-RiskX: An Explainable Deep Learning Approach for Identifying At-Risk Patients During Pandemics.

Nada Zendaoui^1,2, Nardjes Bouchemal^1,3, Mohamed Rafik Aymene Berkani⁴

¹Institute of Mathematics and Computer Science, Abdelhafid Boussouf University Center of Mila, Mila 43000, Algeria.

Bioengineering (Basel, Switzerland)

|October 29, 2025

Summary

This summary is machine-generated.

This study introduces AI-RiskX, an explainable deep learning model for identifying high-risk patients during pandemics. It achieves 98.78% accuracy, improving public health decision-making for vulnerable populations.

Keywords:

CNN LSTM artificialintelligence at-risk patient decision making deep learning explainable artificial intelligence

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Area of Science:

Public Health
Artificial Intelligence
Computational Biology

Background:

Pandemics strain healthcare systems, necessitating accurate identification of high-risk individuals.
Existing AI models lack interpretability and fail to account for diverse patient vulnerabilities.

Purpose of the Study:

To develop an explainable deep learning model (AI-RiskX) for identifying at-risk patients during pandemics.
To enhance timely intervention and resource allocation for COVID-19 and related infections.

Main Methods:

Integrated five public health datasets (asthma, diabetes, heart, kidney, thyroid).
Utilized Synthetic Minority Over-sampling Technique (SMOTE) for class balancing.
Employed a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model.
Incorporated SHAP for model interpretability and a rule-based module for stratification.

Main Results:

Achieved 98.78% accuracy in classifying at-risk patients.
Provided both individual and population-level interpretability.
Successfully stratified patients by age and pregnancy status.

Conclusions:

AI-RiskX offers a scalable and interpretable solution for equitable patient classification.
The model supports critical decision-making in public health emergencies.
Addresses limitations of previous AI models by integrating diverse data and prioritizing explainability.