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Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection.

Zhuo Liu¹, Gerui Zhang¹, Zhao Jingyuan¹

¹The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China.

Journal of Healthcare Engineering

|March 19, 2020

Summary

Deep reinforcement learning combined with second-generation sequencing offers a promising approach for identifying lung infection pathogens. This integration aims to improve the speed and accuracy of diagnosis for targeted pulmonary disease treatment.

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Area of Science:

Medical Informatics
Genomics
Artificial Intelligence

Background:

Deep reinforcement learning (DRL) shows potential in computer-aided diagnosis and therapy, especially with big data from the medical Internet of Things.
Accurate and rapid pathogen identification is crucial for effective treatment of pulmonary infectious diseases.

Purpose of the Study:

To explore the application value of integrating deep reinforcement learning with second-generation sequencing for diagnosing and treating pulmonary infectious diseases.
To present DRL methods for identifying lung infection pathogens and analyze current diagnostic strategies.

Main Methods:

Review and presentation of representative deep reinforcement learning methods applicable to pathogen identification.
Analysis of the current status and characteristics of pathogenic diagnosis for pulmonary infectious diseases.
Examination of common second-generation sequencing technologies used in lung infection diagnosis.

Main Results:

Deep reinforcement learning methods show potential for pathogen identification in lung infections.
Second-generation sequencing technologies offer valuable tools for diagnosing lung infections.
Integration of DRL and second-generation sequencing presents a novel approach for pulmonary disease diagnosis.

Conclusions:

The integration of deep reinforcement learning and second-generation sequencing holds significant promise for advancing the diagnosis and treatment of lung infections.
This synergy is expected to accelerate the development of smart healthcare solutions, leveraging big data and the medical Internet of Things.
Future research should focus on overcoming challenges in combining these technologies for enhanced clinical application.