ArticleJournal of healthcare engineering2020
Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection.
Article in Journal of healthcare engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
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- scFocus: Detecting branching probabilities in single-cell data with SAC.Computational and structural biotechnology journal · 2025Article
- Clinical application of whole exome sequencing technology in small-for-gestational-age children.American journal of translational research · 2023Article
- Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques.BioMed research international · 2022Article
- Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things.Computational intelligence and neuroscience · 2022Article
- Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, deep reinforcement learning, associated with medical big data generated and collected from medical Internet of Things, is prospective for computer-aided diagnosis and therapy. In this paper, we focus on the application value of the second-generation sequencing technology in the diagnosis and treatment of pulmonary infectious diseases with the aid of the deep reinforcement learning. Specifically, the rapid, comprehensive, and accurate identification of pathogens is a prerequisite for clinicians to choose timely and targeted treatment. Thus, in this work, we present representative deep reinforcement learning methods that are potential to identify pathogens for lung infection treatment. After that, current status of pathogenic diagnosis of pulmonary infectious diseases and their main characteristics are summarized. Furthermore, we analyze the common types of second-generation sequencing technology, which can be used to diagnose lung infection as well. Finally, we point out the challenges and possible future research directions in integrating deep reinforcement learning with second-generation sequencing technology to diagnose and treat lung infection, which is prospective to accelerate the evolution of smart healthcare with medical Internet of Things and big data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.