Deep Q-networks with web-based survey data for simulating lung cancer intervention prediction and assessment in the elderly: a quantitative study
Open accessAbstract
Background: Lung cancer screening and intervention might be important to help detect lung cancer early and reduce the mortality, but little was known about lung cancer intervention strategy associated with intervention effect for preventing lung cancer. We employed Deep Q-Networks (DQN) to respond to this gap. The aim was to quantitatively predict lung cancer optimal intervention strategy and assess intervention effect in aged 65 years and older (the elderly). Methods: We screened lung cancer high risk with web-based survey data and conducted simulative intervention. DQN models were developed to predict optimal intervention strategies to prevent lung cancer in elderly men and elderly women separately. We assessed the intervention effects to evaluate the optimal intervention strategy. Results: Proposed DQN models quantitatively predicted and assessed lung cancer intervention. DQN models performed well in five stratified groups (elderly men, elderly women, men, women and the whole population). Stopping smoking and extending quitting smoking time were optimal intervention strategies in elderly men. Extending quitting time and reducing smoked cigarettes number were optimal intervention strategies in elderly women. In elderly men and women, the maximal reductions of lung cancer incidence were 31.81% and 24.62% separately. Lung cancer incidence trend was deduced from the year of 1984 to 2050, which predicted that the difference of lung cancer incidence between elderly men and women might be significantly decreased after thirty years quitting time. Conclusions: We quantitatively predicted optimal intervention strategy and assessed lung cancer intervention effect in the elderly through DQN models. Those might improve intervention effects and reasonably prevent lung cancer.
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Word2Vec is capable of unsupervised learning to determine semantic meaning and similarity between words.
“By leveraging a contextual window, Word2Vec is capable of unsupervised learning to determine semantic meaning and similarity between words (Zhao et al. 2022; Subba and Kumari 2022; Oubenali et al. 2022).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Oubenali et al. 2022
The Introduction and Discussion discuss deep Q-networks for lung cancer intervention, not Word2Vec. Searches for Word2Vec, contextual windows, word embeddings, and semantic similarity found no passage addressing the claim in our copy. Page numbers were not available. (LLM, conf=0.78)
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An integrated decision framework for evaluating and recommending health care services
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An effective undersampling method for biomedical named entity recognition using machine learning
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FSOU-Net: Feature supplement and optimization U-Net for 2D medical image segmentation
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Electronic community health information system usability evaluation in Ethiopia
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Researching public health datasets in the era of deep learning: a systematic literature review
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Privacy-Preserving Medical Data Generation Using Adversarial Learning
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Privacy-Preserving Logistic Regression Model Training Scheme by Homomorphic Encryption
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Explainable AI for Kidney Stone Prediction: A Machine Learning-Based Clinical Analysis
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The Experimental Study of Machine Learning Classifiers for Disease Prediction
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Comparison Between Two Hospitals to Study the Impact of COVID-19 on Emergency Medicine Activities
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A Bicentric Study to Investigate the Impact of COVID-19 on Urological Patients
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Image Segmentation for Medical Analysis
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Predictive Process Mining a Systematic Literature Review
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Design and implementation of closed-loop drug management in the pharmaceutical industry
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Machine Learning Approaches for COVID-19 Classification and Potential Novel Therapeutic Targets
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Computer-Aided Detection of Brain Midline Using CT Images
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Topology‐aware anatomical segmentation of the Circle of Willis: HUNet unveils the vascular network
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Heart Disease Prediction Combining Autoencoders, Neural Network Clustering and Optimal Tuning
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Towards a fairer vision: addressing class imbalance in image gender recognition
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An Explainable Machine Learning Approach to Multiclass Diabetes Risk Assessment
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- Authors
- Chen, S., & Wu, S.
- Affiliations
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- ORCID
- Chen, Songjing
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2022-01-04
- Volume
- 22
- Issue
- 1
- Pages
- 1-1
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC8725301
- OpenAlex
- W4205619992
- Cited by
- 96
- References
- 39
- Citations by year
- 2026: 2 · 2025: 17 · 2024: 40 · 2023: 24 · 2022: 7 · 2021: 4 · 2020: 1 · 2017: 1
- FWCI
- 14.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- General Project on Humanities and Social Science Research of Ministry of Education of China (19YJC870002)
- National Key R&D Program of China (2016YFC0901602)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- lung cancer prevention, lung cancer screening, deep Q-network, smoking cessation, older adults, sex differences, reinforcement learning
- MeSH terms
- Aged, Female, Humans, Lung Neoplasms, Male, Smoking, Incidence, Internet, Early Detection of Cancer
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