Deep Q-networks with web-based survey data for simulating lung cancer intervention prediction and assessment in the elderly: a quantitative study

Abstract

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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