Objective To develop a predictive model for healthcare-associated lower respiratory tract infection(HA-LRTI) in hospitalized patients, and establish a simple risk scoring method. Methods Survey data of healthcare-associated infection(HAI)in a few hospitals in 2014 was as training dataset, a Lasso-logistic regression model for predicting HA-LRTI in hospitalized patients was established, minimum model of Bayesian information criterion (BIC) was chosen as the final model, scoring method was established by magnifying regression coefficient by the same scale, survey data of 2015 and 2016 were used as the validation dataset, and was compared with risk scoring method established in the literatures. Results Among the 360 steps of Lasso, smallest BIC (6 690.4) occurred at step 24 with regularization parameter λ=130.8. The risk scoring method consisted 17 items, which was 1/4 of the amount of literature risk scoring method, DeLong's test showed that there was no significant difference in area under the curve of receiver operating characteristic between two scoring methods (Z=0.371,P=0.710), decision curve analysis almost overlaid, the net reclassification index was -0.0149, with no significant difference (Z=-1.301,P=0.193), the integrated discrimination index was 0.006, and difference was significant (P=0.014). Conclusion Lasso-logistic regression model established a simple scoring method of HA-LRTI risk for inpatients, the items of the method is relatively concise and the predictive effect is accurate.