Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia

Abstract

Ding-Yun Feng,1,* Yong Ren,2,* Mi Zhou,3,* Xiao-Ling Zou,1 Wen-Bin Wu,1 Hai-Ling Yang,1 Yu-Qi Zhou,1 Tian-Tuo Zhang1 1Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, Guangzhou, People’s Republic of China; 2Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, People’s Republic of China; 3Department of Surgery Intensive Care Unit, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Tian-Tuo Zhang; Yu-Qi ZhouDepartment of Pulmonary and Critical Care Medicine, Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, People’s Republic of ChinaTel [phone]Email [email protected]; [email protected]: Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality worldwide. Although there are many predictors of death for CAP, there are still some limitations. This study aimed to build a simple and accurate model based on available and common clinical-related feature variables for predicting CAP mortality by adopting machine learning techniques.Methods: This was a single-center retrospective study. The data used in this study were collected from all patients (≥ 18 years) with CAP admitted to research hospitals between January 2012 and April 2020. Each patient had 62 clinical-related features, including clinical diagnostic and treatment features. Patients were divided into two endpoints, and by using Tensorflow2.4.1 as the modeling framework, a three-layer fully connected neural network (FCNN) was built as a base model for classification. For a comprehensive comparison, seven classical machine learning methods and their integrated stacking patterns were introduced to model and compare the same training and test data.Results: A total of 3997 patients with CAP were included; 205 (5.12%) died in the hospital. After performing deep learning methods, this study established an ensemble FCNN model based on 12 FCNNs. By comparing with seven classical machine learning methods, the area under the curve of the ensemble FCNN was 0.975 when using deep learning algorithms to classify poor from good prognosis based on available and common clinical-related feature variables. The predicted outcome was poor prognosis if the ControlNet’s poor prognosis score was greater than the cutoff value of 0.50. To confirm the scientificity of the ensemble FCNN model, this study analyzed the weight of random forest features and found that mainstream prognostic features still held weight, although the model is perfect after integrating other factors considered less important by previous studies.Conclusion: This study used deep learning algorithms to classify prognosis based on available and common clinical-related feature variables in patients with CAP with high accuracy and good generalizability. Every clinical-related feature is important to the model.Keywords: deep learning, community-acquired pneumonia, mortality, predictor

Keywords

• deep learning
• community-acquired pneumonia
• mortality
• predictor