ArticleRisk management and healthcare policy2021
Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia.
Article in Risk management and healthcare policy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Artificial Intelligence Applications in Pneumonia: Diagnosis and Outcome Prediction.Current pulmonology reports · 2026Review
- Machine learning-based model for predicting all-cause mortality in severe pneumonia.BMJ open respiratory research · 2025Article
- Advancing risk factor identification for pediatric lobar pneumonia: the promise of machine learning technologies.Frontiers in pediatrics · 2025Article
- Revolutionizing oncology care: pioneering AI models to foresee pneumonia-related mortality.Frontiers in oncology · 2025Article
- Incorporation of Suppression of Tumorigenicity 2 into Random Survival Forests for Enhancing Prediction of Short-Term Prognosis in Community-ACQUIRED Pneumonia.Journal of clinical medicine · 2022Article
- Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images.Canadian respiratory journal · 2022Article
- Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia.Frontiers in bioengineering and biotechnology · 2022Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCommunity-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.
methodsThis 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.
resultsA 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.
conclusionThis 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.
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