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

Feng, Ding-Yun; Ren, Yong; Zhou, Mei; Zou, Xiaoling; Wu, Wenbin; Yang, Hailing; Zhou, Yuqi; Zhang, Tiantuo

doi:10.2147/rmhp.s317735

Cited by 7 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And several studies have employed machine learning methods to predict mortality from pneumonia ( Cooper et al, 1997 ; Wiemken et al, 2017 ; Kang et al, 2020 ), while other adverse outcomes have received less attention, especially one-year post-enrollment status. Feng et al built a three-layer fully connected neural network to classify the prognosis of CAP patients with high accuracy and good generalizability by using ML techniques to predict CAP mortality ( Feng et al, 2021 ). The proposed ML-based models including the CURB-65 score could accurately predict the death within 30 days or initial admission to the ICU from the emergency department with an AUC of 0.844 ( Kang et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia

Guo

Chu

et al. 2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Background: The ability to assess adverse outcomes in patients with community-acquired pneumonia (CAP) could improve clinical decision-making to enhance clinical practice, but the studies remain insufficient, and similarly, few machine learning (ML) models have been developed.Objective: We aimed to explore the effectiveness of predicting adverse outcomes in CAP through ML models.Methods: A total of 2,302 adults with CAP who were prospectively recruited between January 2012 and March 2015 across three cities in South America were extracted from DryadData. After a 70:30 training set: test set split of the data, nine ML algorithms were executed and their diagnostic accuracy was measured mainly by the area under the curve (AUC). The nine ML algorithms included decision trees, random forests, extreme gradient boosting (XGBoost), support vector machines, Naïve Bayes, K-nearest neighbors, ridge regression, logistic regression without regularization, and neural networks. The adverse outcomes included hospital admission, mortality, ICU admission, and one-year post-enrollment status.Results: The XGBoost algorithm had the best performance in predicting hospital admission. Its AUC reached 0.921, and accuracy, precision, recall, and F1-score were better than those of other models. In the prediction of ICU admission, a model trained with the XGBoost algorithm showed the best performance with AUC 0.801. XGBoost algorithm also did a good job at predicting one-year post-enrollment status. The results of AUC, accuracy, precision, recall, and F1-score indicated the algorithm had high accuracy and precision. In addition, the best performance was seen by the neural network algorithm when predicting death (AUC 0.831).Conclusions: ML algorithms, particularly the XGBoost algorithm, were feasible and effective in predicting adverse outcomes of CAP patients. The ML models based on available common clinical features had great potential to guide individual treatment and subsequent clinical decisions.

show abstract

Section: Discussionmentioning

confidence: 99%

Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia

Guo

Chu

et al. 2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…In addition, the specificity of both scores is weak [ 49 ]. Several studies have demonstrated that the prognostic performance of machine learning models was better than PSI and CURB-65 [ 9 , 12 , 13 , 16 , 50 , 51 , 52 ]. In our study, the 30-day AUC and C-index of the RSF model were 0.9428 (95% CI, 0.9113–0.9744) and 0.8595 (95% CI, 0.8445–0.8745), respectively, both higher than those of PSI score and the CURB-65 score.…”

Section: Discussionmentioning

confidence: 99%

Incorporation of Suppression of Tumorigenicity 2 into Random Survival Forests for Enhancing Prediction of Short-Term Prognosis in Community-ACQUIRED Pneumonia

Zhang

Zeng

Lin

et al. 2022

JCM

View full text Add to dashboard Cite

(1) Background: Biomarker and model development can help physicians adjust the management of patients with community-acquired pneumonia (CAP) by screening for inpatients with a low probability of cure early in their admission; (2) Methods: We conducted a 30-day cohort study of newly admitted adult CAP patients over 20 years of age. Prognosis models to predict the short-term prognosis were developed using random survival forest (RSF) method; (3) Results: A total of 247 adult CAP patients were studied and 208 (84.21%) of them reached clinical stability within 30 days. The soluble form of suppression of tumorigenicity-2 (sST2) was an independent predictor of clinical stability and the addition of sST2 to the prognosis model could improve the performance of the prognosis model. The C-index of the RSF model for predicting clinical stability was 0.8342 (95% CI, 0.8086–0.8598), which is higher than 0.7181 (95% CI, 0.6933–0.7429) of CURB 65 score, 0.8025 (95% CI, 0.7776–8274) of PSI score, and 0.8214 (95% CI, 0.8080–0.8348) of cox regression. In addition, the RSF model was associated with adverse clinical events during hospitalization, ICU admissions, and short-term mortality; (4) Conclusions: The RSF model by incorporating sST2 was more accurate than traditional methods in assessing the short-term prognosis of CAP patients.

show abstract

“…Tese results suggest that the deep learning model is useful for prognostic evaluation using CXR images in patients with pneumonia at diagnosis. Feng et al developed a deep learning prognostic model for CAP using nonimaging data (such as comorbidities, vitals, and blood biomarkers), with a sensitivity of 74.4% to 98.2%, specifcity of 83.1% to 100%, and accuracy of 79.3% to 99% [30]. Furthermore, deep learning models have been reported to predict the severity of COVID-19 pneumonia using CXR images [21][22][23].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images

Anai¹,

Hisasue²,

Takaki³

et al. 2022

Canadian Respiratory Journal

View full text Add to dashboard Cite

Background and Aims. Chest X-ray (CXR) is indispensable to the assessment of severity, diagnosis, and management of pneumonia. Deep learning is an artificial intelligence (AI) technology that has been applied to the interpretation of medical images. This study investigated the feasibility of classifying fatal pneumonia based on CXR images using deep learning models on publicly available platforms. Methods. CXR images of patients with pneumonia at diagnosis were labeled as fatal or nonfatal based on medical records. We applied CXR images from 1031 patients with nonfatal pneumonia and 243 patients with fatal pneumonia for training and self-evaluation of the deep learning models. All labeled CXR images were randomly allocated to the training, validation, and test datasets of deep learning models. Data augmentation techniques were not used in this study. We created two deep learning models using two publicly available platforms. Results. The first model showed an area under the precision-recall curve of 0.929 with a sensitivity of 50.0% and a specificity of 92.4% for classifying fatal pneumonia. We evaluated the performance of our deep learning models using sensitivity, specificity, PPV, negative predictive value (NPV), accuracy, and F1 score. Using the external validation test dataset of 100 CXR images, the sensitivity, specificity, accuracy, and F1 score were 68.0%, 86.0%, 77.0%, and 74.7%, respectively. In the original dataset, the performance of the second model showed a sensitivity, specificity, and accuracy of 39.6%, 92.8%, and 82.7%, respectively, while external validation showed values of 38.0%, 92.0%, and 65.0%, respectively. The F1 score was 52.1%. These results were comparable to those obtained by respiratory physicians and residents. Conclusions. The deep learning models yielded good accuracy in classifying fatal pneumonia. By further improving the performance, AI could assist physicians in the severity assessment of patients with pneumonia.

show abstract

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

Cited by 7 publications

References 28 publications

Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia

Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia

Incorporation of Suppression of Tumorigenicity 2 into Random Survival Forests for Enhancing Prediction of Short-Term Prognosis in Community-ACQUIRED Pneumonia

Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images

Contact Info

Product

Resources

About