Machine-Learning Model for Mortality Prediction in Patients with Community-Acquired Pneumonia: Development and Validation Study

2021 ◽  
Author(s):  
Catia Cilloniz ◽  
Logan Ward ◽  
Mads Lause Mogensen ◽  
Juan M. Pericàs ◽  
Raul Mendez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Validation Study ◽  
Learning Model ◽  
Community Acquired Pneumonia ◽  
Mortality Prediction ◽  
Machine Learning Model ◽  
Development And Validation

scholarly journals Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant

2021 ◽  
Vol 4 (4) ◽  
pp. e214514
Author(s):  
Margaret L. Lind ◽  
Stephen J. Mooney ◽  
Marco Carone ◽  
Benjamin M. Althouse ◽  
Catherine Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cell ◽  
Stem Cell Transplant ◽  
Learning Model ◽  
Cell Transplant ◽  
Bacterial Sepsis ◽  
Machine Learning Model ◽  
Development And Validation

scholarly journals Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregor Lichtner ◽  
Felix Balzer ◽  
Stefan Haufe ◽  
Niklas Giesa ◽  
Fridtjof Schiefenhövel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critically Ill ◽  
Prediction Models ◽  
Predictive Performance ◽  
Learning Model ◽  
Mortality Prediction ◽  
Viral Pneumonia ◽  
Machine Learning Model ◽  
Mortality Prediction Models ◽  
Time Courses

AbstractIn a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86–0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61–0.65]), SAPS2 (0.72 [95% CI 0.71–0.74]) and SOFA (0.76 [95% CI 0.75–0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73–0.78]) and Wang (laboratory: 0.62 [95% CI 0.59–0.65]; clinical: 0.56 [95% CI 0.55–0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70–0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.

scholarly journals Sa1082 – Development and Validation of a Machine-Learning Model to Predict Adequate Bowel Preparation in a Sample of US Veterans

2019 ◽  
Vol 156 (6) ◽  
pp. S-263-S-264
Author(s):  
Jacob E. Kurlander ◽  
Sameer D. Saini ◽  
Rachel Lipson ◽  
Stacy B. Menees ◽  
Shahnaz Sultan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bowel Preparation ◽  
Learning Model ◽  
Machine Learning Model ◽  
Us Veterans ◽  
Development And Validation

scholarly journals Interpretable Machine Learning Model for Mortality Prediction in ICU: A Multicenter Study

2020 ◽  
Author(s):  
Ka Man Fong ◽  
Shek Yin Au ◽  
George Wing Yiu Ng ◽  
Anne Kit Hung Leung
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Severity Score ◽  
Learning Model ◽  
Mortality Prediction ◽  
Large Dataset ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Precision Recall Curve ◽  
Apache Iv

Abstract Background: Researchers have long been struggling to improve the disease severity score in mortality prediction in ICU. The digitalization of medical health records and advancement of computation power have promoted the use of machine learning in critical care. This study aimed to develop an interpretable machine learning model using datasets from multicenters, and to compare with the APACHE IV, in predicting hospital mortality of patients admitted to ICU.Method: The datasets were assembled from the eICU database including 136145 patients across 208 hospitals throughout the U.S. and 5 ICUs in Hong Kong, including 10909 patients. The two datasets were first combined into one large dataset before 80:20 stratified split into the training set and the test set. The XGBoost machine algorithm was chosen to predict the hospital mortality. The variables in the model were the same as those included in the APACHE IV score. The discrimination and calibration of the model were assessed. The model would be interpreted using the Shapley Additive explanations values.Results: Of the 147054 patients in the whole cohort, the hospital mortality was 9.3%. The area under the precision-recall curve for the XGBoost algorithm was 0.57, and 0.49 for APACHE IV. Similarly, the XGBoost reached an area under the receiving operating curve (AUROC) of 0.90, while APACHE IV had an AUROC of 0.87. Additionally, the XGBoost algorithm showed better calibration than the APACHE IV. The three most important variables were age, heart rate, and whether the patient was on ventilator.Conclusions: The severity score developed by machine learning model using mutlicenter datasets outperformed the APACHE IV in predicting hospital mortality for patients admitted to ICU.

scholarly journals Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes

2021 ◽  
Vol 4 (5) ◽  
pp. e2111315
Author(s):  
Mathieu Ravaut ◽  
Vinyas Harish ◽  
Hamed Sadeghi ◽  
Kin Kwan Leung ◽  
Maksims Volkovs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Learning Model ◽  
Health Data ◽  
Administrative Health Data ◽  
Machine Learning Model ◽  
Development And Validation
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