scholarly journals Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Meng Hsuen Hsieh ◽  
Meng Ju Hsieh ◽  
Chin-Ming Chen ◽  
Chia-Chang Hsieh ◽  
Chien-Ming Chao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Models ◽  
Unplanned Extubation ◽  
Prediction Of Mortality ◽  
Machine Learning Models

scholarly journals Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Longxiang Su ◽  
Zheng Xu ◽  
Fengxiang Chang ◽  
Yingying Ma ◽  
Shengjun Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Early Prediction ◽  
Learning Models ◽  
Prediction Of Mortality ◽  
Severity Prediction ◽  
Machine Learning Models

Background: Early prediction of the clinical outcome of patients with sepsis is of great significance and can guide treatment and reduce the mortality of patients. However, it is clinically difficult for clinicians.Methods: A total of 2,224 patients with sepsis were involved over a 3-year period (2016–2018) in the intensive care unit (ICU) of Peking Union Medical College Hospital. With all the key medical data from the first 6 h in the ICU, three machine learning models, logistic regression, random forest, and XGBoost, were used to predict mortality, severity (sepsis/septic shock), and length of ICU stay (LOS) (>6 days, ≤ 6 days). Missing data imputation and oversampling were completed on the dataset before introduction into the models.Results: Compared to the mortality and LOS predictions, the severity prediction achieved the best classification results, based on the area under the operating receiver characteristics (AUC), with the random forest classifier (sensitivity = 0.65, specificity = 0.73, F1 score = 0.72, AUC = 0.79). The random forest model also showed the best overall performance (mortality prediction: sensitivity = 0.50, specificity = 0.84, F1 score = 0.66, AUC = 0.74; LOS prediction: sensitivity = 0.79, specificity = 0.66, F1 score = 0.69, AUC = 0.76) among the three models. The predictive ability of the SOFA score itself was inferior to that of the above three models.Conclusions: Using the random forest classifier in the first 6 h of ICU admission can provide a comprehensive early warning of sepsis, which will contribute to the formulation and management of clinical decisions and the allocation and management of resources.

scholarly journals Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Antonin Dauvin ◽  
Carolina Donado ◽  
Patrik Bachtiger ◽  
Ke-Chun Huang ◽  
Christopher Martin Sauer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Impaired Renal Function ◽  
Kidney Injury ◽  
Learning Models ◽  
Intensive Care Patients ◽  
Using Data ◽  
Machine Learning Models ◽  
Baseline Hemoglobin

AbstractPatients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86–0.89) classify an individual patient’s baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.

scholarly journals Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiawei He ◽  
Jin Lin ◽  
Meili Duan
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Intensive Care ◽  
Kidney Injury ◽  
Learning Models ◽  
Short Term ◽  
Acute Kidney Disease ◽  
Mimic Iii ◽  
Machine Learning Models

Background: Sepsis-associated acute kidney injury (AKI) is frequent in patients admitted to intensive care units (ICU) and may contribute to adverse short-term and long-term outcomes. Acute kidney disease (AKD) reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models to predict the occurrence of AKD in patients with sepsis-associated AKI.Methods: Using clinical data from patients with sepsis in the ICU at Beijing Friendship Hospital (BFH), we studied whether the following three machine learning models could predict the occurrence of AKD using demographic, laboratory, and other related variables: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), decision trees, and logistic regression. In addition, we externally validated the results in the Medical Information Mart for Intensive Care III (MIMIC III) database. The outcome was the diagnosis of AKD when defined as AKI prolonged for 7–90 days according to Acute Disease Quality Initiative-16.Results: In this study, 209 patients from BFH were included, with 55.5% of them diagnosed as having AKD. Furthermore, 509 patients were included from the MIMIC III database, of which 46.4% were diagnosed as having AKD. Applying machine learning could successfully achieve very high accuracy (RNN-LSTM AUROC = 1; decision trees AUROC = 0.954; logistic regression AUROC = 0.728), with RNN-LSTM showing the best results. Further analyses revealed that the change of non-renal Sequential Organ Failure Assessment (SOFA) score between the 1st day and 3rd day (Δnon-renal SOFA) is instrumental in predicting the occurrence of AKD.Conclusion: Our results showed that machine learning, particularly RNN-LSTM, can accurately predict AKD occurrence. In addition, Δ SOFAnon−renal plays an important role in predicting the occurrence of AKD.

scholarly journals Prediction of central venous catheter-associated deep venous thrombosis in pediatric critical care settings

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Haomin Li ◽  
Yang Lu ◽  
Xian Zeng ◽  
Cangcang Fu ◽  
Huilong Duan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Deep Learning ◽  
Central Venous Catheter ◽  
Venous Thrombosis ◽  
Venous Catheter ◽  
Learning Models ◽  
Central Venous ◽  
Time Points ◽  
Machine Learning Models

Abstract Background An increase in the incidence of central venous catheter (CVC)-associated deep venous thrombosis (CADVT) has been reported in pediatric patients over the past decade. At the same time, current screening guidelines for venous thromboembolism risk have low sensitivity for CADVT in hospitalized children. This study utilized a multimodal deep learning model to predict CADVT before it occurs. Methods Children who were admitted to intensive care units (ICUs) between December 2015 and December 2018 and with CVC placement at least 3 days were included. The variables analyzed included demographic characteristics, clinical conditions, laboratory test results, vital signs and medications. A multimodal deep learning (MMDL) model that can handle temporal data using long short-term memory (LSTM) and gated recurrent units (GRUs) was proposed for this prediction task. Four benchmark machine learning models, logistic regression (LR), random forest (RF), gradient boosting decision tree (GBDT) and a published cutting edge MMDL, were used to compare and evaluate the models with a fivefold cross-validation approach. Accuracy, recall, area under the ROC curve (AUC), and average precision (AP) were used to evaluate the discrimination of each model at three time points (24 h, 48 h and 72 h) before CADVT occurred. Brier score and Spiegelhalter’s z test were used measure the calibration of these prediction models. Results A total of 1830 patients were included in this study, and approximately 15% developed CADVT. In the CADVT prediction task, the model proposed in this paper significantly outperforms both traditional machine learning models and existing multimodal deep learning models at all 3 time points. It achieved 77% accuracy and 90% recall at 24 h before CADVT was discovered. It can be used to accurately predict the occurrence of CADVT 72 h in advance with an accuracy of greater than 75%, a recall of more than 87%, and an AUC value of 0.82. Conclusion In this study, a machine learning method was successfully established to predict CADVT in advance. These findings demonstrate that artificial intelligence (AI) could provide measures for thromboprophylaxis in a pediatric intensive care setting.

scholarly journals A machine-learning approach for dynamic prediction of sepsis-induced coagulopathy in critically ill patients with sepsis: an integrated analysis of the MIMIC-IV and eICU-CRD databases

2020 ◽  
Author(s):  
Qin-Yu Zhao ◽  
Le-Ping Liu ◽  
Jing-Chao Luo ◽  
Yan-Wei Luo ◽  
Huan Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Intensive Care ◽  
External Validation ◽  
Compact Model ◽  
Bayesian Optimization ◽  
Integrated Analysis ◽  
Research Database ◽  
Learning Models ◽  
Machine Learning Models

Abstract Background Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients. Methods Machine-learning models were developed based on septic patients who were older than 18 years and stayed in intensive care units (ICUs) for more than 24 hours in Medical Information Mart for Intensive Care (MIMIC)-IV. Eighty-eight potential predictors were extracted, and 15 various machine-learning models assessed the daily risk of SIC. The most potent model was selected based on its accuracy and Area Under the receiver operating characteristic Curve (AUC), followed by fine-grained hyperparameter adjustment using the Bayesian Optimization Algorithm. The effects of features on prediction scores were measured using the SHapley Additive exPlanations (SHAP) values. A compact model was developed, based on 15 features selected according to their importance and clinical availability. Two models were compared with Logistic Regression and SIC scores in terms of SIC prediction. Additionally, an external validation was performed in the eICU Collaborative Research Database (eICU-CRD). Results Of 11362 patients in MIMIC-IV included in the final cohort, a total of 6744 (59%) patients had SIC during sepsis, and 16183 samples were extracted. The model named Categorical Boosting (CatBoost) had the greatest AUC in our study (0.869 [0.850, 0.886]). Coagulation profile and renal function indicators are the most important features to predict SIC. A compact model was developed with the AUC of 0.854 [0.832, 0.872], while the AUCs of Logistic Regression and SIC scores were 0.746 [0.735, 0.755] and 0.709 [0.687, 0.733], respectively. A cohort of 35252 septic patients in eICU-CRD was analyzed. The AUCs of the full and the compact models in external validation were 0.842 [0.837, 0.846] and 0.803 [0.798, 0.809], respectively, which were still larger than those of Logistic Regression (0.660 [0.653, 0.667]) and SIC scores (0.752 [0.747, 0.757]). Prediction results can be illustrated by using SHAP values in the instance level, which makes our models clinically interpretable. Conclusions We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores. Prediction results of our two models can be interpreted by using SHAP values.

scholarly journals Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: a single center pilot study

2018 ◽  
Author(s):  
Sam Ghazal ◽  
Michael Sauthier ◽  
David Brossier ◽  
Wassim Bouachir ◽  
Philippe Jouvet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Pilot Study ◽  
High Resolution ◽  
Oxygen Saturation ◽  
Predictive Model ◽  
Critically Ill Children ◽  
Learning Models ◽  
Machine Learning Models

AbstractClinicians’ experts in mechanical ventilation are not continuously at each patient’s bedside in an intensive care unit to adjust mechanical ventilation settings and to analyze the impact of ventilator settings adjustments on gas exchange. The development of clinical decision support systems analyzing patients’ data in real time offers an opportunity to fill this gap. The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict hemoglobin oxygen saturation 5 min after a ventilator setting change. Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 7.105 rows of data were obtained from 610 patients, discretized into 3 class labels. Due to data imbalance, four different data balancing process were applied and two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with accuracies of 76%, 62% and 96% for the SpO2 class “< 84%”, “85 to 91%” and “> 92%”, respectively. This pilot study using machine learning predictive model resulted in an algorithm with good accuracy. To obtain a robust algorithm, more data are needed, suggesting the need of multicenter pediatric intensive care high resolution databases.

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