XGBoost Algorithm Prediction of Critical Care Outcome For Adult Patients Presenting To Emergency Department Using Initial Triage Information (Preprint)

2021 ◽  
Author(s):  
Hyoungju Yun ◽  
Jinwook Choi ◽  
Jeong Ho Park
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Emergency Department ◽  
Critical Care ◽  
Adult Patients ◽  
Baseline Model ◽  
Discriminative Performance ◽  
Extreme Gradient Boosting ◽  
Lemeshow Test ◽  
Care Outcome

BACKGROUND Emergency department (ED) triage system to classify and prioritize patients at high risk from less urgent continues to be a challenge. OBJECTIVE This study, comprising 80,433 patients, aims to develop a machine learning algorithm prediction model of critical care outcome for adult patients using information collected during ED triage and compare the performance with that of the baseline model using Korean Triage and Acuity Scale (KTAS). METHODS To predict the need of critical care, we used 13 predictors from triage information: age, gender, mode of ED arrival, time interval between onset and ED arrival, reason of ED visit, chief complaints, systolic blood pressure, diastolic blood pressure, pulse rate, respiratory rate, body temperature, oxygen saturation and level of consciousness. The baseline model with KTAS was developed using logistic regression and the machine learning model with 13 variables was generated using extreme gradient boosting (XGB) and deep neural network (DNN) algorithms. The discrimination was measured by area under the receiver operating characteristic curve(AUC). The ability of calibration with Hosmer–Lemeshow test and reclassification with net reclassification index (NRI) were evaluated. The calibration plot and partial dependence plot were used in analysis. RESULTS The AUC of the model with the full set of variables (0.833–0.861) was better than that of the baseline model (0.796). The XGB model of AUC 0.861 (0.848, 0.874, 95% CI) showed a higher discriminative performance than DNN model of 0.833(0.819, 0.848). The XGB and DNN models proved better reclassification than the baseline model with positive net reclassification index. The XGB models was well calibrated (Hosmer-Lemeshow test p>0.05); however, the DNN showed poor calibration power (H-L test p<0.001). We further interpreted non-linear association between variables and critical care prediction. CONCLUSIONS Our study demonstrated that the performance of the XGB model using initial information at ED triage for predicting patients in need of critical care outperformed the conventional model with KTAS.

scholarly journals Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

2021 ◽  
Author(s):  
Eun-Seok Choi ◽  
Jae Ang Sim ◽  
Young Gon Na ◽  
Jong- Keun Seon ◽  
Hyun Dae Shin
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Confidence Interval ◽  
Diagnostic Accuracy ◽  
Septic Arthritis ◽  
Inflammatory Arthritis ◽  
Supervised Machine Learning ◽  
Diagnostic Model ◽  
Extreme Gradient Boosting ◽  
Wbc Count

Abstract Purpose Prompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy. Methods Patients (n = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared. Results Synovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (P = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (P = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751–0.923) than synovial WBC count (0.740, 95% confidence interval 0.684–0.791; P = 0.033). The developed algorithm was deployed as a free access web-based application (www.septicknee.com). Conclusion The diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model. Level of evidence Diagnostic study Level III (Case–control study).

scholarly journals Machine Learning for the Prediction of Progression in Patients with Acute Kidney Injury in Critical Care

2021 ◽  
Author(s):  
Lifan Zhang ◽  
Canzheng Wei ◽  
Yunxia Feng ◽  
Aijia Ma ◽  
Yan Kang
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Critical Care ◽  
Intensive Care ◽  
Logistic Regression Model ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Stage 1

Abstract Background: Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3. Methods: Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care (MIMIC-III), were included. We excluded patients who had underwent RRT or progressed to AKI stage 3 within 72 hours of the first AKI diagnosis. We also excluded patients with chronic kidney disease (CKD). We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve (ROC), and precision-recall curves (PRC). Results: We included 25711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes (MODS), blood urea nitrogen (BUN), sepsis, and respiratory failure were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression (AU-ROC, 0.926; 95%CI, 0.917 to 0.931 vs. 0.784; 95%CI, 0.771 to 0.796, respectively). Conclusions: The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research. Keywords: Acute kidney injury; Critical care; Logistic Models; Extreme gradient boosting

scholarly journals Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department

2021 ◽  
Vol 10 (9) ◽  
pp. 1875
Author(s):  
I-Min Chiu ◽  
Chi-Yung Cheng ◽  
Wun-Huei Zeng ◽  
Ying-Hsien Huang ◽  
Chun-Hung Richard Lin
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Bacterial Infections ◽  
Clinical Symptoms ◽  
Learning Algorithm ◽  
Gradient Boosting ◽  
P Value ◽  
Young Infants ◽  
Extreme Gradient Boosting ◽  
Sensitivity Level

Background: The aim of this study was to develop and evaluate a machine learning (ML) model to predict invasive bacterial infections (IBIs) in young febrile infants visiting the emergency department (ED). Methods: This retrospective study was conducted in the EDs of three medical centers across Taiwan from 2011 to 2018. We included patients age in 0–60 days who were visiting the ED with clinical symptoms of fever. We developed three different ML algorithms, including logistic regression (LR), supportive vector machine (SVM), and extreme gradient boosting (XGboost), comparing their performance at predicting IBIs to a previous validated score system (IBI score). Results: During the study period, 4211 patients were included, where 126 (3.1%) had IBI. A total of eight, five, and seven features were used in the LR, SVM, and XGboost through the feature selection process, respectively. The ML models can achieve a better AUROC value when predicting IBIs in young infants compared with the IBI score (LR: 0.85 vs. SVM: 0.84 vs. XGBoost: 0.85 vs. IBI score: 0.70, p-value < 0.001). Using a cost sensitive learning algorithm, all ML models showed better specificity in predicting IBIs at a 90% sensitivity level compared to an IBI score > 2 (LR: 0.59 vs. SVM: 0.60 vs. XGBoost: 0.57 vs. IBI score >2: 0.43, p-value < 0.001). Conclusions: All ML models developed in this study outperformed the traditional scoring system in stratifying low-risk febrile infants after the standardized sensitivity level.

scholarly journals A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department

2021 ◽  
Author(s):  
Ting Ting Wu ◽  
Ruo Fei Zheng ◽  
Zhi Zhong Lin ◽  
Hai Rong Gong ◽  
Hong Li
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Critical Care ◽  
Chest Pain ◽  
Predictive Value ◽  
Lasso Regression ◽  
Training Set ◽  
Critical Care Outcomes ◽  
Heart Score ◽  
Care Outcomes

Abstract Background: Currently, the risk stratification of critically ill patient with chest pain is a challenge. We aimed to use machine learning approach to predict the critical care outcomes in patients with chest pain, and simultaneously compared its performance with the HEART score. Methods: This was a retrospective, case-control study in patients with acute non-traumatic chest pain who presented to the emergency department (ED) between January 2017 and December 2019. The outcomes included cardiac arrest, transfer to ICU, and death during treatment in ED. In the randomly sampled training set (70%), a LASSO regression model was developed, and presented with nomogram. The performance was measured in both training set (70% participants) and testing set (30% participants), and findings were compared with the HEART score.Results: We proposed a LASSO regression model incorporating mode of arrival, reperfusion therapy, Killip class, systolic BP, SCr, CKMB, and BNP as independent predictors of critical care outcomes in patients with chest pain. Our model significantly outperformed the HEART score with AUC of 0.953 (95%CI: 0.922 - 0.984) and 0.754 (95%CI: 0.675 - 0.832), respectively. Consistently, our model demonstrated better outcomes regarding the metrics of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Similarly, the decision curve analysis elucidated a greater net benefit of our model over the full ranges of clinical thresholds.Conclusion: We present a promising model for predicting the critical care outcomes in patients with chest pain, and provide substantial support to its application as a decision-making tool in ED.

scholarly journals A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 82
Author(s):  
Chun-Chuan Hsu ◽  
Cheng-CJ Chu ◽  
Ching-Heng Lin ◽  
Chien-Hsiung Huang ◽  
Chip-Jin Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Abdominal Pain ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Research Database ◽  
Machine Learning Model ◽  
Extreme Gradient Boosting ◽  
Return Visits

Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.

scholarly journals A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ting Ting Wu ◽  
Ruo Fei Zheng ◽  
Zhi Zhong Lin ◽  
Hai Rong Gong ◽  
Hong Li
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Critical Care ◽  
Chest Pain ◽  
Regression Model ◽  
Predictive Value ◽  
Lasso Regression ◽  
Training Set ◽  
Critical Care Outcomes ◽  
Care Outcomes

Abstract Background Currently, the risk stratification of critically ill patient with chest pain is a challenge. We aimed to use machine learning approach to predict the critical care outcomes in patients with chest pain, and simultaneously compare its performance with HEART, GRACE, and TIMI scores. Methods This was a retrospective, case-control study in patients with acute non-traumatic chest pain who presented to the emergency department (ED) between January 2017 and December 2019. The outcomes included cardiac arrest, transfer to ICU, and death during treatment in ED. In the randomly sampled training set (70%), a LASSO regression model was developed, and presented with nomogram. The performance was measured in both training set (70% participants) and testing set (30% participants), and findings were compared with the three widely used scores. Results We proposed a LASSO regression model incorporating mode of arrival, reperfusion therapy, Killip class, systolic BP, serum creatinine, creatine kinase-MB, and brain natriuretic peptide as independent predictors of critical care outcomes in patients with chest pain. Our model significantly outperformed the HEART, GRACE, TIMI score with AUC of 0.953 (95%CI: 0.922–0.984), 0.754 (95%CI: 0.675–0.832), 0.747 (95%CI: 0.664–0.829), 0.735 (95%CI: 0.655–0.815), respectively. Consistently, our model demonstrated better outcomes regarding the metrics of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Similarly, the decision curve analysis elucidated a greater net benefit of our model over the full ranges of clinical thresholds. Conclusion We present an accurate model for predicting the critical care outcomes in patients with chest pain, and provide substantial support to its application as a decision-making tool in ED.

scholarly journals January 2021 Critical Care Case of the Month: A 35-Year-Old Man Found Down on the Street

2021 ◽  
Vol 22 (1) ◽  
pp. 1-7
Author(s):  
John Lee ◽  
◽  
Ling Obrand ◽  
Janet Campion
Keyword(s):  
Blood Pressure ◽  
Emergency Department ◽  
Heart Rate ◽  
Septic Shock ◽  
Critical Care ◽  
Chart Review ◽  
Sickle Cell Trait ◽  
Alcohol Intoxication ◽  
Dental Procedure ◽  
History Of

No abstract available. Article truncated after 150 words. History of Present Illness A 35-year-old African-American man with a history of alcohol abuse presented to Emergency Department after he was found down. He was seen by a passerby on the street who witnessed the patient fall with a possible convulsive event. He was brought in by ambulance and was unconscious and unresponsive. PMH, SH, and FH The patient had a history of prior ICU admission in Yuma with septic shock secondary to a dental procedure requiring a tracheostomy in 2018. He also had a history of alcohol intoxication requiring an ED visit about 10 years ago and history of sickle cell trait. Per chart review, the patient took no home medications. Further history was unable to be obtained due to the patient's condition. Physical Examination On arrival the patient had a core temperature of 41°C, systolic blood pressure in the 70s-80s, heart rate of 185, respiratory rate of 19 …

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