A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score

Background & Aims: We aimed at identifying specific emergency department (ED) risk factors for developing complicated acute diverticulitis (AD) and evaluate a machine learning model (ML) for predicting complicated AD. Methods: We analyzed data retrieved from unselected consecutive large bowel AD patients from five hospitals from the Mount Sinai health system, NY. The study time frame was from January 2011 through March 2021. Data were used to train and evaluate a gradient-boosting machine learning model to identify patients with complicated diverticulitis, defined as a need for invasive intervention or in-hospital mortality. The model was trained and evaluated on data from four hospitals and externally validated on held-out data from the fifth hospital. Results: The final cohort included 4997 AD visits. Of them, 129 (2.9%) visits had complicated diverticulitis. Patients with complicated diverticulitis were more likely to be men, black, and arrive by ambulance. Regarding laboratory values, patients with complicated diverticulitis had higher levels of absolute neutrophils (AUC 0.73), higher white blood cells (AUC 0.70), platelet count (AUC 0.68) and lactate (AUC 0.61), and lower levels of albumin (AUC 0.69), chloride (AUC 0.64), and sodium (AUC 0.61). In the external validation cohort, the ML model showed AUC 0.85 (95% CI 0.78–0.91) for predicting complicated diverticulitis. For Youden’s index, the model showed a sensitivity of 88% with a false positive rate of 1:3.6. Conclusions: A ML model trained on clinical measures provides a proof of concept performance in predicting complications in patients presenting to the ED with AD. Clinically, it implies that a ML model may classify low-risk patients to be discharged from the ED for further treatment under an ambulatory setting.

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Development of a Diabetes Melitus Detection and Prediction Model Using Light Gradient Boosting Machine and K-Nearest Neighbour

10.36108/ujees/1202.30.0160 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

B. A Omodunbi

Keyword(s):

Diabetes Mellitus ◽

Machine Learning ◽

Hybrid Model ◽

Learning Model ◽

Experimental Result ◽

Gradient Boosting ◽

Light Gradient ◽

Machine Learning Model ◽

Gradient Boosting Machine ◽

Receiver Operating

Diabetes mellitus is a health disorder that occurs when the blood sugar level becomes extremely high due to body resistance in producing the required amount of insulin. The aliment happens to be among the major causes of death in Nigeria and the world at large. This study was carried out to detect diabetes mellitus by developing a hybrid model that comprises of two machine learning model namely Light Gradient Boosting Machine (LGBM) and K-Nearest Neighbor (KNN). This research is aimed at developing a machine learning model for detecting the occurrence of diabetes in patients. The performance metrics employed in evaluating the finding for this study are Receiver Operating Characteristics (ROC) Curve, Five-fold Cross-validation, precision, and accuracy score. The proposed system had an accuracy of 91% and the area under the Receiver Operating Characteristic Curve was 93%. The experimental result shows that the prediction accuracy of the hybrid model is better than traditional machine learning

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Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Journal of Clinical Medicine ◽

10.3390/jcm9030875 ◽

2020 ◽

Vol 9 (3) ◽

pp. 875

Author(s):

Young Suk Kwon ◽

Moon Seong Baek

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Learning Model ◽

Gradient Boosting ◽

Learning Models ◽

Suspected Infection ◽

Machine Learning Model ◽

Failure Assessment ◽

Qsofa Score ◽

Machine Learning Models

The quick sepsis-related organ failure assessment (qSOFA) score has been introduced to predict the likelihood of organ dysfunction in patients with suspected infection. We hypothesized that machine-learning models using qSOFA variables for predicting three-day mortality would provide better accuracy than the qSOFA score in the emergency department (ED). Between January 2016 and December 2018, the medical records of patients aged over 18 years with suspected infection were retrospectively obtained from four EDs in Korea. Data from three hospitals (n = 19,353) were used as training-validation datasets and data from one (n = 4234) as the test dataset. Machine-learning algorithms including extreme gradient boosting, light gradient boosting machine, and random forest were used. We assessed the prediction ability of machine-learning models using the area under the receiver operating characteristic (AUROC) curve, and DeLong’s test was used to compare AUROCs between the qSOFA scores and qSOFA-based machine-learning models. A total of 447,926 patients visited EDs during the study period. We analyzed 23,587 patients with suspected infection who were admitted to the EDs. The median age of the patients was 63 years (interquartile range: 43–78 years) and in-hospital mortality was 4.0% (n = 941). For predicting three-day mortality among patients with suspected infection in the ED, the AUROC of the qSOFA-based machine-learning model (0.86 [95% CI 0.85–0.87]) for three -day mortality was higher than that of the qSOFA scores (0.78 [95% CI 0.77–0.79], p < 0.001). For predicting three-day mortality in patients with suspected infection in the ED, the qSOFA-based machine-learning model was found to be superior to the conventional qSOFA scores.

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Promoting head CT exams in the emergency department triage using a machine learning model

Neuroradiology ◽

10.1007/s00234-019-02293-y ◽

2019 ◽

Vol 62 (2) ◽

pp. 153-160 ◽

Cited By ~ 4

Author(s):

Eyal Klang ◽

Yiftach Barash ◽

Shelly Soffer ◽

Sigalit Bechler ◽

Yehezkel S. Resheff ◽

...

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Learning Model ◽

Head Ct ◽

Machine Learning Model ◽

Emergency Department Triage

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ML-LGBM: A Machine Learning Model based on Light Gradient Boosting Machine for the Detection of Version Number Attacks in RPL-Based Networks

IEEE Access ◽

10.1109/access.2021.3087175 ◽

2021 ◽

pp. 1-1

Author(s):

Musa Osman ◽

Jingsha He ◽

Fawaz Mahiuob Mohammed Mokbal ◽

Nafei Zhu ◽

Sirajuddin Qureshi

Keyword(s):

Machine Learning ◽

Learning Model ◽

Gradient Boosting ◽

Model Based ◽

Light Gradient ◽

Machine Learning Model ◽

Gradient Boosting Machine

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Alerting on mortality among patients discharged from the emergency department: a machine learning model

Postgraduate Medical Journal ◽

10.1136/postgradmedj-2020-138899 ◽

2020 ◽

pp. postgradmedj-2020-138899

Author(s):

Yiftach Barash ◽

Shelly Soffer ◽

Ehud Grossman ◽

Noam Tau ◽

Vera Sorin ◽

...

Keyword(s):

Machine Learning ◽

Emergency Department ◽

False Positive Rate ◽

Area Under The Curve ◽

Improve Patient Care ◽

Learning Model ◽

Gradient Boosting ◽

Free Text ◽

Machine Learning Model ◽

Discharged Patients

ObjectivesPhysicians continuously make tough decisions when discharging patients. Alerting on poor outcomes may help in this decision. This study evaluates a machine learning model for predicting 30-day mortality in emergency department (ED) discharged patients.MethodsWe retrospectively analysed visits of adult patients discharged from a single ED (1/2014–12/2018). Data included demographics, evaluation and treatment in the ED, and discharge diagnosis. The data comprised of both structured and free-text fields. A gradient boosting model was trained to predict mortality within 30 days of release from the ED. The model was trained on data from the years 2014–2017 and validated on data from the year 2018. In order to reduce potential end-of-life bias, a subgroup analysis was performed for non-oncological patients.ResultsOverall, 363 635 ED visits of discharged patients were analysed. The 30-day mortality rate was 0.8%. A majority of the mortality cases (65.3%) had a known oncological disease. The model yielded an area under the curve (AUC) of 0.97 (95% CI 0.96 to 0.97) for predicting 30-day mortality. For a sensitivity of 84% (95% CI 0.81 to 0.86), this model had a false positive rate of 1:20. For patients without a known malignancy, the model yielded an AUC of 0.94 (95% CI 0.92 to 0.95).ConclusionsAlthough not frequent, patients may die following ED discharge. Machine learning-based tools may help ED physicians identify patients at risk. An optimised decision for hospitalisation or palliative management may improve patient care and system resource allocation.

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Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation

Journal of Personalized Medicine ◽

10.3390/jpm11111055 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1055

Author(s):

Pei-Chen Lin ◽

Kuo-Tai Chen ◽

Huan-Chieh Chen ◽

Md. Mohaimenul Islam ◽

Ming-Chin Lin

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Characteristic Curve ◽

External Validation ◽

Model Performance ◽

Learning Model ◽

Gradient Boosting ◽

Machine Learning Model ◽

Extreme Gradient Boosting ◽

Development And Validation

Accurate stratification of sepsis can effectively guide the triage of patient care and shared decision making in the emergency department (ED). However, previous research on sepsis identification models focused mainly on ICU patients, and discrepancies in model performance between the development and external validation datasets are rarely evaluated. The aim of our study was to develop and externally validate a machine learning model to stratify sepsis patients in the ED. We retrospectively collected clinical data from two geographically separate institutes that provided a different level of care at different time periods. The Sepsis-3 criteria were used as the reference standard in both datasets for identifying true sepsis cases. An eXtreme Gradient Boosting (XGBoost) algorithm was developed to stratify sepsis patients and the performance of the model was compared with traditional clinical sepsis tools; quick Sequential Organ Failure Assessment (qSOFA) and Systemic Inflammatory Response Syndrome (SIRS). There were 8296 patients (1752 (21%) being septic) in the development and 1744 patients (506 (29%) being septic) in the external validation datasets. The mortality of septic patients in the development and validation datasets was 13.5% and 17%, respectively. In the internal validation, XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.86, exceeding SIRS (0.68) and qSOFA (0.56). The performance of XGBoost deteriorated in the external validation (the AUROC of XGBoost, SIRS and qSOFA was 0.75, 0.57 and 0.66, respectively). Heterogeneity in patient characteristics, such as sepsis prevalence, severity, age, comorbidity and infection focus, could reduce model performance. Our model showed good discriminative capabilities for the identification of sepsis patients and outperformed the existing sepsis identification tools. Implementation of the ML model in the ED can facilitate timely sepsis identification and treatment. However, dataset discrepancies should be carefully evaluated before implementing the ML approach in clinical practice. This finding reinforces the necessity for future studies to perform external validation to ensure the generalisability of any developed ML approaches.

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Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach

Scientific Reports ◽

10.1038/s41598-021-80985-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Eyal Klang ◽

Benjamin R. Kummer ◽

Neha S. Dangayach ◽

Amy Zhong ◽

M. Arash Kia ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care Unit ◽

Emergency Department ◽

Intensive Care ◽

Learning Model ◽

Free Text ◽

Combined Model ◽

Text Data ◽

Machine Learning Model ◽

Record Data

AbstractEarly admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200–256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80–324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87–0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91–0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92–0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings.

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Machine learning techniques to predict daily rainfall amount

Journal Of Big Data ◽

10.1186/s40537-021-00545-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Chalachew Muluken Liyew ◽

Haileyesus Amsaya Melese

Keyword(s):

Machine Learning ◽

Pearson Correlation ◽

Daily Rainfall ◽

Learning Model ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Correlation Technique ◽

Learning Techniques ◽

Machine Learning Model ◽

Extreme Gradient Boosting

AbstractPredicting the amount of daily rainfall improves agricultural productivity and secures food and water supply to keep citizens healthy. To predict rainfall, several types of research have been conducted using data mining and machine learning techniques of different countries’ environmental datasets. An erratic rainfall distribution in the country affects the agriculture on which the economy of the country depends on. Wise use of rainfall water should be planned and practiced in the country to minimize the problem of the drought and flood occurred in the country. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The Pearson correlation technique was used to select relevant environmental variables which were used as an input for the machine learning model. The dataset was collected from the local meteorological office at Bahir Dar City, Ethiopia to measure the performance of three machine learning techniques (Multivariate Linear Regression, Random Forest, and Extreme Gradient Boost). Root mean squared error and Mean absolute Error methods were used to measure the performance of the machine learning model. The result of the study revealed that the Extreme Gradient Boosting machine learning algorithm performed better than others.

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Application of Machine Learning to Interpret Steady State Drainage Relative Permeability Experiments

10.2118/207877-ms ◽

2021 ◽

Author(s):

Eric Sonny Mathew ◽

Moussa Tembely ◽

Waleed AlAmeri ◽

Emad W. Al-Shalabi ◽

Abdul Ravoof Shaik

Keyword(s):

Neural Network ◽

Machine Learning ◽

Experimental Data ◽

Steady State ◽

Relative Permeability ◽

Learning Model ◽

Gradient Boosting ◽

Data Set ◽

Machine Learning Model ◽

Extreme Gradient Boosting

Abstract A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, three different machine learning models was developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. The three different models that were tested and compared were extreme gradient boosting (XGB), deep neural network (DNN) and recurrent neural network (RNN) algorithms. Based on existing mathematical models, a leading edge framework was developed where a large database of Kr and Pc curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were utilized to estimate Kr curves based on Darcy's law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the machine learning model. The entire data set was split into 80% for training and 20% for testing. K-fold cross validation technique was applied to increase the model accuracy by splitting the 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, 9 folds were used for training and the remaining one was used for model validation. Once the model is trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The machine learning model learns to capture fluid flow behavior inside the core from the training dataset. The trained/tested model was thereby employed to estimate Kr curves based on available experimental results. The performance of the developed model was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective cross plots along with comparisons of ground-truth versus AI predicted curves indicate that the model is capable of making accurate predictions with error percentage between 0.2 and 0.6% on history matching experimental data for all the three tested ML techniques (XGB, DNN, and RNN). This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The results also include a comparison between classical machine learning approaches, shallow and deep neural networks in terms of accuracy in predicting the final Kr curves. The various models discussed in this research work currently focusses on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

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