Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm 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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Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study

Diagnostics ◽

10.3390/diagnostics11112102 ◽

2021 ◽

Vol 11 (11) ◽

pp. 2102

Author(s):

Eyal Klang ◽

Robert Freeman ◽

Matthew A. Levin ◽

Shelly Soffer ◽

Yiftach Barash ◽

...

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Acute Diverticulitis ◽

External Validation ◽

White Blood Cells ◽

Learning Model ◽

Complicated Diverticulitis ◽

Gradient Boosting ◽

Proof Of Concept ◽

Machine Learning Model

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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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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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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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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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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Early Detection of Readmission Risk for Decision Support Based on Clinical Notes

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3304 ◽

2021 ◽

Vol 11 (2) ◽

pp. 529-534

Author(s):

Kareen Teo ◽

Ching Wai Yong ◽

Joon Huang Chuah ◽

Belinda Pingguan Murphy ◽

Khin Wee Lai

Keyword(s):

Machine Learning ◽

Characteristic Curve ◽

Model Performance ◽

Discharge Summary ◽

Learning Model ◽

Readmission Rates ◽

Clinical Notes ◽

Medical Care Costs ◽

Machine Learning Model ◽

Readmission Risk

Hospital readmission shortly after discharge is contributing to rising medical care costs. Attempts have been exerted to reduce readmission rates by predicting patients at high risk of this episode on the basis of unstructured clinical notes. Discharge summary as part of the clinical prose is effective at modeling readmission risk. However, the predictive value of notes written upon discharge offers few opportunities to reduce the chance of readmission because the target patient might have already been discharged. This paper presents the use of early clinical notes in building a machine learning model to predict readmission at 48 h immediately after a patient's admission. Extensive feature engineering, testing multiple algorithms, and algorithm tuning were performed to enhance model performance. A risk scoring framework that combines the data- and knowledge-driven feature scores in risk computation was developed. The proposed predictive model showed better prognostic capability than the machine learning model alone in terms of the ability to detect readmission. In specific, the proposed algorithm showed improvements of 11%–28% in sensitivity and 1%–3% in the area-under-the-receiver operating characteristic curve.

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

Diagnostics ◽

10.3390/diagnostics12010082 ◽

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.

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Early Prediction of Ventilator-associated Pneumonia in Critical Care Patients: a Machine Learning Model

10.21203/rs.3.rs-129953/v1 ◽

2020 ◽

Author(s):

Yingjian Liang ◽

Chengrui Zhu ◽

Cong Tian ◽

Qizhong Lin ◽

Zhiliang Li ◽

...

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Sensitivity And Specificity ◽

External Validation ◽

Model Performance ◽

Learning Model ◽

Ventilator Associated Pneumonia ◽

Data Set ◽

Machine Learning Model ◽

Mimic Iii

Abstract Background: This study was performed to develop and validate machine learning models for the early detection of ventilator-associated pneumonia (VAP) in patients 24 h before the diagnosis that enables VAP patients to receive early intervention and reduces the occurrence of complications.Patients and Methods: This study was based on the MIMIC-III dataset, which was a retrospective cohort. The random forest algorithm was applied to construct a base classifier, and the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity and specificity of the prediction model were evaluated. Meanwhile, a Clinical Pulmonary Infection Score (CPIS)-based model (threshold value≥3) using the same training and test data set was used as the control model.Results: A total of 38,515 ventilation durations occurred in 61,532 ICU admissions. VAP occurred in 212 of these durations. We incorporated 42 VAP risk factors on admission and routinely measured vital characteristics and laboratory results. Five-fold cross-validation was performed to evaluate the model performance, and the model achieved an AUC of 84.4%±1.7% on validation, 74.3%±2.5% sensitivity and 70.7.6%±1.2% specificity 24 h before the gold standard time (at least 48 h after ventilation). Our VAP machine learning model improved the AUC of the CPIS-based model by almost 25%, and the sensitivity and specificity were also improved by almost 14% and 15%, respectively.Conclusions: We developed and internally validated an automated model of VAP prediction in the MIMIC-III cohort. The VAP prediction model achieved high performance for AUC, sensitivity and specificity. and its performance was superior to that of the CPIS model. External validation and prospective interventional or outcome studies using this prediction model are envisioned as future work.

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An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging

Journal of Clinical Medicine ◽

10.3390/jcm9030658 ◽

2020 ◽

Vol 9 (3) ◽

pp. 658 ◽

Cited By ~ 1

Author(s):

Jun-Cheng Weng ◽

Tung-Yeh Lin ◽

Yuan-Hsiung Tsai ◽

Man Teng Cheok ◽

Yi-Peng Eve Chang ◽

...

Keyword(s):

Machine Learning ◽

Suicidal Ideation ◽

Learning Algorithm ◽

Area Under The Curve ◽

Learning Model ◽

Supervised Machine Learning ◽

Gradient Boosting ◽

Machine Learning Model ◽

Extreme Gradient Boosting ◽

Depressive Patients

It is estimated that at least one million people die by suicide every year, showing the importance of suicide prevention and detection. In this study, an autoencoder and machine learning model was employed to predict people with suicidal ideation based on their structural brain imaging. The subjects in our generalized q-sampling imaging (GQI) dataset consisted of three groups: 41 depressive patients with suicidal ideation (SI), 54 depressive patients without suicidal thoughts (NS), and 58 healthy controls (HC). In the GQI dataset, indices of generalized fractional anisotropy (GFA), isotropic values of the orientation distribution function (ISO), and normalized quantitative anisotropy (NQA) were separately trained in different machine learning models. A convolutional neural network (CNN)-based autoencoder model, the supervised machine learning algorithm extreme gradient boosting (XGB), and logistic regression (LR) were used to discriminate SI subjects from NS and HC subjects. After five-fold cross validation, separate data were tested to obtain the accuracy, sensitivity, specificity, and area under the curve of each result. Our results showed that the best pattern of structure across multiple brain locations can classify suicidal ideates from NS and HC with a prediction accuracy of 85%, a specificity of 100% and a sensitivity of 75%. The algorithms developed here might provide an objective tool to help identify suicidal ideation risk among depressed patients alongside clinical assessment.

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