Balanced training of a hybrid ensemble method for imbalanced datasets: a case of emergency department readmission prediction

2017 ◽  
Vol 32 (10) ◽  
pp. 5735-5744 ◽  
Author(s):  
Arkaitz Artetxe ◽  
Manuel Graña ◽  
Andoni Beristain ◽  
Sebastián Ríos
Keyword(s):  
Emergency Department ◽  
Ensemble Method ◽  
Imbalanced Datasets

scholarly journals A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yong Zhang ◽  
Dapeng Wang
Keyword(s):  
Imbalanced Data ◽  
Ensemble Method ◽  
Support Vector ◽  
Data Sets ◽  
Imbalanced Learning ◽  
Imbalanced Datasets ◽  
Learning Methods ◽  
Training Samples ◽  
Imbalanced Data Classification ◽  
Area Under Roc Curve

In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers. This paper proposes a cost-sensitive ensemble method based on cost-sensitive support vector machine (SVM), and query-by-committee (QBC) to solve imbalanced data classification. The proposed method first divides the majority-class dataset into several subdatasets according to the proportion of imbalanced samples and trains subclassifiers using AdaBoost method. Then, the proposed method generates candidate training samples by QBC active learning method and uses cost-sensitive SVM to learn the training samples. By using 5 class-imbalanced datasets, experimental results show that the proposed method has higher area under ROC curve (AUC), F-measure, and G-mean than many existing class-imbalanced learning methods.

Performance Analysis of Two-Stage Iterative Ensemble Method over Random Oversampling Methods on Multiclass Imbalanced Datasets

2021 ◽  
Author(s):  
S. Sridhar ◽  
A. Kalaivani
Keyword(s):  
Learning Process ◽  
Imbalanced Data ◽  
Main Idea ◽  
Ensemble Method ◽  
Supervised Machine Learning ◽  
Two Stage ◽  
Imbalanced Datasets ◽  
Imbalance Problem ◽  
Real World Applications ◽  
Made In

Data imbalance occurring among multiclass datasets is very common in real-world applications. Existing studies reveal that various attempts were made in the past to overcome this multiclass imbalance problem, which is a severe issue related to the typical supervised machine learning methods such as classification and regression. But, still there exists a need to handle the imbalance problem efficiently as the datasets include both safe and unsafe minority samples. Most of the widely used oversampling techniques like SMOTE and its variants face challenges in replicating or generating the new data instances for balancing them across multiple classes, particularly when the imbalance is high and the number of rare samples count is too minimal thus leading the classifier to misclassify the data instances. To lessen this problem, we proposed a new data balancing method namely a two-stage iterative ensemble method to tackle the imbalance in multiclass environment. The proposed approach focuses on the rare minority sample’s influence on learning from imbalanced datasets and the main idea of the proposed approach is to balance the data without any change in class distribution before it gets trained by the learner such that it improves the learner’s learning process. Also, the proposed approach is compared against two widely used oversampling techniques and the results reveals that the proposed approach shows a much significant improvement in the learning process among the multiclass imbalanced data.

Analysis of Deliberate Self-Wrist-Cutting Episodes Presenting to the Emergency Department

Crisis ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 155-160 ◽  
Author(s):  
Jin Kim ◽  
Han Joon Kim ◽  
Soo Hyun Kim ◽  
Sang Hoon Oh ◽  
Kyu Nam Park
Keyword(s):  
Emergency Department ◽  
Medical Records ◽  
Attempted Suicide ◽  
Suicide Attempts ◽  
Psychiatric Interview ◽  
Clinical Variables ◽  
Completed Suicide ◽  
Number Of Patients ◽  
Group A ◽  
Efficacy Of Treatment

Abstract. Background: Previous suicide attempts increase the risk of a completed suicide. However, a large proportion of patients with deliberate self-wrist cutting (DSWC) are often discharged without undergoing a psychiatric interview. Aims: The aims of this study were to investigate the differences in the characteristics and outcomes of patients with DSWC and those with deliberate self-poisoning (DSP) episodes. The results of this study may be used to improve the efficacy of treatment for DSWC patients. Method: We retrospectively reviewed the medical records of 598 patients with DSWC and DSP who were treated at the emergency department of Seoul Saint Mary's Hospital between 2008 and 2013. We assessed sociodemographic information, clinical variables, the reasons for the suicide attempts, and the severity of the suicide attempts. Results: A total of 141 (23.6%) patients were included in the DSWC group, and 457 (76.4%) were included in the DSP group. A significantly greater number of patients in the DSWC group had previously attempted suicide (p = .014). A total of 63 patients (44.7%) in the DSWC group and 409 patients (89.5%) in the DSP group underwent psychiatric interviews. Conclusion: More DSWC patients had previously attempted suicide, but fewer of them underwent psychiatric interviews compared with the DSP patients.

Emergency Department Admission and Mortality Rate for Suicidal Behavior

Crisis ◽  
2014 ◽  
Vol 35 (6) ◽  
pp. 406-414 ◽  
Author(s):  
Raimondo Maria Pavarin ◽  
Angelo Fioritti ◽  
Francesca Fontana ◽  
Silvia Marani ◽  
Alessandra Paparelli ◽  
...  
Keyword(s):  
Emergency Department ◽  
Metropolitan Area ◽  
Suicidal Behavior ◽  
Mortality Risk ◽  
Illicit Drugs ◽  
Attempted Suicide ◽  
Cox Model ◽  
Emergency Department Admission ◽  
Completed Suicide

Background: The international literature reports that for every completed suicide there are between 8 and 22 visits to an Emergency Department (ED) for attempted suicide/suicidal behavior. Aims: To describe the characteristics of admission to emergency departments (EDs) for suicide-related presenting complaints in the metropolitan area of Bologna; to estimate the risk for all-cause mortality and for suicide; to identify the profiles of subjects most at risk. Method: Follow-up of patients admitted to the EDs of the metropolitan area of Bologna between January 2004 and December 2010 for attempted suicide. A Cox model was used to evaluate the association between sociodemographic variables and the general mortality risk. Results: We identified 505 cases of attempted suicide, which were more frequent for female subjects, over the weekend, and at night (8:00 p.m./8:00 a.m.). The most used suicide methods were psychotropic drugs, sharp or blunt objects, and jumping from high places. In this cohort, 3.6% of subjects completed suicide (4.5% of males vs. 2.9% of females), 2.3% within 1 year of the start of follow-up. The most common causes of death were drug use and hanging. In the multivariate analysis, those who used illicit drugs 24 hr prior to admission to the ED (hazard ratio [HR] = 3.46, 95% CI = 1.23–9.73) and patients who refused the treatment (HR = 6.74, 95% CI = 1.86–24.40) showed an increased mortality risk for suicide. Conclusion: Deliberate self-harm patients presenting to the ED who refuse treatment represent a specific target group for setting up dedicated prevention schemes.

Field Investigations of Emergency Department Syndromic Surveillance Signals - New York City

2004 ◽  
Author(s):  
Linda Steiner-Sichel ◽  
L. Greenko ◽  
R. Heffernan ◽  
M. Layton ◽  
D. Weiss
Keyword(s):  
Emergency Department ◽  
New York ◽  
New York City ◽  
York City ◽  
Syndromic Surveillance ◽  
Field Investigations
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