A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset

Ghorbani, Ramin; Ghousi, Rouzbeh; Makui, Ahmad; Atashi, Amir

doi:10.1109/access.2020.3013320

Cited by 37 publications

(24 citation statements)

References 60 publications

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“…The authors developed an SVM and SMOTE hybrid strategy (SVM-SMOTE) that included several methods, including a Genetic Algorithm (GA) for feature selection (FS) and Stacking and Boosting (EM) for prediction. SVM-SMOTE was used to tackle imbalanced data problems [65]. Using clustering, weighted scoring, SVM, and EM, Ksieniewicz et al suggested a hybrid method for managing severely imbalanced data categorization in geometric space [66].…”

Section: Ghorbani Et Al Proposed a New Hybrid Model Based On Amentioning

confidence: 99%

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Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multiclass data.

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Section: Ghorbani Et Al Proposed a New Hybrid Model Based On Amentioning

confidence: 99%

“…BPFs [180], MPRM [181] Speech Recognition AL [168] Social Media PCA + DA + CNN [145] Imaging GSVM-BA [182] Spam Detection SMOTE-tBPSO-SVM [183] Malware Detection Boosting + Sampling [184], SMOTE + RF [185] Medical GA + SVM-SMOTE [65] Mortality Prediction Ensemble + SMOTE [71] Sentiment Analysis…”

Section: A Related Study On Himc Data Frameworkmentioning

confidence: 99%

Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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“…Additionally, machine learning is a potential method in improving various functions in the healthcare sector. The application also enhances the diagnostic accuracy for further exploration and remedial procedures and subsequently mitigates hospital readmission, increasing service expenses (59)(60)(61).…”

Section: Prioritisation Predictive Systemmentioning

confidence: 99%

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Zamzam

Al-Ani

Wahab

et al. 2021

Front. Public Health

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The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.

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“…Nowadays, massive volumes of data recorded in electronic health records (EHRs) also supported researchers to design models and algorithms for in-hospital mortality prediction which aim at improving the predicting performance and facilitating the clinical decision making. Outperformance of mortality prediction methods based on machine learning models have been shown by many works [6][7][8][9][10][11][12][13]. Some of them show better prediction performance of machine learning models than traditional scoring systems [7,8] and some of them develop various models and machine learning algorithms for mortality prediction [6,[9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Outperformance of mortality prediction methods based on machine learning models have been shown by many works [6][7][8][9][10][11][12][13]. Some of them show better prediction performance of machine learning models than traditional scoring systems [7,8] and some of them develop various models and machine learning algorithms for mortality prediction [6,[9][10][11][12][13]. Further more, the deep learning models achieve particularly satisfactory performance in ICU mortality prediction tasks due to their strong ability of capturing non-linear patterns hidden in data [14].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Post-Discharge Mortality Prediction Method Considering Diagnosis Information for ICU Patients

Liu

Guo

Cui

2021

Preprint

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Background and Objective: Mortality prediction is widely used to stratify patients into different risk categories and to provide prognosis evaluation. Nowadays, scoring systems, which predict mortality with some scores reflecting the severity of diseases and physiological states of patients in ICU, have been widely applied for in-hospital mortality prediction. Many research works which focus on designing better machine learning models and algorithms for mortality prediction also have achieved great performance. However, it is not enough to make post-discharge prognosis of mortality only with the aid of better models and algorithms while richer patient related information can make big differences. In this study, we propose a deep learning method considering patient diagnosis information for post-discharge prognosis of mortality. This method can help to significantly improve the performance of prediction. Further more, we propose a method of calculating disease Shapley values to evaluate the mortality risk brought by disease factors. Methods: Deep learning models including long short-term memory (LSTM) and temporal convolutional network (TCN) are trained with patient physiological time series data and diagnosis information of different prevalence to predict post-discharge mortality risk of different time windows. Disease Shapley values to evaluate the mortality risk brought by disease factors are the weighted average of marginal contributions of diseases to patient mortality. Experiments of several post-discharge mortality prediction tasks of different time windows are conducted on the large freely accessible MIMIC-III dataset. To provide more sufficient comparison, the diagnosis information is also introduced for traditional machine learning models. Results: In our experiment, LSTM achieves highest AUROC and the improvements of which are 8.67%, 9.68%, 13.33%, 12.32% and 12.25% with the help of diagnosis information for five post-discharge mortality prediction tasks of different time window. Several patient examples are shown to present the mortality risk brought by disease factors, of which the analysis results are in line with clinical experiences. Conclusions: In general, our proposed method can improve performance of ICU patient post-discharge mortality prediction and help to evaluate how much do different kinds of diseases which a patient suffers from increase his mortality, thus providing support for clinical decisions.

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A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset

Cited by 37 publications

References 60 publications

Survey on Highly Imbalanced Multi-class Data

Survey on Highly Imbalanced Multi-class Data

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

A Deep Learning Post-Discharge Mortality Prediction Method Considering Diagnosis Information for ICU Patients

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