Heart attack mortality prediction: an application of machine learning methods

More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.

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Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

Journal of Clinical Medicine ◽

10.3390/jcm8111906 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1906 ◽

Cited By ~ 8

Author(s):

Jau-Woei Perng ◽

I-Hsi Kao ◽

Chia-Te Kung ◽

Shih-Chiang Hung ◽

Yi-Horng Lai ◽

...

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Deep Learning ◽

Mortality Prediction ◽

Screening Tools ◽

Learning Methods ◽

Suspected Infection ◽

Machine Learning Methods ◽

Critical Patients ◽

Sepsis Screening

In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.

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Machine Learning Methods for Mortality Prediction of Polytraumatized Patients in Intensive Care Units – Dealing with Imbalanced and High-Dimensional Data

Intelligent Data Engineering and Automated Learning – IDEAL 2014 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-10840-7_38 ◽

2014 ◽

pp. 309-317 ◽

Cited By ~ 3

Author(s):

María N. Moreno García ◽

Javier González Robledo ◽

Félix Martín González ◽

Fernando Sánchez Hernández ◽

Mercedes Sánchez Barba

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Intensive Care Units ◽

High Dimensional Data ◽

Mortality Prediction ◽

High Dimensional ◽

Learning Methods ◽

Machine Learning Methods

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Predicting Risk of Mortality in Pediatric ICU Based on Ensemble Step-Wise Feature Selection

Health Data Science ◽

10.34133/2021/9365125 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Shenda Hong ◽

Xinlin Hou ◽

Jin Jing ◽

Wendong Ge ◽

Luxia Zhang

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Mortality Prediction ◽

Ranking And Selection ◽

Data Driven ◽

Feature Ranking ◽

Learning Methods ◽

Machine Learning Methods ◽

Pediatric Icu ◽

Logistic Regression Classifier

Background. Prediction of mortality risk in intensive care units (ICU) is an important task. Data-driven methods such as scoring systems, machine learning methods, and deep learning methods have been investigated for a long time. However, few data-driven methods are specially developed for pediatric ICU. In this paper, we aim to amend this gap—build a simple yet effective linear machine learning model from a number of hand-crafted features for mortality prediction in pediatric ICU. Methods. We use a recently released publicly available pediatric ICU dataset named pediatric intensive care (PIC) from Children’s Hospital of Zhejiang University School of Medicine in China. Unlike previous sophisticated machine learning methods, we want our method to keep simple that can be easily understood by clinical staffs. Thus, an ensemble step-wise feature ranking and selection method is proposed to select a small subset of effective features from the entire feature set. A logistic regression classifier is built upon selected features for mortality prediction. Results. The final predictive linear model with 11 features achieves a 0.7531 ROC-AUC score on the hold-out test set, which is comparable with a logistic regression classifier using all 397 features (0.7610 ROC-AUC score) and is higher than the existing well known pediatric mortality risk scorer PRISM III (0.6895 ROC-AUC score). Conclusions. Our method improves feature ranking and selection by utilizing an ensemble method while keeping a simple linear form of the predictive model and therefore achieves better generalizability and performance on mortality prediction in pediatric ICU.

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