Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

Download Full-text

Severity and Outcome Detection for the Cancer Patients with COVID-19 Using Machine Learning Models

10.21203/rs.3.rs-989924/v1 ◽

2021 ◽

Author(s):

Akbar Davoodi ◽

Shaghayegh Haghjooy Javanmard ◽

Golnaz Vaseghi ◽

Amirreza Manteghinejad

Keyword(s):

Machine Learning ◽

Intensive Care Unit ◽

Intensive Care ◽

Cancer Patients ◽

Cancer Center ◽

Support Vector ◽

Neutrophil Lymphocyte Ratio ◽

Icu Admission ◽

Reactive Protein ◽

Risk Of Death

Abstract Background:The COVID-19 pandemic challenges the healthcare system to provide enough resources to battle the pandemic without jeopardizing routine treatments. As a result, this is important that we can predict the outcomes of patients at the time of admission. This study aims to apply different machine learning (ML) models for predicting Intensive Care Unit (ICU) admission and mortality of Cancer Patients infected with COVID-19.Methods:This study's data were collected from a referral cancer center in Iran. The study included all patients with cancer and a confirmed diagnosis of COVID-19.Different ML prediction algorithms like Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbours (kNN), Random Forest (RF), and Support Vector Machine (SVM) were used. Also, we applied the SelectKBest method to find the most important features for predicting ICU admission and mortality.Results:Three hundred thirty-nine patients enrolled in the study. One hundred fifteen were admitted to the Intensive Care Unit (ICU), and 118 patients died during the hospital admission. The Area Under Curve (AUC) for predicting mortality is 0.61 for LR, 0.74 for NB, 0.61 for kNN, 0.6 for SVM, and 0.79 for RF. The AUC for predicting ICU admission is 0.61 for LR, 0.74 for NB, 0.56 for kNN, 0.55 for SVM, and 0.7 for RF.C-reactive protein (CRP), Aspartate transaminase (AST), and Neutrophil-Lymphocyte Ratio (NLR) also are the most common features in predicting ICU admission and mortality.Conclusion:Our findings show the promise of different AI methods for predicting the risk of death or ICU in cancer patients infected with COVID-19, highlighting the importance of first laboratory results and patients' symptoms.

Download Full-text

Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

European Radiology Experimental ◽

10.1186/s41747-019-0121-6 ◽

2019 ◽

Vol 3 (1) ◽

Cited By ~ 2

Author(s):

Magda Marcon ◽

Alexander Ciritsis ◽

Cristina Rossi ◽

Anton S. Becker ◽

Nicole Berger ◽

...

Keyword(s):

Machine Learning ◽

Pilot Study ◽

Texture Analysis ◽

Area Under The Curve ◽

Texture Features ◽

Breast Ultrasound ◽

Support Vector ◽

Maximum Area ◽

Svm Algorithm ◽

Automated Breast Ultrasound

Abstract Background Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification. Methods This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions. Results Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii). Conclusions TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

Download Full-text

A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study (Preprint)

10.2196/preprints.22400 ◽

2020 ◽

Author(s):

Angier Allen ◽

Samson Mataraso ◽

Anna Siefkas ◽

Hoyt Burdick ◽

Gregory Braden ◽

...

Keyword(s):

Machine Learning ◽

Health Care ◽

Intensive Care ◽

Racial Disparities ◽

Equal Opportunity ◽

Racial Bias ◽

Learning Algorithm ◽

Early Warning Score ◽

Machine Learning Algorithm ◽

Saps Ii

BACKGROUND Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups. OBJECTIVE The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups. METHODS Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care–III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). RESULTS The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively). CONCLUSIONS This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.

Download Full-text

Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature

Contrast Media & Molecular Imaging ◽

10.1155/2021/7830909 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Li Zhang ◽

Xia Zhe ◽

Min Tang ◽

Jing Zhang ◽

Jialiang Ren ◽

...

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Logistic Regression ◽

Learning Algorithm ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Support Vector ◽

Low Grade ◽

High Grade ◽

Random Forest Tree

Purpose. This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI). Methods. This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1. Results. In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases ( P < 0.05 ) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade. Conclusions. The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.

Download Full-text

Prediction of Protein–ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm

International Journal of Molecular Sciences ◽

10.3390/ijms22020939 ◽

2021 ◽

Vol 22 (2) ◽

pp. 939

Author(s):

Jiazhi Song ◽

Guixia Liu ◽

Jingqing Jiang ◽

Ping Zhang ◽

Yanchun Liang

Keyword(s):

Machine Learning ◽

Protein Function ◽

Learning Algorithm ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Atp Binding ◽

Deep Convolutional Neural Networks ◽

Binding Residues

Accurately identifying protein–ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein–ATP binding residues; however, as new machine-learning techniques are being developed, the prediction performance could be further improved. In this paper, an ensemble predictor that combines deep convolutional neural network and LightGBM with ensemble learning algorithm is proposed. Three subclassifiers have been developed, including a multi-incepResNet-based predictor, a multi-Xception-based predictor, and a LightGBM predictor. The final prediction result is the combination of outputs from three subclassifiers with optimized weight distribution. We examined the performance of our proposed predictor using two datasets: a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. Our predictor achieved area under the curve (AUC) values of 0.925 and 0.902 and Matthews Correlation Coefficient (MCC) values of 0.639 and 0.642, respectively, which are both better than other state-of-art prediction methods.

Download Full-text

A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study

JMIR Public Health and Surveillance ◽

10.2196/22400 ◽

2020 ◽

Vol 6 (4) ◽

pp. e22400

Author(s):

Angier Allen ◽

Samson Mataraso ◽

Anna Siefkas ◽

Hoyt Burdick ◽

Gregory Braden ◽

...

Keyword(s):

Machine Learning ◽

Health Care ◽

Intensive Care ◽

Racial Disparities ◽

Equal Opportunity ◽

Racial Bias ◽

Learning Algorithm ◽

Early Warning Score ◽

Machine Learning Algorithm ◽

Saps Ii

Background Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups. Objective The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups. Methods Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care–III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). Results The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively). Conclusions This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.

Download Full-text

Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units

Frontiers in Medicine ◽

10.3389/fmed.2021.676277 ◽

2021 ◽

Vol 8 ◽

Author(s):

Kumiko Tanaka ◽

Taka-aki Nakada ◽

Nozomi Takahashi ◽

Takahiro Dozono ◽

Yuichiro Yoshimura ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Pleural Effusion ◽

Diagnostic Accuracy ◽

Intensive Care Units ◽

Area Under The Curve ◽

Chest Radiographs ◽

Data Set ◽

Portable Chest ◽

Icu Physicians

Purpose: Portable chest radiographs are diagnostically indispensable in intensive care units (ICU). This study aimed to determine if the proposed machine learning technique increased in accuracy as the number of radiograph readings increased and if it was accurate in a clinical setting.Methods: Two independent data sets of portable chest radiographs (n = 380, a single Japanese hospital; n = 1,720, The National Institution of Health [NIH] ChestX-ray8 dataset) were analyzed. Each data set was divided training data and study data. Images were classified as atelectasis, pleural effusion, pneumonia, or no emergency. DenseNet-121, as a pre-trained deep convolutional neural network was used and ensemble learning was performed on the best-performing algorithms. Diagnostic accuracy and processing time were compared to those of ICU physicians.Results: In the single Japanese hospital data, the area under the curve (AUC) of diagnostic accuracy was 0.768. The area under the curve (AUC) of diagnostic accuracy significantly improved as the number of radiograph readings increased from 25 to 100% in the NIH data set. The AUC was higher than 0.9 for all categories toward the end of training with a large sample size. The time to complete 53 radiographs by machine learning was 70 times faster than the time taken by ICU physicians (9.66 s vs. 12 min). The diagnostic accuracy was higher by machine learning than by ICU physicians in most categories (atelectasis, AUC 0.744 vs. 0.555, P < 0.05; pleural effusion, 0.856 vs. 0.706, P < 0.01; pneumonia, 0.720 vs. 0.744, P = 0.88; no emergency, 0.751 vs. 0.698, P = 0.47).Conclusions: We developed an automatic detection system for portable chest radiographs in ICU setting; its performance was superior and quite faster than ICU physicians.

Download Full-text

Predicting Prolonged Length of ICU Stay through Machine Learning

Diagnostics ◽

10.3390/diagnostics11122242 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2242

Author(s):

Jingyi Wu ◽

Yu Lin ◽

Pengfei Li ◽

Yonghua Hu ◽

Luxia Zhang ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care ◽

External Validation ◽

Prediction Performance ◽

Brier Score ◽

Support Vector ◽

Internal Validation ◽

Prolonged Length ◽

Saps Ii ◽

Overall Performance

This study aimed to construct machine learning (ML) models for predicting prolonged length of stay (pLOS) in intensive care units (ICU) among general ICU patients. A multicenter database called eICU (Collaborative Research Database) was used for model derivation and internal validation, and the Medical Information Mart for Intensive Care (MIMIC) III database was used for external validation. We used four different ML methods (random forest, support vector machine, deep learning, and gradient boosting decision tree (GBDT)) to develop prediction models. The prediction performance of the four models were compared with the customized simplified acute physiology score (SAPS) II. The area under the receiver operation characteristic curve (AUROC), area under the precision-recall curve (AUPRC), estimated calibration index (ECI), and Brier score were used to measure performance. In internal validation, the GBDT model achieved the best overall performance (Brier score, 0.164), discrimination (AUROC, 0.742; AUPRC, 0.537), and calibration (ECI, 8.224). In external validation, the GBDT model also achieved the best overall performance (Brier score, 0.166), discrimination (AUROC, 0.747; AUPRC, 0.536), and calibration (ECI, 8.294). External validation showed that the calibration curve of the GBDT model was an optimal fit, and four ML models outperformed the customized SAPS II model. The GBDT-based pLOS-ICU prediction model had the best prediction performance among the five models on both internal and external datasets. Furthermore, it has the potential to assist ICU physicians to identify patients with pLOS-ICU risk and provide appropriate clinical interventions to improve patient outcomes.

Download Full-text

Feature engineering combined with 1-D convolutional neural network for improved mortality prediction

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2020-0056 ◽

2020 ◽

Vol 16 (4) ◽

Author(s):

Rohit Verma ◽

Saumil Maheshwari ◽

Anupam Shukla

Keyword(s):

Neural Network ◽

Machine Learning ◽

Intensive Care ◽

Convolutional Neural Network ◽

Real Time ◽

Intensive Care Units ◽

Mortality Prediction ◽

Support Vector ◽

Feature Engineering ◽

Light Gradient

AbstractObjectivesThe appropriate care for patients admitted in Intensive care units (ICUs) is becoming increasingly prominent, thus recognizing the use of machine learning models. The real-time prediction of mortality of patients admitted in ICU has the potential for providing the physician with the interpretable results. With the growing crisis including soaring cost, unsafe care, misdirected care, fragmented care, chronic diseases and evolution of epidemic diseases in the domain of healthcare demands the application of automated and real-time data processing for assuring the improved quality of life. The intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of Electronic Health Record (EHR). This data allows for the development of a prediction tool with perfect knowledge backing.MethodWe aimed to build the mortality prediction model on 2012 Physionet Challenge mortality prediction database of 4,000 patients admitted in ICU. The challenges in the dataset, such as high dimensionality, imbalanced distribution and missing values, were tackled with analytical methods and tools via feature engineering and new variable construction. The objective of the research is to utilize the relations among the clinical variables and construct new variables which would establish the effectiveness of 1-Dimensional Convolutional Neural Network (1-D CNN) with constructed features.ResultsIts performance with the traditional machine learning algorithms like XGBoost classifier, Light Gradient Boosting Machine (LGBM) classifier, Support Vector Machine (SVM), Decision Tree (DT), K-Neighbours Classifier (K-NN), and Random Forest Classifier (RF) and recurrent models like Long Short-Term Memory (LSTM) and LSTM-attention is compared for Area Under Curve (AUC). The investigation reveals the best AUC of 0.848 using 1-D CNN model.ConclusionThe relationship between the various features were recognized. Also, constructed new features using existing ones. Multiple models were tested and compared on different metrics.

Download Full-text

PSO-ELM with Modified Acceleration Coefficients for Classifying the Active Compound

Current Bioinformatics ◽

10.2174/1574893616666210515141605 ◽

2021 ◽

Vol 16 ◽

Author(s):

Dian Eka Ratnawati ◽

Marjono Marjono ◽

Nashi Widodo

Keyword(s):

Machine Learning ◽

Active Compound ◽

Learning Algorithm ◽

Area Under The Curve ◽

Support Vector ◽

Active Compounds ◽

Acceleration Coefficient ◽

Learning Machine ◽

Hidden Neurons ◽

Mlp Classifier

Background: The classification of active compounds based on their function using machine learning is essential for predicting the function of new active compounds quickly. These classification results are beneficial and accelerate the work of laboratory assistants in identifying the function of active compounds. In this study, an active compound is represented by the simplified molecular-input line-entry system (SMILES) code. Objective: This paper proposes a modified acceleration coefficient to improve the PSO-ELM performance for predicting the function of the SMILES code. Method: The research uses a machine-learning algorithm that is a combination of the Particle Swarm Optimization and Extreme Learning Machine (PSO-ELM). ELM is used to classify the SMILES code, while PSO is used to optimize ELM parameters, i.e., weight, bias, and the number of hidden neurons. The important parameters that significantly influence the PSO performance are acceleration coefficients. The acceleration coefficients, that is, modified sigmoid-based acceleration coefficient (SBAC), are introduced and compared with seven other acceleration coefficients. Results: The experimental results show that the sensitivity, specificity, accuracy, and the area under the curve (AUC) of the proposed acceleration coefficients outperforms all other acceleration coefficients. The increased accuracy of the proposed paper can reach up to 2.64%, 5.84%, 7.93%, 8.44%, and 16.29% for Support Vector Machine (SVM), decision tree, AdaBoost, MLP Classifier, and Gaussian Naïve Bayes algorithms, respectively. Conclusion: The acceleration coefficients affect the prediction accuracy of the SMILES code classification. The proposed acceleration coefficients improve the performance of the PSO-ELM for predicting the function of the SMILES code.

Download Full-text