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Critical Care (London, England) May 2024Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of... (Review)
Review
BACKGROUND
Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering. Specifically, the role of feature selection and extraction in enhancing model accuracy has been underexplored.
OBJECTIVES
This scoping review aims to fulfill two primary objectives: To identify pivotal features for predicting sepsis across a variety of ML models, providing valuable insights for future model development, and To assess model efficacy through performance metrics including AUROC, sensitivity, and specificity.
RESULTS
The analysis included 29 studies across diverse clinical settings such as Intensive Care Units (ICU), Emergency Departments, and others, encompassing 1,147,202 patients. The review highlighted the diversity in prediction strategies and timeframes. It was found that feature extraction techniques notably outperformed others in terms of sensitivity and AUROC values, thus indicating their critical role in improving sepsis prediction models.
CONCLUSION
Key dynamic indicators, including vital signs and critical laboratory values, are instrumental in the early detection of sepsis. Applying feature selection methods significantly boosts model precision, with models like Random Forest and XG Boost showing promising results. Furthermore, Deep Learning models (DL) reveal unique insights, spotlighting the pivotal role of feature engineering in sepsis prediction, which could greatly benefit clinical practice.
Topics: Humans; Sepsis; Machine Learning
PubMed: 38802973
DOI: 10.1186/s13054-024-04948-6 -
Scientific Reports Mar 2024Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it...
Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images.
Topics: Image Processing, Computer-Assisted; Machine Learning; Neural Networks, Computer; Supervised Machine Learning; Positron-Emission Tomography
PubMed: 38480847
DOI: 10.1038/s41598-024-56706-x -
International Journal of Medical... Aug 2023Revision hip arthroplasty has a less favorable outcome than primary total hip arthroplasty and an understanding of the timing of total hip arthroplasty failure may be...
AIM
Revision hip arthroplasty has a less favorable outcome than primary total hip arthroplasty and an understanding of the timing of total hip arthroplasty failure may be helpful. The aim of this study is to develop a combined deep learning (DL) and machine learning (ML) approach to automatically detect hip prosthetic failure from conventional plain radiographs.
METHODS
Two cohorts of patients (of 280 and 352 patients) were included in the study, for model development and validation, respectively. The analysis was based on one antero-posterior and one lateral radiographic view obtained from each patient during routine post-surgery follow-up. After pre-processing, three images were obtained: the original image, the acetabulum image and the stem image. These images were analyzed through convolutional neural networks aiming to predict prosthesis failure. Deep features of the three images were extracted for each model and two feature-based pipelines were developed: one utilizing only the features of the original image (original image pipeline) and the other concatenating the features of the three images (3-image pipeline). The obtained features were either used directly or reduced through principal component analysis. Both support vector machine (SVM) and random forest (RF) classifiers were considered for each pipeline.
RESULTS
The SVM applied to the 3-image pipeline provided the best performance, with an accuracy of 0.958 ± 0.006 in the internal validation and an F1-score of 0.874 in the external validation set. The explainability analysis, besides identifying the features of the complete original images as the major contributor, highlighted the role of the acetabulum and stem images on the prediction.
CONCLUSIONS
This study demonstrated the potentialities of the developed DL-ML procedure based on plain radiographs in the detection of the failure of the hip prosthesis.
Topics: Humans; Arthroplasty, Replacement, Hip; Deep Learning; Hip Prosthesis; Prosthesis Failure; Machine Learning
PubMed: 37220702
DOI: 10.1016/j.ijmedinf.2023.105095 -
Marine Pollution Bulletin Mar 2024The recurrent brown tide phenomenon, attributed to Aureococcus anophagefferens (A. anophagefferens), constitutes a significant threat to the Qinhuangdao sea area in...
The recurrent brown tide phenomenon, attributed to Aureococcus anophagefferens (A. anophagefferens), constitutes a significant threat to the Qinhuangdao sea area in China, leading to pronounced ecological degradation and substantial economic losses. This study utilized machine learning and deep learning techniques to predict A. anophagefferens population density, aiming to elucidate the occurrence mechanism and influencing factors of brown tide. Specifically, Random Forest (RF) algorithm was utilized to impute missing water quality data, facilitating its direct application in subsequent algal population prediction models. The results revealed that all four models-RF, Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN)-exhibited high accuracy in predicting A. anophagefferens population densities, with R values exceeding 0.75. RF, in particular, showed exceptional accuracy and reliability, with an R value surpassing 0.8. Additionally, the study ascertained five critical factors influencing A. anophagefferens population density: ammonia nitrogen, pH, total nitrogen, temperature, and silicate.
Topics: Reproducibility of Results; Deep Learning; Stramenopiles; Machine Learning; Nitrogen
PubMed: 38364640
DOI: 10.1016/j.marpolbul.2024.116148 -
European Heart Journal. Cardiovascular... Mar 2024
Topics: Humans; Deep Learning; Machine Learning; Mammography; Coronary Artery Disease
PubMed: 38262145
DOI: 10.1093/ehjci/jeae019 -
Water Science and Technology : a... Nov 2023Machine learning (ML), a branch of artificial intelligence (AI), has been increasingly used in environmental engineering due to the ability to analyze complex nonlinear... (Review)
Review
Machine learning (ML), a branch of artificial intelligence (AI), has been increasingly used in environmental engineering due to the ability to analyze complex nonlinear problems (such as ones connected with water quality management) through a data-driven approach. This study provides an overview of different ML algorithms applied for monitoring and predicting river water quality. Different parameters could be monitored or predicted, such as dissolved oxygen (DO), biological and chemical oxygen demand (BOD and COD), turbidity levels, the concentration of different ions (such as Mg and Ca), heavy metal or other pollutant's concentration, pH, temperature, and many more. Although many algorithms have been investigated for the prediction of river water quality, there are several which are most commonly used in engineering practice. These models mostly include so-called supervised learning algorithms, such as artificial neural network (ANN), support vector machine (SVM), random forest (RF), decision tree (DT), and deep learning (DL). To further enhance prediction power, novel hybrid algorithms, could be used. However, the quality of prediction is not only dependent on the applied algorithm but also on the availability of previously mentioned water quality parameters, their selection, and the combination of input data used to train the ML model.
Topics: Water Quality; Environmental Monitoring; Rivers; Artificial Intelligence; Algorithms; Machine Learning; Support Vector Machine
PubMed: 37966184
DOI: 10.2166/wst.2023.331 -
Journal of Neural Engineering Sep 2023(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26...
(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.
Topics: Humans; Schizophrenia; Deep Learning; Brain; Machine Learning; Magnetic Resonance Imaging
PubMed: 37673060
DOI: 10.1088/1741-2552/acf734 -
Brain and Behavior Sep 2023Decision-making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in...
BACKGROUND
Decision-making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers' personality traits (i.e., risk-taking or risk-averse) in recent years. Although there are findings of signal decision-making and brain activity, the implementation of an intelligent brain-based technique to predict risk-averse and risk-taking managers is still in doubt.
METHODS
This study proposes an electroencephalogram (EEG)-based intelligent system to distinguish risk-taking managers from risk-averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time-frequency domain analysis method, was used on resting-state EEG data to extract statistical features. Then, a two-step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features.
RESULTS
Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1-measure, indicating that machine learning (ML) models can distinguish between risk-taking and risk-averse managers using the features extracted from the alpha frequency band in 10 s analysis window size.
CONCLUSIONS
The findings of this study demonstrate the potential of using intelligent (ML-based) systems in distinguish between risk-taking and risk-averse managers using biological signals.
Topics: Humans; Feasibility Studies; Electroencephalography; Wavelet Analysis; Brain; Algorithms; Machine Learning; Support Vector Machine
PubMed: 37366037
DOI: 10.1002/brb3.3139 -
Current Neuropharmacology 2024Brain disorders are one of the major global mortality issues, and their early detection is crucial for healing. Machine learning, specifically deep learning, is a... (Review)
Review
BACKGROUND AND OBJECTIVE
Brain disorders are one of the major global mortality issues, and their early detection is crucial for healing. Machine learning, specifically deep learning, is a technology that is increasingly being used to detect and diagnose brain disorders. Our objective is to provide a quantitative bibliometric analysis of the field to inform researchers about trends that can inform their Research directions in the future.
METHODS
We carried out a bibliometric analysis to create an overview of brain disorder detection and diagnosis using machine learning and deep learning. Our bibliometric analysis includes 1550 articles gathered from the Scopus database on automated brain disorder detection and diagnosis using machine learning and deep learning published from 2015 to May 2023. A thorough bibliometric análisis is carried out with the help of Biblioshiny and the VOSviewer platform. Citation analysis and various measures of collaboration are analyzed in the study.
RESULTS
According to a study, maximum research is reported in 2022, with a consistent rise from preceding years. The majority of the authors referenced have concentrated on multiclass classification and innovative convolutional neural network models that are effective in this field. A keyword analysis revealed that among the several brain disorder types, Alzheimer's, autism, and Parkinson's disease had received the greatest attention. In terms of both authors and institutes, the USA, China, and India are among the most collaborating countries. We built a future research agenda based on our findings to help progress research on machine learning and deep learning for brain disorder detection and diagnosis.
CONCLUSION
In summary, our quantitative bibliometric analysis provides useful insights about trends in the field and points them to potential directions in applying machine learning and deep learning for brain disorder detection and diagnosis.
.Topics: Humans; Bibliometrics; Deep Learning; Machine Learning; Brain Diseases
PubMed: 38847379
DOI: 10.2174/1570159X22999240531160344 -
Analytical Sciences : the International... Dec 2023
Topics: Machine Learning
PubMed: 37996767
DOI: 10.1007/s44211-023-00447-w