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Разнообразие сетчатки между множественным склерозом и расстройством спектрального спектра нейромиелита: перекрестное исследование с диагностической моделью.

ОКРУЖАЮЩАЯ СРЕДА И ЦЕЛИ: Изменения сетчатки в множественных склерозах (MS) и оптовом расстройстве нейромиелита (NMOSD) остаются неясными, особенно в том, что касается конкретных моделей и масштабов микроваскулярных изменений. Цель этого исследования заключалась в сопоставлении этих изменений и разработке моделей логистической регрессии и машинного обучения с использованием комбинированных микроваскуловых и структурных параметров сетчатки для дифференциации заболеваний. МЕТОДЫ: Пациенты с MS или NMOSD в клинически стабильных фазах прошли стабильную оптическо-согласованную томографию (OCT) и анг

Совпадение в аннотации оригинала
BACKGROUND AND OBJECTIVES: Retinal alterations in multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) remain unclear, … sion model with microvascular metrics achieved an AUC of 0.900. Among machine learning classifiers, support vector machine performed best (AUC 0.912, accuracy 84.5%). DISCUSSION: Distinct retinal microvascular patterns differentiate NMOSD from MS and correlate with disability severity, especially considering ON history. OCTA-based models provide accurate, non-invasive …

MRI radiomics combined with machine learning for diagnosing mild cognitive impairment: a focus on the cerebellar gray and white matter.

OBJECTIVE: Mild Cognitive Impairment (MCI) is a recognized precursor to Alzheimer's Disease (AD), presenting a significant risk of progressi … odels, including Random Forest (RF), Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), K Nearest Neighbors (KNN), Extra Trees, Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). Each model was optimized for penalty parameters through 5-fold cross-validation to construct radiomic models. The DeLong tes …

Comparison of traditional regression modeling vs. AI modeling for the prediction of dental caries: a secondary data analysis.

INTRODUCTION: There are substantial gaps in our understanding of dental caries in primary and permanent dentition and various predictors usi … ML algorithm with accuracy, sensitivity. and Kappa as 81%, 84%, and 61%, respectively, followed by Support Vector Machine and Lasso Regression algorithms, both with 84% specificity. The most important variables for prediction found were age and visible plaque. CONCLUSIONS: The machine learning model outperformed the traditional statistical model in the prediction of c …

Machine Learning for Clinical Decision Support of Acute Streptococcal Pharyngitis: A Pilot Study.

BACKGROUND: Group A Streptococcus (GAS) is the predominant bacterial pathogen of pharyngitis in children. However, distinguishing GAS from v … P) (n=36) and viral pharyngitis (n=18). All patients underwent a McIsaac score evaluation. A linear support vector machine algorithm was used for classification. RESULTS: The machine learning algorithm resulted in a positive predictive value of 80.6 % (27 of 36) for GAS-P infection. The false discovery rates for GAS-P infection were 19.4 % (7 of 36). CONCLUSIONS: Appl …

Postoperative delirium prediction after cardiac surgery using machine learning models.

OBJECTIVE: Postoperative delirium (POD) is a common postoperative complication in elderly patients, especially those undergoing cardiac surg … cross-validation for model training and testing. Five machine learning models (random forest (RF), support vector machine (SVM), radial based kernel neural network (RBFNN), K-nearest neighbour (KNN), and Kernel ridge regression (KRR)) were compared using area under the receiver operating characteristic curve (AUC-ROC), accuracy (ACC), sensitivity (SN), specificity (SPE …

Early antidepressant treatment response prediction in major depression using clinical and TPH2 DNA methylation features based on machine learning approaches.

OBJECTIVE: To identify DNA methylation and clinical features, and to construct machine learning classifiers to assign the patients with majo … features. Five classification algorithms (logistic regression, classification and regression trees, support vector machine, logitboost and random forests) were used to establish the models. Performance metrics (AUC, F-Measure, G-Mean, accuracy, sensitivity, specificity, positive predictive value and negative predictive value) were computed with 5-fold-cross-validation. …

Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review.

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two…

Aberrant white matter microstructure evaluation by automated fiber quantification in typhoon-related post-traumatic stress disorder.

Super typhoons can lead to post-traumatic stress disorder (PTSD), which can adversely affect a person's mental health after a disaster. Neur … s' DTI and compared diffusion metrics among the three groups. To evaluate diagnostic value, we used support vector machine (SVM) and a random forest (RF) classifier to build a machine learning model. White matter fiber segmentation between the three groups was found to be statistically significant for the fractional anisotropy (FA) value of the right anterior thalamic r …

Multi-Omics Analysis and Machine Learning Prediction Model for Pregnancy Outcomes After Intracytoplasmic Sperm Injection-in vitro Fertilization.

BACKGROUND: To explore the methylation profiles in cumulus cells (CCs) of women undergoing intracytoplasmic sperm injection-in vitro fertilization (ICSI-IVF) and establish a prediction model of pregnancy outcomes using machine learning approaches. METHODS: Methylation data were retrieved from the Gene Expression Omnibus (GEO) database, and differentially methylated genes (DMGs) were subjected to gene set analyses. Support vector machine (SVM), random forest (RF), and logistic regression (LR) were used to establish the prediction model, and microarray data from GEO was analyzed to identify dif…

Revealing Unknown Controlled Substances and New Psychoactive Substances Using High-Resolution LC-MS-MS Machine Learning Models and the Hybrid Similarity Search Algorithm.

High-resolution liquid chromatography-tandem mass spectrometry (LC--MS-MS)-based machine learning models are constructed to address the analytical challenge of identifying unknown controlled substances and new psychoactive substances (NPSs). Using a training set composed of 770 LC-MS-MS barcode spectra (with binary entries 0 or 1) obtained generally by high-resolution mass spectrometers, three classification machine learning models were generated and evaluated. The three models are artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbor (k-NN) models. In these mode…

Exploratory analysis of machine learning approaches for surveillance of Zika-associated birth defects.

In 2016, Centers for Disease Control and Prevention (CDC) established surveillance of pregnant women with Zika virus infection and their inf … ble method for predicting case status. Ensemble models (using machine learning algorithms including support vector machines, logistic regression, random forests, k-nearest neighbors, gradient boosted trees, and decision trees) were developed and trained using data collected from January 2016-October 2017. Models were developed separately, on data from the U.S. states, n …

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).

Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These … ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a sy …

Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study.

BACKGROUND: Data derived from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study were analyzed in an effort to employ machine learning methods to predict the composite endpoint described in the original study. METHODS: We identified 573 CORAL subjects with complete baseline data and the presence or absence of a composite endpoint for the study. These data were subjected to several models including a generalized linear (logistic-linear) model, support vector machine, decision tree, feed-forward neural network, and random forest, in an effort to attempt to predict the co…

Predicting primary outcomes of brain tumor patients with advanced neuroimaging MRI measures.

BACKGROUND: Advanced neuroimaging measures along with clinical variables acquired during standard imaging protocols provide a rich source of … 62; mean age = 51.2 yrs.) in a machine learning analysis in order to predict outcomes. RESULTS: A support vector machine classifier with a t-test filter and recursive feature elimination predicted patient mortality (18-month interval) with 80.7% accuracy, language deficits (transient) with 74.2%, motor deficits with 71.0%, language outcomes (persistent) with 80.7% and …

Rapid FTIR Spectral Fingerprinting of Kidney Allograft Perfusion Fluids Distinguishes DCD from DBD Donors: A Pilot Machine Learning Study.

BACKGROUND/OBJECTIVES: Rapid, objective phenotyping of donor kidneys is needed to support peri-implant decisions. Label-free Fourier-transfo … ocessing steps were applied, and feature extraction was based on the Fast Correlation-Based Filter. Support vector machines and Naïve Bayes were evaluated. Unsupervised structure was assessed based on cosine distance, multidimensional scaling, and hierarchical clustering. Two-dimensional correlation spectroscopy (2D-COS) was used to examine band co-variation. RESULTS: …

Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI.

The bottleneck associated with the validation of the parameters of the entropy model has limited the application of this model to modern fun … ized entropy values of these biomarked brain areas were regarded as the feature vectors input for a support vector machine to classify the epileptogenic hemisphere, and the classification effectiveness was cross-validated. Nine biomarked brain areas were confirmed by the optimized entropy values, including medial superior frontal gyrus and superior parietal gyrus (p < . …

Predicting sentinel lymph node metastasis in breast cancer using an interpretable machine learning approach based on multi-domain clinical features.

BACKGROUND: Accurate identification of sentinel lymph node (SLN) status is essential for surgical and adjuvant therapy decisions in breast c … del outputs and quantify feature contributions. RESULTS: Ten-fold cross-validation showed that the support vector machine (SVM) model achieved the best performance among nine ML algorithms. After optimizing to 26 variables, the internal test achieved an AUC of 0.892 (95% CI: 0.854-0.940), outperforming the clinical model (AUC 0.796, 95% CI: 0.791-0.811). In the prospec …

Classification and detection of Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes using Fourier-transform near infrared spectroscopy coupled with machine learning.

This study was conducted to investigate the potential use of Fourier-transform near-infrared (FT-NIR) spectroscopy combined with machine lea … methods, and supervised algorithms including partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). Furthermore, the accuracy of the developed ML pipelines was further enhanced through under-sampling and boosting steps. The results demonstrated that …

Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.

BACKGROUND/OBJECTIVES: Alzheimer's disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioecono … models, enhanced by explainable AI (XAI). METHODS: Four ML classifiers-K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)-were trained on demographic and clinical features. For stage-wise classification, five DL models-CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2-were applied to MRI scans. Interpretabil …

CT-Based Radiomics Models with External Validation for Prediction of Recurrence and Disease-Specific Mortality After Radical Surgery of Colorectal Liver Metastases.

PURPOSE: To build computed tomography (CT)-based radiomics models, with independent external validation, to predict recurrence and disease-s … n the models' building, using different families of machine learning (ML) algorithms. RESULTS: The Support Vector Machine and Naive Bayes ML classifiers provided the best predictive performance. A relevant role of second-order and higher-order texture features emerged from the largest lesion and the liver residual parenchyma. The prediction models for recurrence showed …