Результаты поиска

Computer-aided autism diagnosis using visual attention models and eye-tracking: replication and improvement proposal.

BACKGROUND: Autism Spectrum Disorder (ASD) diagnosis can be aided by approaches based on eye-tracking signals. Recently, the feasibility of building Visual Attention Models (VAMs) from features extracted from visual stimuli and their use for classifying cases and controls has been demonstrated using Neural Networks and Support Vector Machines. The present work has three aims: 1) to evaluate whether the trained classifier from the previous study was generalist enough to classify new samples with a new stimulus; 2) to replicate the previously approach to train a new classifier with a new dataset…

RGS5 as a Biomarker of Pericytes, Involvement in Vascular Remodeling and Pulmonary Arterial Hypertension.

INTRODUCTION: Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by a sustained rise in mean pulmonary artery … ened using four algorithms, including weighted gene co-expression network analysis, GEO2R analysis, support vector machines recursive feature elimination and robust rank aggregation rank analysis. Regulator of G-protein signaling 5 (RGS5), a pro-apoptotic/anti-proliferative protein, which regulate arterial tone and blood pressure in vascular smooth muscle cells. The exp …

Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models.

BACKGROUND: Preoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation of pers … anual image segmentation of CT slices. The radiomics and radiomics-DL models were constructed using support vector machines (SVMs). Moreover, based on the fusion of clinical and radiological features, the best combined feature set was obtained according to the Akaike information criterion (AIC) analysis. Then the fused model was constructed using logistic regression. R …

Trajectories of craving during medication-assisted treatment for opioid-use disorder: Subtyping for early identification of higher risk.

AIMS: To examine evidence for subtypes of opioid craving trajectories during medication for opioid use disorder (MOUD), and to (a) test whether these subtypes differed on MOUD-related outcomes, and (b) determine whether nonresponders could be identified before treatment initiation. DESIGN, SETTING, AND PARTICIPANTS: Outpatients (n = 211) being treated with buprenorphine or methadone for up to 16 weeks. Growth mixture modeling was used to identify unobserved craving-trajectory subtypes. Support Vector Machines (SVM) were trained to predict subtype membership from pretreatment data. MEASUREMEN…

Voice Analysis for Neurological Disorder Recognition-A Systematic Review and Perspective on Emerging Trends.

Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disor … of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Ano …

Identification of breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment by an intelligent vacuum-assisted biopsy.

BACKGROUND: Neoadjuvant systemic treatment elicits a pathologic complete response (pCR) in about 35% of women with breast cancer. In such cases, breast surgery may be considered overtreatment. We evaluated multivariate algorithms using patient, tumor, and vacuum-assisted biopsy (VAB) variables to identify patients with breast pCR. METHODS: We developed and tested four multivariate algorithms: a logistic regression with elastic net penalty, an Extreme Gradient Boosting (XGBoost) tree, Support Vector Machines (SVM), and neural network. We used data from 457 women, randomly partitioned into trai…

Prediction of dyslipidemia using gene mutations, family history of diseases and anthropometric indicators in children and adolescents: The CASPIAN-III study.

Dyslipidemia, the disorder of lipoprotein metabolism resulting in high lipid profile, is an important modifiable risk factor for coronary heart diseases. It is associated with more than four million worldwide deaths per year. Half of the children with dyslipidemia have hyperlipidemia during adulthood, and its prediction and screening are thus critical. We designed a new dyslipidemia diagnosis system. The sample size of 725 subjects (age 14.66 ± 2.61 years; 48% male; dyslipidemia prevalence of 42%) was selected by multistage random cluster sampling in Iran. Single nucleotide polymorphisms (rs18

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method.

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based … discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features th …

A prototype tobacco-associated oral squamous cell carcinoma classifier using RNA from brush cytology.

BACKGROUND: Oral cancer in the form of squamous cell carcinoma (OSCC) is typically detected in advanced stages when treatment is complex and … mples from tobacco/betel nut users. With the measurement of two additional mRNAs and analysis using support vector machines algorithm for class prediction of these cancers was produced. RESULTS: This OSCC classifier based on the levels of 5 mRNAs in RNA from brush cytology initially showed 0.93 sensitivity and 0.91 specificity in differentiating OSCC from benign oral m …

Ручная обработка против методов извлечения глубоких признаков для диагностики множественной скlerоза на основе МРТ.

Фон: Несмотря на значительные продвижения в анализе медицинских изображений, автоматический диагноз множественной скlerоза (МС) на основе магнитно-резонансной томографии (МРТ) остается сложной задачей из-за сложности 3D-данных мозга и вариабельности вида плоскостей поражений. Цель: В этой работе мы предлагаем эффективную и оптимизированную систему извлечения признаков для автоматического диагноза МС с использованием МРТ с весом FLAIR, T1 и T2. Предложенный подход улучшает Декимальные Дескрипторные Паттерны (DDP) за счет интеграции локальной информации градиента, создавая 3D-представление текст

Совпадение в аннотации оригинала
Background: Despite significant advances in medical image analysis, automated diagnosis of Multiple Sclerosis (MS) from magnetic resonance i … sing multiple classical machine learning classifiers, including Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Logistic Regression. Furthermore, the performance of the proposed handcrafted feature-based approach was compared to features extracted using a deep learning-based model (vision-language model, VLM), specifical …

Leveraging data-driven machine learning: From explainable risk prediction to hierarchical clustering-based subtypes of postoperative delirium in a prospective non-cardiac surgery cohort.

STUDY OBJECTIVE: To leverage perioperative indicators in developing an explainable machine learning (ML) model for postoperative delirium (P … d six algorithms: adaptive boosting with classification trees, random forest (RF), neural networks, support vector machines, extreme gradient boosting with classification trees and logistic regression. SHapley Additive exPlanations (SHAP). was used to interpret the best one and to externally validate it in another large tertiary hospital. Among patients who developed PO …

Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Review.

Malnutrition is a critical complication among cancer patients, affecting ≤80% of individuals depending on cancer type, stage, and treatment. … under the curve >0.80). Machine learning algorithms, including decision trees, random forests, and support vector machines, outperformed conventional screening tools. Deep learning models applied to medical imaging achieved high segmentation accuracy (Dice similarity coefficient: 0.92-0.94), enabling early cachexia detection. AI-driven virtual dietitian systems improve …

Predicting gross-total resection in skull base chondrosarcoma: a multi-institutional machine learning study.

OBJECTIVE: The aim of this study was to develop and validate an anatomy-driven model that predicts gross-total resection (GTR) of skull base … alidation (n = 46) sets. Five algorithms were tuned: generalized linear model (GLM), random forest, support vector machine, k-nearest neighbors, and eXtreme Gradient Boosting. For interpretability, a multivariable logistic regression model was fitted and a nomogram for predicting GTR was built. Discrimination performance was summarized by the area under the curve (AUC), …

A leakage-controlled and SHAP driven machine learning framework for paediatric respiratory disease classification using Indian hospital EHR data.

BACKGROUND: Classification of paediatric respiratory diseases is important for timely diagnosis and decision-making. Real-world clinical data is often compounded by challenges of data leaking, class imbalance, and high dimensional data modalities, which might compromise the reliability, methodological rigour, and interpretability of the model. METHODS: This research work introduces a leakage-controlled and explainable machine learning model for multiclass classification of respiratory diseases in children namely Bronchitis, Bronchopneumonia, Upper Respiratory Tract Infection (URTI), and Wheeze

Identifying Biomarkers Using a Portable, Home-Based Eye-Tracking System to Predict Short-Term Visual Fatigue Deterioration: Prospective Observational Feasibility Study.

BACKGROUND: The escalating prevalence of screen-related eye fatigue has become a health burden in the digital era worldwide, yet routine mon … ics showing significant between-group differences were used as inputs for four ML models, including support vector machine (SVM), decision tree, extreme gradient boosting (XGBoost), and random forest, to identify deteriorated asthenopia. Model performance was evaluated with fivefold cross-validation. RESULTS: This study enrolled 38 participants aged 19-31 (mean 24.8, S …

Profiling cognition and brain metabolism in amyotrophic lateral sclerosis and frontotemporal dementia.

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are described as a disease continuum, given their shared clinical, gen … ty to differentiate ALS-cn, ALS-FTD and bvFTD using the identified markers, machine-learning models-support vector machine (SVM) and random forest (RF)-were applied, offering a streamlined, data-driven approach to improve diagnostic precision across this spectrum of disorders. Twenty ALS-cn, 19 ALS-FTD and 21 bvFTD patients were included. Neuropsychological composite z- …

Radiomic features and carotid stenosis in periodontitis a two stage bootstrap and multimodal machine learning study.

This study aims to develop and validate a deep learning model based on Cone Beam Computed Tomography (CBCT) radiomic features to achieve ear … sing Logistic Regression combined with AIC. Three machine learning models—Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF)—were developed and evaluated using five-fold cross-validation. The best-performing model was the RF model, achieving an Area Under the Curve(AUC) of 0.892, sensitivity of 0.957, specificity of 0.710, and accuracy of 0.8 …

Development and validation of venous thrombosis risk prediction model and scale for lung cancer patients in intensive care unit based on interpretable machine learning.

BACKGROUND: Venous thromboembolism (VTE) is a prevalent and grave complication in patients with lung cancer, with an incidence of more than … rest neighbors (KNN), Light Gradient Boosting Machine (LightGBM), Naïve Bayes (NBS), random forest, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). Model interpretability was elucidated using SHapley Additive exPlanations (SHAP) values. A practical clinical scale was developed based on the optimal model, and the cutoff value was determined using t …

Attention-based Transformer-LSTM architecture for early diagnosis and staging of early-stage Parkinson's disease using fNIRS data.

BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder requiring early diagnosis and accurate staging for optimal … UC analysis. The ATLAS-PD model was compared with traditional machine learning algorithms including Support Vector Machine, Random Forest, K-Nearest Neighbors, and Back-Propagation Neural Network. McNemar's test and bootstrap resampling were conducted to assess superiority. Interpretability analysis was conducted using permutation importance to quantify channel contribu …

Combination of Naples prognostic score and pathological factors for evaluating the long-term prognosis of patients with late-onset colorectal cancer: a multicenter machine learning study.

BACKGROUND: Given the increasingly aging population, precise prognostic models are essential for optimizing postoperative care in patients w … Cox regression, generalized boosted regression modeling, supervised principal components, survival support vector machine, partial least squares regression for Cox, and Cox-Boost were used to identify predictive models for LOCRC. Model performance was assessed by the concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA). RESULTS: I …