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Development and Validation of Machine Learning Models for Predicting 7-Day Mortality in Critically Ill Patients with Traumatic Spinal Cord Injury: A Multicenter Retrospective Study.

BACKGROUND: Traumatic spinal cord injury (TSCI), a severe central nervous system injury, despite treatment advances, critically ill patients … eighbors, Light Gradient Boosting Machine, Logistic Regression, Neural Network, Random Forest (RF), Support Vector Machine, and Extreme Gradient Boosting. Model Performance is evaluated via receiver operating characteristic curves, calibration curves, decision curve analysis, accuracy, sensitivity, specificity, precision, and F1 score, whereas Shapley Additive Explanati …

Изучение факторов, определяющих эволюцию хронических повреждений в множестве склерозов с помощью машинного обучения.

ЦЕЛИ: Исследование было направлено на выявление факторов, влияющих на эволюцию хронических повреждений при множественном склерозе (МС) с использованием метода машинного обучения. МАТЕРИАЛЫ И МЕТОДЫ: Продольные данные собирались у лиц с множественным склерозом (RRMS). Знак "железного обода" был определен с использованием количественного картографирования уязвимости (QSM), а микроструктурные повреждения были оценены с помощью коэффициентов восстановления потока (FLEAIR) с использованием T1/fluid. Дополнительные данные включали базовый объем повреждений, объем церебрального повреждения Т2-гиперин

Совпадение в аннотации оригинала
OBJECTIVES: The study aimed to identify factors influencing the evolution of chronic lesions in multiple sclerosis (MS) using a machine lear … res were integrated into machine learning models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) to predict lesion volume change, with the most predictive model selected for feature importance analysis. RESULTS: The study included 47 RRMS individuals (mean age, 30.6 ± 8.0 years [standard deviation], 6 males) and 833 chronic lesions. Mac …

Research on gut microbiota characteristics of PBC patients at different ALBI grades based on machine learning.

BACKGROUND: The Albumin-Bilirubin (ALBI) score and grade are widely used to stratify patients with primary biliary cholangitis (PBC) into di … s machine learning methods-including random forest (RF), lasso, gradient boosting machine (GBM) and support vector machine (SVM)-were employed to identify key features and to build and validate predictive models using bootstrap techniques. RESULTS: Clinical characteristics of ALBI grade 1 patients were comparatively better than those of ALBI grade 2 and 3 patients, inc …

Application of machine learning methods for predicting esophageal variceal bleeding in patients with cirrhosis.

OBJECTIVE: To develop and compare machine learning models based on CT morphology features, serum biomarkers, and basic physical conditions t … the 5-fold cross-validation was used in the research. The AUCs of linear regression, random forest, support vector machine, and adaptive boosting were, respectively, 0.742, 0.854, 0.719, and 0.821 in the training set. For the test set, the AUCs of models were, respectively, 0.763, 0.818, 0.648, and 0.804. CONCLUSIONS: Our study used CT morphological measurements, serum …

Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning.

OBJECTIVE: The macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complic … xtremely high (above 95%). During external validation, the performance evaluation revealed that the Support Vector Machine (SVM) model outperformed other models with an AUC value of 0.947, and the scorecard model has an AUC of 0.915. Additionally, we have established an online assessment system for early identification of MAS secondary to SLE. CONCLUSION: Machine learn …

[Machine learning methods in differential diagnosis of ACTH-dependent hypercortisolism].

AIM: To develop a noninvasive method of differential diagnosis of ACTH-dependent hypercortisolism, as well as to evaluate the effectiveness … lop predictive models: Linear Discriminant Analysis, Logistic Regression, elastic network (GLMNET), Support Vector machine (SVM Radial), k-nearest neighbors (kNN), Naive Bayes, binary decision tree (CART), C5.0 decision tree algorithms, Bagged CART, Random Forest, Gradient Boosting (Stochastic Gradient Boosting, GBM). RESULTS: The study included 223 patients (163 women …

Prediction of Acute Respiratory Distress Syndrome in Traumatic Brain Injury Patients Based on Machine Learning Algorithms.

Background: Acute respiratory distress syndrome (ARDS) commonly develops in traumatic brain injury (TBI) patients and is a risk factor for p … ing, light gradient boosting machine, Random Forest, adaptive boosting, complement naïve Bayes, and support vector machine. The performance of machine learning algorithms were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy and F score. Results: 649 TBI patients from the MIMIC-III database were included w …

AGTR1, PLTP, and SCG2 associated with immune genes and immune cell infiltration in calcific aortic valve stenosis: analysis from integrated bioinformatics and machine learning.

Background: Calcific aortic valve stenosis (CAVS) is a crucial cardiovascular disease facing aging societies. Our research attempts to identify immune-related genes through bioinformatics and machine learning analysis. Two machine learning strategies include Least Absolute Shrinkage Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE). In addition, we deeply explore the role of immune cell infiltration in CAVS, aiming to study the potential therapeutic targets of CAVS and explore possible drugs. Methods: Download three data sets related to CAVS from the…

Machine learning-based diagnostic method of pre-therapeutic 18F-FDG PET/CT for evaluating mediastinal lymph nodes in non-small cell lung cancer.

OBJECTIVES: We aimed to find the best machine learning (ML) model using 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for evaluating metastatic mediastinal lymph nodes (MedLNs) in non-small cell lung cancer, and compare the diagnostic results with those of nuclear medicine physicians. METHODS: A total of 1329 MedLNs were reviewed. Boosted decision tree, logistic regression, support vector machine, neural network, and decision forest models were compared. The diagnostic performance of the best ML model was compared with that of physicians. The ML method…

Pattern recognition techniques for automatic detection of suspicious-looking anomalies in mammograms.

We have employed two pattern recognition methods used commonly for face recognition in order to analyse digital mammograms. The methods are based on novel classification schemes, the AdaBoost and the support vector machines (SVM). A number of tests have been carried out to evaluate the accuracy of these two algorithms under different circumstances. Results for the AdaBoost classifier method are promising, especially for classifying mass-type lesions. In the best case the algorithm achieved accuracy of 76% for all lesion types and 90% for masses only. The SVM based algorithm did not perform as …

Fusion of MRI-radiomics and clinical features to improve pathological T-stage prediction of rectal cancer: a multicenter study.

BACKGROUND: Developing a rational model for precise preoperative staging of rectal cancer using magnetic resonance imaging is crucial for im … om T2WI images. Least absolute shrinkage and selection operator were applied for feature selection. Support vector machine classifier was used to develop the radiomics model. Six clinical features were collected and selected to build the clinical model using the recursive feature elimination algorithm. A weighted fusion method was applied to combine the radiomics with c …

FT-MDNNMDs: early detection of breast cancer using fine-tuned multi-deep neural networks with TCGA and clinical image datasets.

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early and accurate detection is critical … odel achieves an accuracy of 99.57%, outperforming conventional machine learning algorithms such as Support Vector Machine (94.46%), Decision Tree (93.54%), and Naïve Bayes (91.22%). The model also achieved superior MCC (99.46%), F-score (99.57%), and recall (99.63%). In addition to algorithmic comparison, the performance of the proposed model is compared with existing …

MRI-Based Radiomics Model for Predicting Eligibility for Conservative Management of Endometrial Cancer.

OBJECTIVE: Various machine learning and deep learning methods were used to develop and validate radiomics models based on magnetic resonance … important features. Radiomics models were constructed using four machine learning methods including support vector machine (SVM), random forest, decision tree, logistic regression, and one deep learning method that is multilayer perceptron (MLP). They were validated using five-fold cross validation and tested on the external test set. The performance of the models was e …

Identification of serum metabolite biomarkers and metabolic reprogramming mechanisms to predict recurrence in cholangiocarcinoma.

Cholangiocarcinoma (CCA) has high recurrence rates that severely limit long-term survival. Effective tools for accurate recurrence monitorin … mance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was conducted. Support Vector Machine (SVM) modeling was employed to develop a predictive framework based on metabolite biomarkers. The analysis revealed significant alterations in metabolomics and lipidomics across CCA recurrence subtypes. Notably, changes in metabolites such as amino …

Salivary gland tumor detection from saliva to theranostic application of surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) can be considered a rapid, label-free, nondestructive analytical measurement for tumor detection … ed and optimized using other supervised methods, e.g., partial least squares-discriminant analysis, support vector machine classification, and linear discriminant analysis-principal component analysis. Then, their classification abilities were tested with external samples, achieving impressive accuracy. The study showed that the SERS spectra of the two analyzed classes …

Diagnostic performance of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for detecting osteoarthritis and rheumatoid arthritis from blood serum.

Osteoarthritis (OA) and rheumatoid arthritis (RA) are the two most common rheumatic diseases worldwide, causing pain and disability. Both co … rometer. Machine learning models combining Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) were trained to binary classify preprocessed ATR-FTIR spectra as follows: controls vs. OA, controls vs. RA, and OA vs. RA. For a separated test dataset and the validation dataset, the overall model performance was better in classifying between …

Differential alterations of structural network in temporal lobe epilepsy with different seizure types are associated with cognitive and psychiatric status.

BACKGROUND: The fundamental pathophysiologic understanding of different seizure types in Temporal lobe epilepsy (TLE) remains unclear. This … e relationships between clinical variables and disrupted network characteristics. Additionally, the support vector machine (SVM) algorithm was utilized for the classification of controlled and uncontrolled FBTCS. RESULTS: Compared to HC, TLE seizure type subgroups presented differently aberrant SC within the frontostriatal network. Additionally, alterations in the rich …

Development of a neoadjuvant chemotherapy efficacy prediction model for nasopharyngeal carcinoma integrating magnetic resonance radiomics and pathomics: a multi-center retrospective study.

OBJECTIVE: This study aimed to develop and validate a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NACT) in nasopharyngeal carcinoma (NPC) by integrating radiomics and pathomics features using a particle swarm optimization-supported support vector machine (PSO-SVM). METHODS: A retrospective multi-center study was conducted, which included 389 NPC patients who received NACT from three institutions. Radiomics features were extracted from magnetic resonance imaging scans, while pathomics features were derived from histopathological images. A total of 2,667 radiomics f…

Decoding the proteome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for cell-penetrating peptides involved in pathogenesis or applicable as drug delivery vectors.

Synthetic or natural derived cell-penetrating peptides (CPPs) are vastly investigated as tools for the intracellular delivery of membrane-impermeable molecules. As viruses are intracellular obligate parasites, viral originated CPPs have been considered as suitable intracellular shuttling vectors for cargo transportation. A total of 310 CPPs were identified in the proteome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Screening the proteome of the cause of COVID-19 reveals that SARS-CoV-2 CPPs (SCV2-CPPs) span the regions involved in replication, protein-nucleotide and protei

Automatic detection of microsleep episodes with feature-based machine learning.

STUDY OBJECTIVES: Microsleep episodes (MSEs) are brief episodes of sleep, mostly defined to be shorter than 15 s. In the electroencephalogra … quantified eye movements, and median frequency. Three algorithms were used for MSE classification: support vector machine (SVM), random forest (RF), and an artificial neural network (long short-term memory [LSTM] network). Data of 53 patients were used for the training of the classifiers, and 23 for testing. RESULTS: MSEs were identified with a high performance (sensi …