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Алгоритмы машинного обучения для виртуального раннего обнаружения и выявления нейродегенеративных и нейрокогнитивных расстройств: систематический обзор.

Происхождение и аналоговые расстройства (например, болезнь Альцгеймера, болезнь Паркинсона) приводят к нейронной потере; нейрокогнитивные расстройства (например, бред, слабоумие) свидетельствуют о когнитивном упадке. Раннее обнаружение имеет решающее значение для эффективного управления. Машинное обучение помогает в более точном выявлении заболеваний, потенциально преобразуя медицинское обслуживание. В рамках этого всеобъемлющего систематического обзора обсуждается вопрос о том, каким образом машинное обучение (ML) может способствовать раннему выявлению этих расстройств, превосходя при этом тр

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… vector machine | Random Forest (RF) or Support Vector Machine (SVM) have the potential to detect PD. | The study suggests a novel approach to Parkinson disease identification through free speech analysis. … The support vector machine (SVM) tools were also analyzed. …

Machine learning for acute kidney injury: Changing the traditional disease prediction mode.

Acute kidney injury (AKI) is a serious clinical comorbidity with clear short-term and long-term prognostic implications for inpatients. The diversity of risk factors for AKI has been recognized in previous studies, and a series of predictive models have been developed using traditional statistical methods in conjunction with its preventability, but they have failed to meet the expectations in limited clinical applications, the rapid spread of electronic health records and artificial intelligence machine learning technology has brought new hope for the construction of AKI prediction models. In

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… vector machines | Classification | ■ Support vector machines is a class of generalized linear classifiers that perform binary classification of data in a supervised learning manner, and whose decision boundary is the maximum margin hyperplane solved for the learned samples. | ■ Can solve high-dimensional … vector machines | None | KDIGO: Creatinine Alfieri et al. ( 23 ) | eICU and MIMIC-III | 35,573 | 0.89 | None | Deep learning Logistic regression | None | AKIN: Creatinine and urine output Qian et al. ( 18 ) | MIMIC-III | 17,205 | 0.905 | None | Logistic regression Support vector machines Random forest …

A review on machine learning approaches and trends in drug discovery.

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in t

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… Support vector machines Vapnik introduced Support Vector Machines (SVM) in the late seventies [124] . … The support vector machines look for the hyper plane that best separates the data, maximizing the generalization capacity of the model. …

Модель диагностики болезней MS на основе плотности с использованием алгоритма поиска капуцинов и ансамбля глубоких нейронных сетей.

Множественный склероз (MS) — серьезная болезнь мозга, которая постоянно разрушает клетки мозга, влияет на зрение, баланс, мышечный контроль и повседневную активность. В этом исследовании используется взвешенное сочетание глубоких нейронных сетей и методов оптимизации для диагностики заболеваний MS. Этот метод использует кусочки изображений магнитного резонанса (MRI) в качестве входных. Затем, после предварительной обработки, процесс сегментации и идентификации района, представляющего интерес (РОИ), осуществляется с использованием сочетания алгоритма " нечеткие " c-means (FCM) и алгор

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… The independent component analysis network dataset was analyzed using RF, support vector machine (SVM), k-nearest neighbors (KNN), Naïve Bayes (NB), and artificial neural network (ANN) techniques. … The best results were obtained with ensemble decision trees (EDT) and ensemble support vector machines (ESVM), which had accuracy rates of 98.2% and 98.5%, respectively. …

Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.

IntroductionArtificial intelligence (AI) is a branch of technology enabling machines to emulate complex human skills; it can also entail problem-solving using bioinspired methods. It is used for automating systematic literature reviews (SLR), that is, defining a clinical question, locating relevant literature, preliminary screening, study evaluation, data extraction and analysis. Title and abstract screening is one of the most time-consuming and error-prone phases involved in developing a systematic review. While AI promises to expedite this process, adopting it faces challenges due to concern

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… vector machines, stochastic gradient descent | Pancreatic cancer, Indoor allergen reduction, Inguinal hernia [ 111 ] | Popoff | Canada | 2020 | Support vector machine (SVM), Naïve Bayes, bagged CART | Liver cancer, Lung cancer, Psoriasis, Melanoma, Obesity [ 112 ] | Hamel | Canada | 2020 | DistillerSR … vector machine (SVM) | Digital Technology Use and Adolescent Mental Health. …

Искусственный интеллект основывается на достижениях в области наномедицины для лечения мозговых расстройств: обновленный обзор повествования.

Наномедицина - это наномасштабные, биосопоставимые материалы, которые предлагают перспективные альтернативы традиционным методам лечения расстройств мозга. Технологические достижения в области искусственного интеллекта (АИ), в частности машинного обучения (МЛ) и глубокого обучения (ДЛ), трансформируют область наномедицины путем совершенствования диагностики заболеваний, биомаркировки, прогнозирования и мониторинга заболеваний, целевых поставок наркотиков и терапевтического вмешательства, а также содействия расчетным и методологическим разработкам. Эти успехи могут быть достигнуты за счет анали

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… Vector Machine. … PD vs. healthy controls Parkinson’s disease Blood (Plasma) Next Generation Sequencing (NGS) Plasma EV-derived miRNA Support Vector Machine (SVM) 15. …

Распространение эпсилонного токсина Clostridium Perfringens изолируется среди пациентов с множественным склерозом и расстройством спектра нейромиелита в Иране.

Клостридий перфрингенс эпсилон токсин (ETX) является мощным нейротоксином, который пересекает мозговой барьер (BBB) и который, как было предложено, связан с демиелинизирующими расстройствами CNS. Здесь мы изучили распространенность C. перфрингенов и антиETX иммуноглобулина G (IgG) и иммуноглобулина M (IgM) в стуле и сере пациентов с множественным склерозом (MS) и спектральным расстройством нейромиелита (NMOSD) по сравнению со здоровым контролем. МЕТОДЫ: В период с сентября 2019 года по июнь 2020 года было собрано 43 образца стула и 43 образца серы из MS (n = 33) и NMOSD (n = 10) пациентов, а т

Real-World Data and Machine Learning to Predict Cardiac Amyloidosis.

(1) Background: Cardiac amyloidosis or "stiff heart syndrome" is a rare condition that occurs when amyloid deposits occupy the heart muscle. Many patients suffer from it and fail to receive a timely diagnosis mainly because the disease is a rare form of restrictive cardiomyopathy that is difficult to diagnose, often associated with a poor prognosis. This research analyses the characteristics of this pathology and proposes a statistical learning algorithm that helps to detect the disease. (2) Methods: The hospitalization clinical (medical and nursing ones) records used for this study are the ba

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma.

IntroductionAs a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM.

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… Among 109 features, 61 were proved to yield great contributions to the identification of three GBM subtypes, and the RBF-based support vector machine (SVM) gives the best diagnostic performance. … vector machine recursive feature elimination (SVM-RFE) was used to evaluate all 109 signatures. …

Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma.

Multiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient's disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3)

Artificial intelligence for risk assessment and outcome prediction in malignant haematology.

Risk stratification is the backbone of clinical decision-making for patients with haematological malignancies. Models currently used in routine clinical practice often rely on static rules, conventional statistics and expert consensus. While pragmatic and accessible, these models fail to capture the complex biological interactions and patient heterogeneity, especially as data dimensionality grows given the availability of more comprehensive molecular analysis. Artificial intelligence (AI), particularly machine learning (ML), offers the potential to process such high-dimensional data, uncover l

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… function support vector machine; RF, random forest; SVM, support vector machine; W‐Bayes Net, weighted Bayesian Network; XGB, extreme gradient boosting. … vector machine; XGB, extreme gradient boosting. …

Современные методы диагностики медицинских паразитов и артроподов

Обычные методы, такие, как микроскопия, используются для диагностики паразитических заболеваний и медицинских заболеваний, связанных с артроподами, на протяжении многих лет. Некоторые методы считаются золотыми. Однако их ограниченная чувствительность, специфичность и точность, а также потребность в дорогостоящих реагентах и высококвалифицированных технических специалистах являются критическими проблемами. В этой связи постоянно разрабатываются новые инструменты для уменьшения числа проблем. Недавно three Возникли самые современные методы: штрих-кодирование ДНК, геометрические морфометры и иску

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… Finally, identification of Plasmodium species was processed based on special characteristics, such as ring- or band-form trophozoites, using deep neural network or support vector machine algorithms ( Figure 4 ) [ 55 ]. …

Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances.

Background: Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous malignancy, with various outcomes despite significant advances in therapeutic options. Current conventional prognostic tools, e.g., the International Prognostic Index (IPI), lack sufficient precision at an individual patient level. However, artificial intelligence (AI), including machine learning (ML) and deep learning (DL), can enable specialists to navigate complex datasets, with the final aim of improving prognostic models for DLBCL. Objectives: This scoping review aims to systematically map the current litera

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… vector machine; VRL, vitreoretinal lymphoma. … RB-CHOP trial; R-CHOP, rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone; SVM, support vector machine; TCGA, The Cancer Genome Atlas. …

A Scalable Radiomics- and Natural Language Processing-Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study.

BackgroundThe identification of objective pain biomarkers can contribute to an improved understanding of pain, as well as its prognosis and better management. Hence, it has the potential to improve the quality of life of patients with cancer. Artificial intelligence can aid in the extraction of objective pain biomarkers for patients with cancer with bone metastases (BMs).ObjectiveThis study aimed to develop and evaluate a scalable natural language processing (NLP)- and radiomics-based machine learning pipeline to differentiate between painless and painful BM lesions in simulation computed tomo

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… vector machine), and NNet (neural networks) classifiers. … We are unable to offer a convincing explanation as to why neural networks outperformed random forest and support vector machine classifiers in our analysis. …

Применение искусственного интеллекта в перипостетической суставной инфекции.

Перипростетическая суставная инфекция (ПСО) представляет собой one Из наиболее разрушительных осложнений, вызванных общей совместной артропластикой, часто требующих дополнительных операций и антимикробной терапии, и потенциально ведущих к инвалидности. Это значительно увеличивает нагрузку как на пациентов, так и на систему здравоохранения. С учетом значительных страданий, причиняемых ПСО, его профилактика и лечение уже давно являются ключевыми факторами, вызывающими озабоченность. Вместе с тем сохраняются проблемы, связанные с точной оценкой индивидуального риска, предотвращением инфекции, сов

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… Vector Machine; KSVM, Kernel Support Vector Machine; LGBM, Light Gradient Boosting Machine; MLP, multi-layer perceptron; ELWV, ensemble learning of weighted voting; MSIS, Musculoskeletal Infection Society; EBJIS, European Bone and Joint Infection Society; ACC, accuracy. … Vector Machine; KSVM, Kernel Support Vector Machine; LGBM, Light Gradient Boosting Machine; MLP, multi-layer perceptron; ELWV, ensemble learning of weighted voting; MSIS, Musculoskeletal Infection Society; EBJIS, European Bone and Joint Infection Society; ACC, accuracy. …

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer.

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of

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… These were the processes that ultimately led to the creation of a predictive binary classification model with support-vector machines (SVM). … vector machine; SNF, similarity network fusion; SNN, super-layered neural network architecture; SparRec, Sparse Recovery; SVD, singular value decomposition; SVM, support vector machine; t-SNE, t-distributed stochastic neighbor embedding; UNIPred, unbalance-aware network integration and prediction of …

Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma.

This paper investigates the application of Survival Analysis (SA) techniques to forecast outcomes after autologous Hematopoietic Stem Cell Transplantation (aHSCT) for Multiple Myeloma (MM). By leveraging six SA models, we examine their predictive capabilities, measured through the Concordance Index (C-index) metric. Beyond evaluating model performance, we analyze feature importance using permutation and SHAP methods, highlighting key clinical factors such as treatment history, disease stage, and prior disease progression or relapse as critical predictors of survival. The findings suggest that

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… A comprehensive review of ML methodologies applied in HSCT highlights the frequent use of ensemble techniques, regression models, Bayesian approaches, and support vector machines, which predominantly use clinical and genetic data to drive their predictions [ 10 ]. … The SVMs employed in this study are the Fast Survival Support Vector Machine (FS) and the Fast Kernel Survival Support Vector Machine (FKS). …

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis.

Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thou

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… Support vector machine (AUC 0.95, p = 0.477), random forest (AUC 0.97, p = 0.31), and gradient boosting machine (AUC 0.98) showed comparable capacity to differentiate cardiac amyloidosis and hypertrophic cardiomyopathy when compared to logistic regression (AUC 0.91) [ 66 ]. … Pafig | Hexpeptides associated with amyloid fibrillar aggregates | Support vector machine | Accuracy of 81% and Matthews correlation coefficient of 0.63 | [ 128 ] Pse-in-One | DNA, RNA, and protein sequences | Support vector machine and neural network | Matthews correlation coefficient 0.739, sensitivity …

Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to significant global health burden, including both acute infections and persistent post-acute sequelae, also known as Long COVID (LC) in survivors. While clinical management has reduced case-fatality rates, a substantial proportion of patients develop LC, a heterogeneous syndrome with long-term symptoms. This complex continuum requires therapeutic strategies for both the acute and chronic phases. Plasma proteomics has emerged as a powerful tool in precision medicin

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… Supervised learning algorithms such as support vector machines, random forests, and gradient boosting methods have been used to identify protein panels associated with severity, including inflammatory cytokines, complement components, and coagulation markers [ 5 , 140 ]. … Table 3 Comparative analysis of classical and deep learning models for diagnostic and prognostic prediction of SARS-CoV-2 sequelae Model | Type | Application | Strengths | Application in COVID-19 | Application in long COVID (LC) | Reference support vector machine (SVM) | Supervised | Patient classification …

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has bee

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… ML algorithms such as Random Forest (RF) and Support Vector Machine (SVM) can be engaged to enhance the performance of scoring function. … Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM), as well as other methods, could be used for a large amount of unstructured information analysis for new drug target identification. …