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Machine learning-based prediction of one-year mortality after alloHCT identifies the impact of pre-transplant immunity and inflammation.

Accurate prediction of mortality after allogeneic hematopoietic stem cell transplantation (alloHCT) is essential for individualized treatment decisions, yet existing clinical risk scores capture only a limited number of variables and show modest predictive performance. In our single-center retrospective analysis, we included data from 909 adult patients with hematologic malignancies undergoing alloHCT. We used 31 features to build machine-learning models to predict death within the first year after alloHCT. These features included established clinical risk factors together with pre-transplant

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30

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… Supervised machine learning models rely on labelled input data to learn the relationship with output training data [ 43 ], and are often used to classify data or make predictions. …

Machine Learning Meets Cancer.

The role of machine learning (a part of artificial intelligence-AI) in the diagnosis and treatment of various types of oncology is steadily increasing. It is expected that the use of AI in oncology will speed up both diagnostic and treatment planning processes. This review describes recent applications of machine learning in oncology, including medical image analysis, treatment planning, patient survival prognosis, and the synthesis of drugs at the point of care. The fast and reliable analysis of medical images is of great importance in the case of fast-flowing forms of cancer. The introductio

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… Supervised machine learning utilizes mean apparent diffusion coefficient (ADC) or mean cerebral blood volume (CBV) data features and high and low grade of tumor types as classes for mathematical algorithm training and assigning datasets to classes. … The study in [ 62 ] examined automatic contour scoring using fit indices and supervised machine learning. …

Decoding Immunodeficiencies with Artificial Intelligence: A New Era of Precision Medicine.

Primary and secondary immunodeficiencies comprise a wide array of illnesses marked by immune system abnormalities, resulting in heightened vulnerability to infections, autoimmunity, and cancers. Notwithstanding progress in diagnostic instruments and an enhanced comprehension of the underlying pathophysiology, delayed diagnosis and underreporting persist as considerable obstacles. The implementation of artificial intelligence into clinical practice has surfaced as a viable method to enhance early detection, risk assessment, and management of immunodeficiencies. Recent advancements illustrate ho

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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… An Overview of the Supervised Machine Learning Methods. Horizons 4, 51–62. 10.20544/horizons.b.04.1.17.p05 Nicora G., Vitali F., Dagliati A., Geifman N., Bellazzi R. (2020). Integrated Multi-Omics Analyses in Oncology: a Review of Machine Learning Methods and Tools. Front. …

Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.

Cardiovascular disease remains a major global health burden. Owing to its complex pathogenesis and marked clinical heterogeneity, conventional one-size-fits-all strategies often yield limited benefit for a substantial proportion of patients. Precision medicine advocates individualized management based on patients' clinical and molecular characteristics to improve outcomes. In this context, multi-omics and machine learning provide critical technical support for precision medicine: multi-omics can capture the full spectrum of cardiovascular disease from molecular alterations to phenotypic manife

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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… Database | Search strategy PubMed | “deep learning”[Title/Abstract] OR “support vector machine”Title/Abstract] OR “Machine learning”[Title/Abstract] OR “supervised machine learning”[Title/ Abstract] OR “unsupervised machine learning”[Title/Abstract] OR “Machine learning”[MeSH Terms] OR “supervised machine … machine learning. | 80 participants | Mild Cognitive Impairment and Parkinson’s Disease | Decision Tree (DT) Random Forest (RF) Naïve Bayes (NB) Support Vector Machine (SVM) K-Nearest Neighbor (KNN) | SVM and RF showed the best performance and detected MCI with an accuracy of over 80.0%. | The study …

Искусственные интеллектуальные технологии зондирования для борьбы с раком, диабетом и COVID-19.

Интеграция искусственного интеллекта (АИ) с износимыми сенсорными технологиями может привести к революции в мониторинге и лечении различных хронических заболеваний и острых заболеваний. Комбинированные износы АИ классифицируются по таким основополагающим методам зондирования, как электрохимическая, колориметрическая, химическая, оптическая, а также давление/стабильность. Алгоритмы АИ повышают эффективность изношенных датчиков, обеспечивая индивидуализированный, постоянный надзор и прогнозный анализ, помогая распознавать время и оптимизируя терапевтические методы. В этой рукописи рассматриваютс

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… Liu et al. [ 16 ] We utilized supervised machine learning technology to analyze data from 40 patients’ smartwatches. It demonstrated excellent predictive accuracy for mortality in end-of-life cancer patients, achieving 93%. …

Research on RNA modification in disease diagnosis and prognostic biomarkers: current status and challenges.

RNA modification, as a crucial post-transcriptional regulatory mechanism, plays a pivotal role in normal physiological processes and is closely associated with the onset and progression of various human diseases. Recent studies have highlighted significant alterations in the level of RNA modifications, including m6A, m6Am, m1A, m5C, m7G, ac4C, Ψ, and A-to-I editing, across multiple diseases. These findings suggest the potential of RNA modifications and their regulatory factors as biomarkers for early disease diagnosis and prognosis. This review provides an overview of statistical methods, mach

Полуобучение для прогнозирования множественного склероза.

Фон: Множественный склероз (MS) — хроническое аутоиммунное заболевание центральной нервной системы с склонностью вызывать серьезные неврологические расстройства. Точное и раннее прогнозирование прогресса в области НР имеет исключительно важное значение для управления им и его лечения. Методы: В настоящем документе мы сравниваем ряд самомаркированных полуконтролируемых методов обучения, используемых для прогнозирования MS на основе маркированных и немаркированных медицинских данных. В частности, мы сравниваем результаты самоподготовки, СЕТРЕД, совместной подготовки, совместной подготовки с Коми

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… Semi-Supervised Algorithms Self-labeling strategies are one class of semi-supervised machine learning methods that apply model prediction as pseudo-labels for unlabeled data. …

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

Artificial Neural Network Prediction of Mortality in Cancer Patients Presenting for Radiation Therapy at a Multisite Institution.

IntroductionFor many decades, the management of cancer has utilized radiation therapy, which continues to evolve with technology to improve patient outcomes. However, despite the standardization of treatment plans and the establishment of best clinical practices based on prospective, randomized trials and adherence to National Comprehensive Cancer Network (NCCN) guidelines, the outcomes from radiation therapy are highly variable and dependent on a number of factors, including patient demographics, tumor characteristics/histology, and treatment parameters. In this study, we attempt to use avail

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… Several commonly used supervised machine learning models will be described briefly. Logistic regression is a technique that uses a logistic model with multivariate input and outputs a binary value (i.e., 1 or 0). It performs computations comparable to those of a single-node neural network. …

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges.

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover

Искусственный интеллект, видоизменяющий парадигму гематологического диагноза и лечения злокачественных заболеваний: от статической оценки до динамического управления точностью.

Искусственный интеллект (ИИ) все чаще изучается в качестве инструмента содействия более точному и динамичному управлению диагностикой и лечением гематологов. В отличие от предыдущих обзоров, в которых основное внимание уделялось отдельным типам заболеваний или изолированным технологическим путям, в настоящем документе содержится всеобъемлющий обзор текущих прикладных программ АИШ и последних достижений в области диагностики, классификации, оценки прогнозов и принятия решений по лечению лейкемии, лимфомы, множественной миэломы и миэлодиспластических синдромов. Она охватывает ключевые технически

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… Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RCT et al (2002) Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8:68–74. 10.1038/nm0102-68 22. … Eckardt J-N, Röllig C, Metzeler K, Kramer M, Stasik S, Georgi J-A et al (2023) Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning. Haematologica 108:690–704. 10.3324/haematol.2021.280027 49. …

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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… Developing a supervised machine learning model for predicting perioperative acute kidney injury in arthroplasty patients. Comput Biol Med. (2022) 144:105351. 10.1016/j.compbiomed.2022.105351 53. Caragata R, Wyssusek KH, Kruger P. …

The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?

Hematopoietic stem cell transplantation (HSCT) is a life-saving therapy for hematologic malignancies, such as leukemia and lymphoma and other severe conditions but is associated with significant risks, including graft versus host disease (GVHD), relapse, and treatment-related mortality. The increasing complexity of clinical, genomic, and biomarker data has spurred interest in machine learning (ML), which has emerged as a transformative tool to enhance decision-making and optimize outcomes in HSCT. This review examines the applications of ML in HSCT, focusing on donor selection, conditioning re

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… Support Vector Machine (SVM) | They are supervised machine learning algorithms used for classification and regression. They work by finding the optimal hyperplane that maximizes the margin between data points of different classes, often using kernel functions to handle non-linear separations. …

Революционизация медицины сна: влияние машинного обучения на диагностику, лечение и индивидуальное лечение.

Преобразовательным инструментом в медицине сна стало обучение основам махинации (ML), предлагающее новаторские решения для диагностики, лечения и понимания расстройств сна. В настоящем обзоре рассматривается нынешнее состояние и будущий потенциал применения ML в этой области. Methods Мы провели всеобъемлющий обзор литературы с уделением особого внимания ключевым достижениям в области технологий ML, применяемых для лечения сна. Были изучены, в частности, такие конкретные области, как классификация стадий сна, оптимизация лечения апноэ и бессонницы сна, а также внедрение износостойкой технологии

Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges.

The expanding digitalization of routine diagnostic histological slides holds a potential to apply artificial intelligence (AI) to pathology, including bone marrow (BM) histology. In this perspective, we describe potential tasks in diagnostics that can be supported, investigations that can be guided, and questions that can be answered by the future application of AI on whole-slide images of BM biopsies. These range from characterization of cell lineages and quantification of cells and stromal structures to disease prediction. First glimpses show an exciting potential to detect subtle phenotypic

Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.

BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can lead to cardiovascular-related unplanned readmissions.ObjectiveWe aimed to develop a machine learning (ML) model to predict 90-day unplanned readmissions for major adverse cardiovascular events (MACE) in hospitalized patients with blood cancers.DesignA retrospective population-based cohort study.MethodsWe analyzed patients aged ≥18 with blood cancers (leukemia, lymphoma, myeloma) using the Nationwide Readmissions Database. MACE included acute myo

Applications of Neural Network-Based Plan-Cancer Method for Primary Diagnosis of Mesothelioma Cancer.

"Malignant mesothelioma (MM)" is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is "malignant mesothelioma." Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination "epithelial/mesenchymal appearance of MM," however, makes a definite diagnosi

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… Predicting cancer using supervised machine learning: mesothelioma. Technology and Health Care . 2021;29(1):45–58. doi: 10.3233/THC-202237. 8. Alam T. M., Shaukat K., Mahboob H., et al. … Suitable supervised machine learning techniques for malignant mesothelioma diagnosis. 2018 11th Biomedical Engineering International Conference (BMEiCON); 2018, November; Chiang Mai, Thailand. pp. 1–5. 17. Şentürk Z. K., Hammer N. …