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Viscoelastic point-of-care testing to assist with the diagnosis, management and monitoring of haemostasis: a systematic review and cost-effectiveness analysis.

BACKGROUND: Patients with substantive bleeding usually require transfusion and/or (re-)operation. Red blood cell (RBC) transfusion is independently associated with a greater risk of infection, morbidity, increased hospital stay and mortality. ROTEM (ROTEM® Delta, TEM International GmbH, Munich, Germany; www.rotem.de), TEG (TEG® 5000 analyser, Haemonetics Corporation, Niles, IL, USA; www.haemonetics.com) and Sonoclot (Sonoclot® coagulation and platelet function analyser, Sienco Inc., Arvada, CO) are point-of-care viscoelastic (VE) devices that use thromboelastometry to test for haemostasis in w

Sentinel lymph node status in vulval cancer: systematic reviews of test accuracy and decision-analytic model-based economic evaluation.

BACKGROUND: Vulval cancer causes 3-5% of all gynaecological malignancies and requires surgical removal and inguinofemoral lymphadenectomy (IFL). Complications affect > 50% of patients, including groin wound infection, lymphoedema and cellulitis. A sentinel lymph node (SLN) is the first groin node with the highest probability of malignancy. SLN biopsy would be useful if it could accurately identify patients in whom cancer has spread to the groin, without removing all groin nodes. SLNs can be identified by isosulfan blue dye and/or technetium-99 ((99m)Tc) radioactive tracer during lymphoscintigr

Systematic review and validation of prediction rules for identifying children with serious infections in emergency departments and urgent-access primary care.

BACKGROUND: Although the vast majority of children with acute infections are managed at home, this is one of the most common problems encountered in children attending emergency departments (EDs) and primary care. Distinguishing children with serious infection from those with minor or self-limiting infection is difficult. This can result in misdiagnosis of children with serious infections, which results in a poorer health outcome, or a tendency to refer or admit children as a precaution; thus, inappropriately utilising secondary-care resources. OBJECTIVES: We systematically identified clinica

[Cluster Analysis and Ablation Success Rate in Atrial Fibrillation Patients Undergoing Catheter Ablation].

OBJECTIVE: Atrial fibrillation (AF) is a disease of high heterogeneity, and the association between AF phenotypes and the outcome of different catheter ablation strategies remains unclear. Conventional classification of AF (e.g. according to duration, atrial size, and thromboembolism risk) fails to provide reference for the optimal stratification of the prognostic risks or to guide individualized treatment plan. In recent years, research on machine learning has found that cluster analysis, an unsupervised data-driven approach, can uncover the intrinsic structure of data and identify clusters o

Testing strategies for Lynch syndrome in people with endometrial cancer: systematic reviews and economic evaluation.

BACKGROUND: Lynch syndrome is an inherited genetic condition that is associated with an increased risk of certain cancers. The National Institute for Health and Care Excellence has recommended that people with colorectal cancer are tested for Lynch syndrome. Routine testing for Lynch syndrome among people with endometrial cancer is not currently conducted. OBJECTIVES: To systematically review the evidence on the test accuracy of immunohistochemistry- and microsatellite instability-based strategies to detect Lynch syndrome among people who have endometrial cancer, and the clinical effectivenes

Palivizumab for immunoprophylaxis of respiratory syncytial virus (RSV) bronchiolitis in high-risk infants and young children: a systematic review and additional economic modelling of subgroup analyses.

BACKGROUND: Respiratory syncytial virus (RSV) is a seasonal infectious disease, with epidemics occurring annually from October to March in the UK. It is a very common infection in infants and young children and can lead to hospitalisation, particularly in those who are premature or who have chronic lung disease (CLD) or congenital heart disease (CHD). Palivizumab (Synagis®, MedImmune) is a monoclonal antibody designed to provide passive immunity against RSV and thereby prevent or reduce the severity of RSV infection. It is licensed for the prevention of serious lower respiratory tract infectio

Detecting lesion-specific ischemia in patients with coronary artery disease with computed tomography fractional flow reserve measured at different sites.

OBJECTIVES: Whether a stenosis can cause hemodynamic lesion-specific ischemia is critical for the treatment decision in patients with coronary artery disease (CAD). Based on coronary computed tomography angiography (CCTA), CT fractional flow reserve (FFRCT) can be used to assess lesion-specific ischemia. The selection of an appropriate site along the coronary artery tree is vital for measuring FFRCT. However the optimal site to measure FFRCT for a target stenosis remains to be adequately determined. The purpose of this study was to determine the optimal site to measure FFRCT for a target lesio

[Cost-effectiveness analysis of schizophrenic patient care settings: impact of an atypical antipsychotic under long-acting injection formulation].

UNLABELLED: Schizophrenia is a disease affecting the young adults and amounts to approximately 300,000 people in France. The French public psychiatric sector takes care of approximately 150,000 adults schizophrenics: 50% benefit from ambulatory care, 50% are in partial or full-time hospitalization care. Schizophrenia represents the first diagnosis that psychiatric sectors take in charge. The costs associated with schizophrenia, mainly hospital costs, are important and were estimated at 2% of the total medical costs in France. In the French social welfare system, the social costs (pensions, all

Predicting Colonic Neoplasia Surgical Complications: A Machine Learning Approach.

BACKGROUND: A range of statistical approaches have been used to help predict outcomes associated with colectomy. The multifactorial nature of complications suggests that machine learning algorithms may be more accurate in determining postoperative outcomes by detecting nonlinear associations, which are not readily measured by traditional statistics. OBJECTIVE: The aim of this study was to investigate the utility of machine learning algorithms to predict complications in patients undergoing colectomy for colonic neoplasia. DESIGN: Retrospective analysis using decision tree, random forest, and

Interferon-Gamma Release Assay Testing for Latent Tuberculosis Infection: A Health Technology Assessment.

BACKGROUND: Many people infected with the Mycobacterium tuberculosis complex (the bacteria that cause tuberculosis [TB]) have an inactive stage of infection known as latent tuberculosis infection (LTBI). A person with LTBI is at risk of developing active TB. Screening for, and treating people with, LTBI is an important part of preventing adverse health outcomes, reducing the risk of reactivation and the further spread of tuberculosis in a community. We conducted a health technology assessment of interferon-gamma release assay (IGRA) for the detection of LTBI, compared to the standard tuberculi

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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… The decision tree algorithm uses a tree structure and uses layers of inference to achieve the final classification. | ■ Decision trees are easy to understand and interpret and can be analyzed visually ■ Can handle both nominal and numeric data ■ More suitable for handling samples with missing attributes … Two methods, a neural network and gradient boosting decision tree, were used to establish models by age group. …

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

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 techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web o

Усиленная диагностика заболевания множественным склерозом на основе подхода, основанного на ансамблях

Множественный склероз (MS) — болезнь, наносящая удар по центральной нервной системе. Согласно самым последним статистическим данным MS Atlas 2.8 million люди во всем мире диагностировали MS. Недавно в рамках исследований стали изучаться методы машинного обучения для прогнозирования НР с использованием различных данных. Целью настоящего документа является разработка комплексного подхода к диагностике MS с использованием профилей выражения генов при одновременном решении проблемы классового дисбаланса, связанной с данными. Предлагается иерархический комплексный подход с использованием методов го

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… The Random Forest (RF) algorithm is an extension of decision trees [ 45 ]. Decision trees work by constructing a tree structure that works on a set of conditions. Each internal node carries a test attribute, and the branches have conclusions for this test. Every leaf node represents a class label. … A weak point of decision trees is their poor accuracy; this is due to the fact that they fragment datasets and prefer majority classes. …

Методы машинного обучения для классификации множественного склероза и болезни Альцгеймера с использованием геномных данных.

Сложные заболевания создают трудности в прогнозировании из-за их многофакторного и полигенного характера. В этом исследовании используется машинное обучение (ML) для анализа геномных данных из британского биобанка с целью прогнозирования геномной предрасположенности к таким сложным болезням, как множественный склероз (MS) и болезнь Альцгеймера (AD). Мы проверили логистическую регрессию (LR), методы ансамбля деревьев и модели глубокого обучения с этой целью. LR продемонстрировал замечательную стабильность в различных поднаборах данных, превосходя глубинные подходы к обучению, которые продемонст

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… GB: Gradient-Boosted Decision Trees; ET: Extremely Randomized Trees; RF: Random Forest; LR: Logistic Regression; FFN: Feedforward Neural Networks; CNN: Convolutional Neural Networks. … GB: Gradient-Boosted Decision Trees; ET: Extremely Randomized Trees; RF: Random Forest; LR: Logistic Regression; FFN: Feedforward Neural Networks; CNN: Convolutional Neural Networks. …

Стратегия Монте-Карло по планированию последующей медицинской деятельности: пример множественных данных о миеломе.

Ключевое значение для индивидуального лечения раковых заболеваний имеет разработка стратегий последующей деятельности в интересах конкретных пациентов. Весьма полезными были бы инструменты оказания помощи врачам в принятии решений о лечении и планировании последующих мероприятий на основе предпочтений пациентов и медицинских данных. Эти инструменты должны включать реалистичные модели развития заболеваний в рамках лечения, многоцелевую оптимизацию стратегий лечения и эффективные алгоритмы для индивидуализации последующих мер с учетом истории пациента. Мы предлагаем модель эволюции рака с исполь

Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.

Artificial Intelligence and machine learning are increasingly used to interrogate complex biological data. This systematic review evaluates their application to multi-omics for the molecular characterization of hematological malignancies, an area with unmet clinical need. We searched PubMed, Embase, Institute of Electrical and Electronics Engineers Xplore, and Web of Science from January 2015 to December 2024. Two reviewers screened records, extracted data, and used a modified appraisal emphasizing explainability, performance, reproducibility, and ethics. From 2847 records, 89 studies met incl

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… Decision trees and Logistic Regression are shown to be the most intrinsically explainable, probably due to their simplicity and built-in parameterization. RFs (a stochastic extension of decision trees) are the most explained by post hoc evaluation. … Whilst this is a strong performance, these same approaches showed the lowest rates of explainability implementation at only 33%, compared to 100% for decision trees. …

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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… In the case of CoForest, multiple decision trees work together to classify unlabeled data based on the ensemble diversity. Each tree in the ensemble is individually trained on a portion of the labeled data. … Democratic Co-Learning used a decision tree classifier and favored label correction by applying a strong confidence constraint of 0.95 and utilized squared error weighting (with exponent = 2) in an attempt to balance label noise. …

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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… One approach that stands out from the rest is that which makes use of decision trees. Specifically, a decision tree is a hierarchical structure composed of nodes and branches that join them. … Of the different algorithms that use decision trees, Random Forest stands out above all others [139] . It is a meta-algorithm that uses a set of decision trees (ensemble) to build a solution to the problem that it intends to solve through bagging and boosting approaches. …

Explainable artificial intelligence for precision medicine in acute myeloid leukemia.

Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of "black box" in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leuk