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Using 5 consecutive years of NICE guidance to describe the characteristics and influencing factors on the economic evaluation of orphan oncology drugs.

OBJECTIVE: Orphan oncology drugs used in this article were defined by the type of disease treated by drugs, as drugs used to treat rare diseases with a prevalence of ≤ 500 per million people per year. In this article, our concern was to explore focus on the economic evaluation of the National Institute for Health and Care Excellence (NICE), when orphan oncology drugs were appraised for reimbursement, and provide advice and suggestions to decision-makers. METHODS: A retrospective study was used in this study. Thirty guidance were gathered as our subject by NICE from 2016 to 2020, excluded drug

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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… Random forest is a machine learning technique that uses an ensemble of individual decision trees, with each “tree” outputting a binary output, with the most common output being the consensus decision. …

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

Выявляется высокий уровень использования медицинских услуг после операции на шейном позвоночнике с использованием комплексных методов прогнозирования.

Расходы на медицинское обслуживание пациентов с хроническими заболеваниями позвоночника и пациентов, проходящих хирургическую операцию, продолжают расти заметными темпами. Таким образом, выявление пациентов, подверженных риску в плане более широкого использования послеоперационного медицинского обслуживания, имеет решающее значение для оптимизации результатов медицинского обслуживания и распределения ресурсов. Цель нашего исследования заключается в использовании целого ряда предоперабельных характеристик пациентов, включая всеобъемлющую медицинскую, хирургическую и социальную историю, для разр

Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives.

Medical biosensors have set the basis of medical diagnostics, and Artificial Intelligence (AI) has boosted diagnostics to a great extent. However, false results are evident in every method, so it is crucial to identify the reasons behind a possible false result in order to control its occurrence. This is the first critical state-of-the-art review article to discuss all the commonly used biosensor types and the reasons that can give rise to potential false results. Furthermore, AI is discussed in parallel with biosensors and their misdiagnoses, and again some reasons for possible false results

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… Moreover, various machine learning algorithms—including support vector machines, principal components analysis, hierarchical cluster analysis, artificial neural networks and decision trees—have been used in AI biosensors for efficacious and precise decision making; SVMs are supervised ML techniques designed …

Logic programming reveals alteration of key transcription factors in multiple myeloma.

Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected r

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

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… RF models utilize a tree-based methodology that constructs multiple uncorrelated decision trees through random sampling of data. The ultimate prediction is derived by averaging the outputs from the decision trees. …

Создание мультимодальной модели прогнозирования для пациентов с множественной миеломой на основе радиомики и клинических особенностей: ретроспективное кохартное исследование

ЦЕЛЬ: Мы стремились к разработке системы прогнозирования выживания, объединяющей радиомикулы и клинические характеристики недавно диагностированной миеломы (ММ) и сопоставлению эффективности различных наборов характеристик и алгоритмов в раннем прогнозировании без прогрессии выживания (ПФС). МАТЕРИАЛЫ И МЕТОДЫ: Это исследование ретроспективно включало 300 Пациенты ММ в период с июня 2022 и июнь 2024, с их фоновой позитронной томографией, вычисленной томографией и магнитно-резонансными радиомагнитными характеристиками и клиническими переменными. После построения оценки рисков на основе радиомик

Effects of bisphosphonates after denosumab discontinuation and treatment effect heterogeneity using causal machine learning.

Discontinuation of denosumab is associated with a rebound increase in osteoporotic fracture (OF) risk, and bisphosphonates (BPs) are commonly recommended as sequential therapy to mitigate this risk. However, their real-world effectiveness-and whether certain patients benefit more than others-remains unclear. To address this question, we employed a causal machine learning approach capable of estimating heterogeneous treatment effects. We conducted a nationwide retrospective cohort study using Korean health insurance claims data and applied the causal survival forest model to evaluate the effect

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… The CSF, akin to a surviving forest, iteratively divides the sample into progressively smaller subgroups, generating a sequence of decision trees that result in subgroups. …

Virtual Screening of FDA-Approved Compounds: Exploring New Alternatives for HIV Treatment.

Human immunodeficiency virus (HIV) infection remains a significant public health challenge, particularly because of the emergence of drug-resistant strains against the drugs currently used in highly active antiretroviral therapy (HAART). The ongoing search for new molecules with therapeutic potential remains crucial. In this study, a virtual screening approach was employed to identify novel candidates with therapeutic potential for HIV. High-throughput screening (HTS) data were used to train and validate quantitative structure-activity relationship (QSAR) models, which were subsequently applie

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… Models based on different algorithms, including decision trees, logistic regression, and gradient boostingamong others, were selected. The F1 scores ranged from 66.11% to 90.18%, while accuracy values ranged from 56.16% to 90.56%. All metrics were calculated using a balanced data set. … PubChem AID | descriptors | algorithm | accuracy | recall | precision | ROC_AUC | F1 651571 | descriptors | decision tree | 56.16 | 86.84 | 55 | 53.5 | 67.35 565 | Mol2vec | logistic regression | 73.76 | 73.97 | 73.97 | 79.45 | 73.97 704 | Morgan-2048 | random forest | 87.79 | 90.45 | 86.54 | 94.38 | …

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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… In a comprehensive review of this type of learning, in addition to tasks, supervised learning techniques such as support vector machine (SVMs), neural network, naive Bayes, logistic regression, memory-based learning, decision trees, random forests, bagged trees, boosted trees, and boosted stumps should … Induction of Decision Trees. Mach Learn. 1 (1), 81–106. 10.1007/bf00116251 Rich J. N. (2016). Cancer Stem Cells: Understanding Tumor Hierarchy and Heterogeneity. Medicine (Baltimore) 95 (Suppl. 1), S2–S7. 10.1097/MD.0000000000004764 Roffe L., Schmidt K., Ernst E. (2005). …

The Cost-Effectiveness Analysis of Transplant-Ineligible Myeloma Patients with Bortezomib plus Thalidomide plus Dexamethasone (VTD) or Bortezomib plus Melphalan plus Prednisolone (VMP) Treatment in Southern Taiwan.

BackgroundThis study aimed to evaluate the cost-effectiveness of treating transplant-ineligible myeloma patients with either a bortezomib plus thalidomide plus dexamethasone (VTD) or a bortezomib plus melphalan plus prednisolone (VMP) treatment in Taiwan.MethodsNewly diagnosed, transplant-ineligible myeloma patients with VTD or VMP therapy were enrolled from two medical centers in southern Taiwan. Quality-adjusted life years (QALYs) were used as the measurement unit of the effectiveness evaluation, and the incremental cost-effectiveness ratio (ICER) was used for comparison between the two grou

Clinical and Genomic Epidemiology of Carbapenem-Nonsusceptible Citrobacter spp. at a Tertiary Health Care Center over 2 Decades.

Carbapenem-nonsusceptible Citrobacter spp. (CNSC) are increasingly recognized as health care-associated pathogens. Information regarding their clinical epidemiology, genetic diversity, and mechanisms of carbapenem resistance is lacking. We examined microbiology records of adult patients at the University of Pittsburgh Medical Center (UMPC) Presbyterian Hospital (PUH) from 2000 to 2018 for CNSC, as defined by ertapenem nonsusceptibility. Over this time frame, the proportion of CNSC increased from 4% to 10% (P = 0.03), as did daily defined carbapenem doses/1,000 patient days (6.52 to 34.5; R2 =

Identifying Educational Needs and Practice Gaps of European Hematologists and Hematology Nurses in the Treatment and Management of Multiple Myeloma.

This needs-assessment aimed to identify clinical challenges faced by hematologists and hematology nurses in the diagnosis, treatment, and management of multiple myeloma, as well as contextual barriers hindering optimal care of patients with multiple myeloma. This manuscript focuses on key findings in relation to decision-making regarding new treatment options. A mixed methods study consisting of qualitative (from semistructured interviews) and quantitative data (from an online survey) was conducted in 8 European countries among hematologists and hematology nurses. The triangulated data led to

Determinants of COVID-19 Mortality in Patients With Cancer From a Community Oncology Practice in Brazil.

PurposeThe COVID-19 pandemic remains a public health emergency of global concern. Determinants of mortality in the general population are now clear, but specific data on patients with cancer remain limited, particularly in Latin America.Materials and methodsA longitudinal multicenter cohort study of patients with cancer and confirmed COVID-19 from Oncoclínicas community oncology practice in Brazil was conducted. The primary end point was all-cause mortality after isolation of the SARS-CoV-2 by Real-Time Polymerase Chain Reaction (RT-PCR) in patients initially diagnosed in an outpatient environ

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… (B) Final results of the Recursive Partitioning Multivariable Model with decision trees, which classifies patients in different death risk populations based on dichotomous independent variables. ICU, intensive care unit. … The results of the recursive partitioning model are comparable with those of primary analysis with multivariable logistic regression, with the selection of similar variables to construct the decision tree displayed in Figure 2 B. …

Вытяжка из листа EGb 761* в качестве образца продукта по концепции процесса.

Часто недооценивается тот факт, что экстракты, получаемые с одного и того же растения, могут существенно различаться по их фитохимическому составу, а также по их фармакокинетическим и фармакодинамическим свойствам, которые лежат в основе их клинической эффективности и безопасности. Специальная экстрактная EGb 761- Это... one из наиболее изученных выдержек растений в мире. В настоящем обзоре, используя этот экстракт в качестве парадигмы, мы рассказываем о том, как климат, район сбора урожая, обработка растительного материала, процесс сушки, экстракционные растворители и детали последующих этапо

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

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… Traditional algorithms encompass classic methods such as support vector machines and decision trees, which are widely applied in cardiovascular omics modeling. … CTA, computed tomography angiography; PET/CT, positron emission tomography/computed tomography; CMR, cardiac magnetic resonance; LR, logistic regression; LASSO, least absolute shrinkage and selection operator; SVM, support vector machine; DT, decision tree; RF, random forest; XGBoost, eXtreme gradient …

Development and Validation of a Prediction Model for 1-Year Mortality in Patients With a Hematologic Malignancy Admitted to the ICU.

ObjectivesTo develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU.DesignA retrospective cohort study.SettingFive university hospitals in the Netherlands between 2002 and 2015.PatientsA total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h.InterventionsNone.Measurements and main resultsWe created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventila

Shotgun metagenome sequencing identification of a set of genes encoded by Actinomyces associated with medication-related osteonecrosis of the jaw.

Medication-related osteonecrosis of the jaw (MRONJ) is intractable and severely affects a patient's quality of life. Although many cases of MRONJ have been reported in the past decade, the disease pathophysiology is unclear and there are no evidence-based therapeutic strategies. MRONJ usually features bone inflammation and infection. Prior studies that explored the association between MRONJ and microbial infection used the culture-based approach, which is not applicable to hundreds of unculturable taxa in the human oral microbiome, or 16S ribosomal RNA gene sequencing, which does not provide q

Автоматизированная радиомасса для прогнозирования реакции терапии и минимальных остаточных заболеваний от базовой МРТ в множестве миеломы.

Это многоцентровое исследование снимков было направлено на создание и проверку автоматизированных моделей радиомикалов, прогнозирующих терапию (TR) и минимальную остаточную реакцию (MRD) в недавно диагностированном миеломе (MM) из базового МРТ. Ретроспективно, 118 Пациенты ММ из GMMG-HD7 судебное разбирательство (EudraCT: 2017-004768-37) с данными о TR и/или MRD после индуктивной терапии и базовой МРТ. Данные были разделены по центрам в комплект учебных материалов (центр 1-2; n = 79) и комплект испытаний (центр 3-10; n = 39). TR был классифицирован как очень хороший частичный ответ или лучше п