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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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… The group designed a decision-tree model that used CD10 (50% cutoff), MUM1 (5%), and FOXP1 (80%) without prior biological assumptions and achieved a kappa of 0.83 with strong concordance to gene expression profiling (GEP) of the cases studied [ 20 ]. … Their work spans across applied neural networks, decision trees, and logistic regression across gene expression and immunohistochemistry datasets to identify prognostic markers such as PD-L1, IKAROS, and Caspase-8 [ 77 , 78 , 79 , 80 ]. …

Established risk prediction models for the incidence of a low lean tissue index in patients with peritoneal dialysis.

ObjectiveThe objective of this study is to investigate the incidence of low lean tissue index (LTI) and the risk factors for low LTI in peritoneal dialysis (PD) patients, including to establish risk prediction models.MethodsA total of 104 PD patients were enrolled from October 2019 to 2021. LTI was measured by bioimpedance spectroscopy. Multivariate logistic regression and machine learning were used to analyze the risk factors for low LTI in PD patients. Kaplan-Meier analysis was used to analyze the survival rate of patients with low LTI.ResultsThe interleukin-6 (IL-6) level, red cell distribu

Использование схемы действий для адаптации с целью адаптации основанных на фактических данных мер по охране сексуального здоровья для молодых людей, пострадавших от травм.

Детей, сталкивающихся с проблемами в домашнем хозяйстве (т.е. потребление психоактивных веществ родителями, тюремное заключение и психические заболевания), относятся к группам, наиболее подверженным сексуальному риску в подростковом возрасте. Такое поведение связано с целым рядом негативных последствий на более позднем этапе жизни, включая злоупотребление наркотическими веществами, низкий уровень образования и тюремное заключение. Одной из стратегий удовлетворения потребностей этой группы населения является адаптация основанных на фактических данных мероприятий (ИВР).МЕТОДЫ: В настоящем исслед

AI-driven prognostics in pediatric bone marrow transplantation: a CAD approach with Bayesian and PSO optimization.

Bone marrow transplantation (BMT) is a critical treatment for various hematological diseases in children, offering a potential cure and significantly improving patient outcomes. However, the complexity of matching donors and recipients and predicting post-transplant complications presents significant challenges. In this context, machine learning (ML) and artificial intelligence (AI) serve essential functions in enhancing the analytical processes associated with BMT. This study introduces a novel Computer-Aided Diagnosis (CAD) framework that analyzes critical factors such as genetic compatibili

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… Several ML algorithms such as logistic regression, support vector machines, and boosted decision trees (BDTs) were applied and contrasted. The AUC was used to evaluate these methods. … Multilayer Perceptron (MLP) learns complex patterns, k-Nearest Neighbors (KNN) relies on proximity for classification, and Decision Tree (DT) provides interpretable decision-making. …

Машинное обучение для интерпретации серума протеина и иммунофиксации электрофореза в мультипликационной миеломе: обзор скопирования.

Фон: Для диагностики множественной миэломы (ММ) необходимы электрофорез сухого белка (СФЭ) и электрофорез иммунофиксации (ИФЕ). Методы машинного обучения (ML) стали потенциальным подходом к автоматизации процесса интерпретации электрофореза в целях повышения эффективности диагностики и увеличения времени перехода. В ходе этого аналитического обзора были выявлены нынешние виды применения ML в диагностике и классификации MM с уделением особого внимания автоматизированному толкованию ОИН и IFE. Методы: базы данных PubMed, Web of Science и Scopus были подвергнуты обыску в соответствии с руководящи

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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… In clinical routine, they provide the means for pragmatic decision support as they essentially function as checklists or simple decision trees that can be memorized without assigning complex weights to their individual variables. … Abbreviations: DT, decision tree; KNN, k nearest neighbour; LR, logistic regression; NB, naïve Bayes; RF, random forest; ROCAUC, receiver‐operating‐characteristic area under the curve; SVM, support vector machine; XGB, extreme gradient boosting. …

Routine Laboratory-Based Machine Learning for Discriminating Multiple Myeloma from Clinical Mimickers: Development and Internal Validation of a Diagnostic Prediction Model.

Background: Multiple myeloma (MM) diagnosis requires specialised testing that is not universally available, contributing to diagnostic delays. The aim of this study was to develop and internally validate statistical and machine learning (ML) models that discriminate MM from clinically similar conditions using exclusively routine laboratory parameters. Methods: This retrospective diagnostic prediction model study included 422 consecutive patients referred to hematology for suspected MM at a tertiary care center (2017-2025). Fifteen routine demographic and laboratory variables were used to train

Несоответствие между готовностью к вакцинации и фактическим состоянием вакцинации SARS-COV-2 у людей с множественным склерозом: продольное исследование.

Фон/цели: Заражение острым острым респираторным синдромом коронавируса типа 2 (SARS-COV-2) создает значительный риск для здоровья, особенно для лиц с хроническими заболеваниями. Множественный склероз (MS) является наиболее распространенным хроническим неврологическим расстройством с иммунной помощью, и вакцинация имеет важное значение для его лечения. Цель этого исследования заключалась в сопоставлении заявленной готовности к вакцинации против SARS-COV-2 с фактическим состоянием вакцинации людей с MS (PwMS) и выявлении факторов, объясняющих это несоответствие. Методы: В продольном двухцентрово

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis.

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated b

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… RSF is a ML algorithm based on decision tree ( Yosefian et al., 2015 ), which has good prediction accuracy without over-fitting, and it is suitable for survival analysis of many diseases ( Farhadian et al., 2021 ; Roshanaei et al., 2022 ). … Application of random forest survival models to increase generalizability of decision trees: a case study in acute myocardial infarction . Comput. Math. Methods Med. 2015 , 576413 . 10.1155/2015/576413 26858773 PMC4698527 Zhang L. Huang T. Xu F. Li S. Zheng S. Lyu J. ( 2022 ). …

Advances in Leukemia detection and classification: A Systematic review of AI and image processing techniques.

BACKGROUND: Leukemia, a heterogeneous group of blood cancers, poses significant challenges to global health due to its complexity, diverse risk factors, and variable outcomes. Accurate and early diagnosis is critical but remains a significant hurdle, particularly in low-resource settings. Recent advancements in artificial intelligence (AI) and image processing offer transformative solutions to improve leukemia detection and classification, addressing limitations in traditional diagnostic methods. METHODS: This study systematically reviewed over 25,000 scientific articles sourced from Scopus,

TECH-VER: A Verification Checklist to Reduce Errors in Models and Improve Their Credibility.

BackgroundIn health economic literature, checklists or best practice recommendations on model validation/credibility always declare verification of the programmed model as a fundamental step, such as 'is the model implemented correctly and does the implementation accurately represent the conceptual model?' However, to date, little operational guidance for the model verification process has been given. In this study, we aimed to create an operational checklist for model users or reviewers to verify the technical implementation of health economic decision analytical models and document their ver

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)

Long-Term Outcome of Patients With a Hematologic Malignancy and Multiple Organ Failure Admitted at the Intensive Care.

ObjectivesHistorically, patients with a hematologic malignancy have one of the highest mortality rates among cancer patients admitted to the ICU. Therefore, physicians are often reluctant to admit these patients to the ICU. The aim of our study was to examine the survival of patients who have a hematologic malignancy and multiple organ failure admitted to the ICU.DesignThis retrospective cohort study, part of the HEMA-ICU study group, was designed to study the survival of patients with a hematologic malignancy and organ failure after admission to the ICU. Patients were followed for at least 1

Outcomes and quality of life after major bile duct injury in long-term follow-up.

IntroductionRecently new standards for reporting outcomes of bile duct injury (BDI) have been proposed. It is unclear how these treatment outcomes are reflected in quality of life (QOL). The aim of this study was to report outcomes and QOL after repair of major BDI and compare repairs by hepatobiliary surgeon to repairs by non-hepatobiliary surgeons.MethodsThis was a retrospective study of patients treated for major (Strasberg E-type) BDI after cholecystectomy at a tertiary hepatobiliary center. Outcomes were assessed using Cho-Strasberg proposed standards. QOL was assessed using Short Form He

Integrating Academic and Community Cancer Care and Research through Multidisciplinary Oncology Pathways for Value-Based Care: A Review and the City of Hope Experience.

As the US transitions from volume- to value-based cancer care, many cancer centers and community groups have joined to share resources to deliver measurable, high-quality cancer care and clinical research with the associated high patient satisfaction, provider satisfaction, and practice health at optimal costs that are the hallmarks of value-based care. Multidisciplinary oncology care pathways are essential components of value-based care and their payment metrics. Oncology pathways are evidence-based, standardized but personalizable care plans to guide cancer care. Pathways have been developed

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… This two-way integration will continue to expand by matching additional data elements captured in EPIC to the pathway decision trees to more efficiently guide pathway choices. … Community disease and regional leaders are closely involved with academic-based colleagues in building the initial decision tree for breast cancer. Next up in the queue are GI and GU multispecialty decision tree builds. …

Positron Emission Tomography-Derived Radiomics and Artificial Intelligence in Multiple Myeloma: State-of-the-Art.

Multiple myeloma (MM) is a heterogeneous neoplasm accounting for the second most prevalent hematologic disorder. The identification of noninvasive, valuable biomarkers is of utmost importance for the best patient treatment selection, especially in heterogeneous diseases like MM. Despite molecular imaging with positron emission tomography (PET) has achieved a primary role in the characterization of MM, it is not free from shortcomings. In recent years, radiomics and artificial intelligence (AI), which includes machine learning (ML) and deep learning (DL) algorithms, have played an important rol

A smartphone app to improve the safety of patients undergoing treatment with oral antineoplastic agents: 4 years of experience in a university hospital.

ObjectiveThis study aims to analyze the impact of the eOncosalud app on the management and follow-up of adverse effects (AE) in patients receiving oral antineoplastic agents.Material and methodsWe performed an observational, prospective study of cancer outpatients treated with oral antineoplastic agents (OAA), monitored by the eOncosalud app between August 2017 and October 2021. Safety variables were collected from eOncosalud: the number of AE; severity of the AE according to CTCAE, version 4.03; timelapse from app installation to first recorded AE; automatic recommendations issued; and the pa

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

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… Decision tree learning is a supervised model that is commonly used in clinical practice for decision-making processes. … Random Forest (RF) is an extension of decision tree learning that combines multiple randomly generated decision trees to improve decision making. Given the complexity of biology, data scientists usually prefer RF to decision tree ( Breiman, 2001 ; Cui et al., 2022b ). …

Research on predicting the progression of multiple myeloma treated with bortezomib based on multimodal ensemble learning.

BackgroundMultiple myeloma (MM) is a malignancy characterized by abnormal plasma cell proliferation. While bortezomib has improved outcomes, significant individual variability persists. Accurate early prediction of patient progression is crucial for optimizing therapeutic intensity and improving long-term survival. Developing an automated, multimodal prediction model can provide clinicians with a robust tool for personalized prognosis, thereby reducing the burden of ineffective treatments on patients.MethodsWe enrolled 207 newly diagnosed MM (NDMM) patients treated with bortezomib. Based on 2-

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… Trees. … Random Forest improves model accuracy and robustness by building multiple decision trees and aggregating their results. KNN is simple and intuitive, suitable for small datasets or as a baseline model. …

Discovering KYNU as a feature gene in hidradenitis suppurativa.

BackgroundHidradenitis suppurativa (HS) is a chronic auto-inflammatory skin condition characterized by nodules, abscesses, and fistulae in skin folds. The underlying pathogenesis of HS remains unclear, and effective therapeutic drugs are limited.MethodsWe acquired mRNA expression profiles from the Gene Expression Omnibus (GEO) database and conducted differential expression analysis between control and HS samples using R software. Four machine learning algorithms (SVM, RF, ANN, and lasso) and WCGNA were utilized to identify feature genes. GO, KEGG, Metascape, and GSVA were utilized for the enri

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… (a) The random forest model displayed the influence of the number of decision trees on the error rate. (b) The importance of the HS-related feature genes based on the RF model. (c)Establishment of a nomogram model. (d) Results of ANN visualization. …