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• Integrated Clinical-Molecular Risk Stratification in Diffuse Large B-Cell Lymphoma: Machine Learning Survival Analysis.
  Background The clinical outcomes of diffuse large B-cell lymphoma (DLBCL) are highly heterogeneous. While clinical indices like the international prognostic index (IPI) are widely used, their predictive accuracy remains limited. The integration of molecular fe…
  europepmc 2026 置信度 0.80
• Development and validation of a multidimensional machine learning model for predicting delayed graft function in deceased donor kidney transplantation.
  Purpose Delayed graft function (DGF) remains a significant complication following deceased donor kidney transplantation. This study aimed to develop and validate a multidimensional machine learning model for predicting DGF by integrating clinical data, machine…
  europepmc 2026 置信度 0.80
• Development and Validation of a Machine Learning-Based Prediction Model for Atrial Fibrillation in Hospitalized Patients with Coronary Heart Disease and Diabetes Mellitus: A Retrospective Study
  Abstract Background Coronary heart disease (CHD) and diabetes mellitus (DM) are among the most prevalent cardiovascular and metabolic disorders worldwide. The coexistence of these conditions substantially increases the risk of adverse cardiovascular outcomes, …
  europepmc 2026 置信度 0.80
• Development and external validation of an online interpretable machine-learning model for predicting delirium risk in acute heart failure.
  ObjectiveDelirium is a frequent complication in acute heart failure and is associated with poor outcomes in the intensive care unit. Machine learning methods can leverage high-dimensional clinical data to support early risk stratification; however, models spec…
  europepmc 2026 置信度 0.80
• Pretreatment Prediction of Tumor Recurrence in Breast Cancer After Neoadjuvant Systemic Therapy Using Machine Learning With Clinical and CT Radiomics Features.
  This study aimed to develop machine learning models for predicting tumor recurrence in breast cancer before neoadjuvant systemic therapy (NST) by integrating clinical and radiomic features derived from pretreatment computed tomography (CT). We retrospectively …
  europepmc 2026 置信度 0.80
• Machine learning-based prediction of in-hospital deep vein thrombosis in patients with acute ischemic stroke: a multicenter study.
  Background Deep vein thrombosis (DVT) is a common complication of acute ischemic stroke (AIS) and may worsen clinical outcomes, yet reliable tools for early risk stratification remain limited. We aimed to develop and internally evaluate machine learning models…
  europepmc 2026 置信度 0.80
• Development and Clinical Utility of Machine Learning Models for Prediction of Same-Day Discharge in Outpatient Hip and Knee Replacement: A Prognostic Study.
  Background Hip and knee replacement are common procedures with an increasing focus on same-day surgery. However, capacity constraints limit the number of eligible patients actually being scheduled for same-day discharge, calling for further selection of those …
  europepmc 2026 置信度 0.80
• Machine learning for thalassemia detection: a critical review of diagnostic pathways and emerging technologies.
  Thalassemia remains a major diagnostic challenge because its detection requires the interpretation of multiple laboratory and molecular findings in different stages of care. Current diagnostic approaches are effective in clinical practice, but they are often d…
  europepmc 2026 置信度 0.80
• Development and Validation of an Interpretable Machine Learning Model for Predicting ICU-Acquired Weakness in Postoperative Patients.
  Background ICU-acquired weakness (ICU-AW) is a common and debilitating complication among critically ill patients, particularly those undergoing major surgery. Early identification of patients at high risk of ICU-AW may facilitate timely preventive strategies …
  europepmc 2026 置信度 0.80
• Predicting Cesarean Section Delivery in the United States Using Machine Learning: Population-Based Retrospective Study.
  Background Cesarean section (C-section) is the most common surgical procedure in the United States, yet its use varies widely across regions and institutions. Although clinical risk factors are central to delivery decisions, geographic context, health system c…
  europepmc 2026 置信度 0.80
• Machine Learning-Based Risk Stratification Tool for Hearing Loss in High-Risk Neonates.
  Objective This study aims to develop and evaluate a machine learning-based risk stratification tool for predicting hearing loss in high-risk neonates using clinical risk factors to support targeted early identification. Methods A total of 270 infants, 105 with…
  europepmc 2026 置信度 0.80
• Machine learning-based prediction model for predicting the impact of insulin resistance on the risk of ischemic cardiomyopathy.
  Background The triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio and triglyceride glucose-body mass (TyG-BMI) index are reliable indicators of insulin resistance (IR). This study investigated their association with ischemic cardiomyopathy (…
  europepmc 2026 置信度 0.80
• Performance of Machine Learning Models to Predict the Need for Computed Tomography Brain Scans From Clinical Features in Emergency Department Headache Patients
  Abstract Background There is no unifying diagnostic tool to guide neuroimaging decisions for unselected emergency department (ED) patients presenting with headache. This study aimed to compare the performance characteristics of various machine learning models …
  europepmc 2026 置信度 0.80
• Comparative analysis of machine learning techniques for cardiovascular disease prediction.
  Background and objectives Early and accurate prediction of cardiovascular disease (CVD) is fundamental for reducing morbidity and mortality. Machine learning (ML) algorithms provide a data-driven, actionable foundation to strengthen clinical decision-making an…
  europepmc 2026 置信度 0.80
• A network toxicology and machine learning approach to uncover the molecular machinery of bisphenol A-induced colorectal cancer.
  The carcinogenic relevance of environmental contaminant bisphenol A (BPA) to colorectal cancer (CRC) has gained growing attention, yet the molecular networks potentially linking BPA exposure to CRC remain incompletely characterized. This study integrated netwo…
  europepmc 2026 置信度 0.80
• Explainable Machine Learning for Predicting Deep Vein Thrombosis in Critically Ill Patients with COPD: Development and Multicenter External Validation.
  Background Deep vein thrombosis (DVT) is a frequent yet underrecognized complication in critically ill patients with chronic obstructive pulmonary disease (COPD). Existing risk assessment tools are not specifically tailored to this high-risk population. We aim…
  europepmc 2026 置信度 0.80
• Development of a Multimodal Machine Learning Model for Predicting Second Osteoporotic Vertebral Compression Fractures.
  Purpose Osteoporosis increases the probability of osteoporotic vertebral compression fractures (OVCF), and the resultant pain severely limits physical activity and increases mortality and morbidity. This study aimed to develop a high-precision multimodal predi…
  europepmc 2026 置信度 0.80
• Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank.
  Osteoporosis (OP) is often underdiagnosed, highlighting the need for tools that can both detect existing disease and predict future risk; large-scale plasma proteomics combined with explainable machine learning enables integrated diagnostic and prognostic mode…
  europepmc 2026 置信度 0.80
• Interpretable machine learning enables early identification of financial risk and resource optimization in colorectal cancer surgery.
  Background Colorectal cancer (CRC) imposes major clinical and economic pressure on health systems. Under diagnosis-related group (DRG) payment, fixed reimbursement benchmarks may not capture the heterogeneity of surgical oncology or the resources required by p…
  europepmc 2026 置信度 0.80
• What can Artificial Intelligence learn from medicine? Generative analogies and reliable machine learning systems.
  In the past few years, machine learning (ML) has been widely (and to an extent, successfully) implemented in medicine. However, uncertainties surrounding ML have made it difficult to establish the bases of its epistemic and methodological warrants. In the lite…
  europepmc 2026 置信度 0.80
• Primary Care Quality and Inappropriate Community Antibiotic Use: A Double Machine Learning Instrumental Variable Approach
  Inappropriate antibiotic use presents a major global health challenge, particularly in low-resource settings where access to quality care is limited but antibiotics remain relatively unrestricted. This study estimates the causal effect of frontline primary car…
  europepmc 2026 置信度 0.80
• Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.
  Machine learning (ML) is transforming cancer research and care by enabling analysis of complex, high-dimensional datasets spanning genomics, transcriptomics, proteomics, imaging, and clinical records. By improving risk stratification, accelerating detection an…
  europepmc 2026 置信度 0.80
• Naïve adaptive immune receptor repertoires in celiac disease assessed by machine learning; impact of the HLA-DQ2.5 allotype on the TCR repertoire.
  The adaptive immune receptor repertoire (AIRR) - the collection of an individual's B-cell and T-cell receptors (BCRs and TCRs, respectively) - encodes cumulative immune history and is shaped by both germline genetics and environmental exposures. Skewed reperto…
  europepmc 2026 置信度 0.80
• Applying a Pharmacometrics-Enabled Machine Learning Analysis to Predict 2-Month Culture Conversion Using Phase 2a Data in a Tuberculosis Clinical Trial.
  Phase 2a trials in tuberculosis patients traditionally assess early bactericidal activity over two weeks, often using the time-to-positivity biomarker, followed by a phase 2b study typically lasting 8-week with time-to-event of culture conversion as the endpoi…
  europepmc 2026 置信度 0.80
• LASSO-based nomogram and machine learning models for predicting 30-day nasogastric tube dependence after acute ischemic stroke.
  Background Nasogastric tube (NGT) feeding is a common method for providing enteral nutrition to patients with acute ischemic stroke (AIS) and dysphagia. However, prolonged NGT dependence contributes to adverse clinical consequences. Early identification of pat…
  europepmc 2026 置信度 0.80
• [Clinical and preprocedural factors influencing in vitro fertilization outcomes: a machine learning-based analysis].
  Infertility affects 15-20% of the reproductive-age population, and assisted reproductive technologies, particularly in vitro fertilization (IVF), play an increasingly important role in its management. IVF success is multifactorial and influenced by numerous cl…
  europepmc 2026 置信度 0.80
• Advances in the development of COPD screening models using respiratory oscillometry: An evolution from traditional regression to machine learning.
  Aim of the study Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, and early screening is critical for improving patient outcomes. This review aims to provide a comprehensive theoretical foundation and practi…
  europepmc 2026 置信度 0.80
• Predicting PD-L1+ CD8- status in NSCLC tissue from clinical indicators using machine learning.
  Background Programmed death-ligand 1 (PD-L1) expression and CD8-positive (CD8+) T-cell infiltration in tumor tissue are associated with prognosis in non-small cell lung cancer (NSCLC). However, the prognostic value of combined PD-L1/CD8 immune phenotyping in s…
  europepmc 2026 置信度 0.80
• Development and internal validation of a machine learning-based disease burden index for irritable bowel syndrome: a multicentre cross-sectional study.
  Background Irritable bowel syndrome (IBS) is one of the most common disorders of gut-brain interaction in gastroenterology clinics worldwide, becoming a significant public health burden. This study aims to provide an exploratory approach for comprehensive asse…
  europepmc 2026 置信度 0.80
• Labeling matters: A multicenter machine learning study on visual field progression in Glaucoma.
  Background To compare machine learning (ML) performance for detecting visual field (VF) progression across different labeling strategies using a large multicenter dataset. Methods In this multicenter retrospective study, VF data were collected from five tertia…
  europepmc 2026 置信度 0.80
• Machine Learning-Integrated Metabolomics for Precision Pharmacotherapy: Advances, Challenges, and Clinical Translation.
  Machine learning (ML) integrated with metabolomics has emerged as a promising strategy to advance precision pharmacotherapy, enabling data-driven prediction of drug response. This review provides an overview of commonly applied ML methodologies in metabolomics…
  europepmc 2026 置信度 0.80
• Identification of Glycolysis-Related Diagnostic Biomarkers for Amyotrophic Lateral Sclerosis Using Machine Learning.
  Background Glycometabolism has been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS), yet the precise molecular mechanisms underlying this association remain poorly understood. The identification of reliable biomarkers for ALS diagnosis re…
  europepmc 2026 置信度 0.80
• Clinical characteristics and machine learning-based prediction of postoperative enterocolitis in intestinal neuronal dysplasia: A 20-year retrospective study
  Abstract Purpose Intestinal neuronal dysplasia (IND) is a congenital enteric neuropathy characterized by abnormalities of the enteric nervous system, most commonly involving hyperplasia of the submucosal plexus and the presence of giant ganglia. This study aim…
  europepmc 2026 置信度 0.80
• External validation of a machine learning-based web application for personalized testing of objective functioning using the five-repetition sit-to-stand test.
  Purpose Simple generalized thresholds for assessing functional impairment in clinical testing are limited, as they fail to consider patient-specific properties such as age, body height, and body mass index. A previously developed machine learning-based model f…
  europepmc 2026 置信度 0.80
• Identification and clinical evaluation of diagnostic biomarkers for ischemic cardiomyopathy based on machine learning and transcriptomics.
  Background Ischemic cardiomyopathy (ICM) is a leading cause of heart failure, yet precise molecular tools for potential diagnosis remain limited. This study aims to identify and clinically evaluate novel diagnostic biomarkers for ICM by integrating comprehensi…
  europepmc 2026 置信度 0.80
• Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer.
  Objective To evaluate a machine learning (ML) model that integrates clinical data and 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG)-PET radiomic features for predicting RAS mutation status and prognosis in patients with colorectal cancer (CRC). Methods This retros…
  europepmc 2026 置信度 0.80
• Machine learning-based prediction model for postoperative blood transfusion in pediatric patients with Hirschsprung's disease.
  Background Children undergoing Hirschsprung's disease (HD) surgery are at risk of postoperative transfusion, which may cause complications and increase resource use. Current decisions are often empirical and lack individualized prediction tools. Methods We ret…
  europepmc 2026 置信度 0.80
• A spatiotemporally validated machine-learning score for venous thromboembolism risk after neurosurgery
  Abstract Background The Caprini model may overestimate postoperative venous thromboembolism (VTE) risk in neurosurgical patients, whereas existing machine-learning models often lack interpretability and robust external validation. We aimed to develop a concise…
  europepmc 2026 置信度 0.80
• Machine learning-based prediction of intraoperative blood transfusion in major surgery: exploiting clinical variables and systemic inflammatory indices.
  Background Intraoperative blood transfusion is common in major surgery. Predicting transfusion risk may improve perioperative management, optimize blood use, and enhance surgical planning. Machine Learning (ML) offers promising tools for individualized risk pr…
  europepmc 2026 置信度 0.80
• 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…
  europepmc 2026 置信度 0.80
• Machine Learning-Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: Multicenter Model Development Study.
  Background Maternal outcomes remain inequitable worldwide. Severe morbidity persists, and current risk assessment tools are largely arbitrary, focusing on biomedical factors while overlooking social determinants of health. There is a need for data-driven AI mo…
  europepmc 2026 置信度 0.80
• Development and validation of a machine learning-based model for predicting fall-related injury risk in hospitalized patients.
  Background Falls are among the most common adverse events in hospitalized patients, with about 30% leading to injury. We developed a machine learning model to predict which in-hospital fall events would lead to patient injury, thereby supporting post-fall risk…
  europepmc 2026 置信度 0.80
• Optimizing cardiovascular disease diagnosis through machine learning models integrating oxidized low-density lipoprotein and routine clinical indicators.
  Introduction To evaluate the diagnostic value of oxidized low-density lipoprotein cholesterol (oxLDL-C) for cardiovascular disease (CVD) and to construct a machine learning model integrating routine clinical indicators, thereby providing an efficient and econo…
  europepmc 2026 置信度 0.80
• [Construction of a genotype-phenotype database and machine learning models for pseudohypertrophic muscular dystrophy].
  Objectives To establish a genotype-phenotype database and machine learning models for pseudohypertrophic muscular dystrophy (PMD), and to explore genotype-phenotype correlations of the disease. Methods Clinical data of children with PMD admitted to Xiangya Hos…
  europepmc 2026 置信度 0.80
• Machine learning-based prediction of peripherally inserted central catheter-related thrombosis in hematological malignancies: development and validation of a dynamic model in a multicenter cohort.
  Purpose Peripherally inserted central catheter-related thrombosis (PICC-RT) is a common and serious complication in patients with hematological malignancies, leading to treatment interruptions and increased morbidity. Traditional risk assessment models are oft…
  europepmc 2026 置信度 0.80
• Application of a machine learning model integrating T cell subsets and clinical markers in 28-Day mortality prediction of sepsis patients.
  To develop a machine learning-based prediction model that integrating T cell subset data with clinical features to predict the 28-day mortality risk in sepsis patients. A retrospective cohort study was conducted using the MIMIC-IV database. Collected data incl…
  europepmc 2026 置信度 0.80
• Predicting Synchronous Liver Metastasis in Pancreatic Cancer Using CT Radiomics and Clinical Features: A Machine Learning Approach.
  Rationale and objectives To address the challenge of preoperative prediction of synchronous liver metastasis (LM) in pancreatic cancer (PC), we developed and validated machine learning models integrating clinical and computed tomography (CT) radiomics features…
  europepmc 2026 置信度 0.80
• Predicting burnout in radiology nurses: an interpretable machine learning model developed and externally validated in a multi-center study.
  Objective To develop and externally validate an interpretable machine learning model for predicting individual burnout risk among radiology nurses, and to identify modifiable, non-linear risk thresholds to guide precision prevention. Methods A two-center, two-…
  europepmc 2026 置信度 0.80
• Interpretable machine learning for presurgical differentiation of Hürthle cell carcinoma and adenoma: a SHAP-augmented approach.
  Background The preoperative differentiation between benign Hürthle cell adenoma (HCA) and malignant Hürthle cell carcinoma (HCC) remains clinically challenging. This study aimed to develop an interpretable machine learning framework to improve diagnostic accur…
  europepmc 2026 置信度 0.80
• Machine Learning Characterization of Readmissions After Chronic Subdural Hematoma Hospitalizations.
  Background and objectives Readmission after chronic or subacute subdural hematoma (cSDH/sSDH) hospitalization is common, yet clinical attention and trial design have focused primarily on surgical recurrence. The full spectrum of readmission events, their clini…
  europepmc 2026 置信度 0.80
• Association and Incremental Predictive Value of the Triglyceride- Glucose Index and Its Derivatives for Hypertension and Diabetes: A Machine Learning-Based Study
  Abstract Background The triglyceride–glucose (TyG) index and its derivatives (TyG–BMI, TyG–WC, and TyG–WHtR) have emerged as reliable markers. This study aims to evaluate their associations with hypertension and diabetes and determine their incremental predict…
  europepmc 2026 置信度 0.80
• Quantum machine learning in diabetes and metabolism: Applications and translational challenges.
  Background and objective Quantum machine learning is emerging as a promising extension of artificial intelligence, with potential advantages over classical approaches in handling complex biomedical data. This review aims to evaluate quantum machine learning ap…
  europepmc 2026 置信度 0.80
• Machine Learning-Based Risk Prediction of In-Hospital Mortality in Patients With Non-ST-Elevation Acute Coronary Syndrome at Various Stages of the Diagnostic Process: Observational Study.
  Background Despite advances in understanding and treating non-ST-elevation acute coronary syndrome (NSTE-ACS), patients continue to experience high rates of adverse outcomes, particularly those with non-ST-segment elevation myocardial infarction, which remains…
  europepmc 2026 置信度 0.80
• Dynamic Risk Assessment for the Development of Persistent Atrial Fibrillation Using Statistical and Machine Learning Approaches.
  Background Cardiac implantable electronic devices (CIEDs) frequently detect brief, often subclinical atrial fibrillation (AF), but their value for predicting progression to persistent AF remains uncertain. Statistical and machine learning (ML) approaches may e…
  europepmc 2026 置信度 0.80
• An Interpretable Machine Learning Framework with Clinical Nomogram for Predicting In-Hospital Mortality in Acute Ischemic Stroke Using High-Granularity Bedside Data.
  Background This multicenter study developed and validated an interpretable machine learning model integrating granular nursing and emergency department data collected within the first 24 hours to predict in-hospital mortality in acute ischemic stroke (AIS). Me…
  europepmc 2026 置信度 0.80
• Prediction of Postoperative Vomiting Within 24 Hours Using Machine Learning With Large Language Model-Enhanced Interpretability: Development and Validation Study.
  Background Postoperative nausea and vomiting are common complications after anesthesia. However, vomiting represents a clinically distinct and objectively measurable endpoint. Objective This study aimed to develop and internally validate predictive models for …
  europepmc 2026 置信度 0.80
• NutrIA: Development and Internal Validation of a Hybrid Clinical Decision Support System for Personalized Preventive Nutrition
  Background The growing burden of lifestyle-related chronic diseases has increased the need for clinically interpretable decision-support tools capable of integrating artificial intelligence with evidence-based preventive nutrition. Although machine learning ha…
  europepmc 2026 置信度 0.80
• Multimodal Ultrasound Combined with Machine Learning for Diagnosing Breast Lesions: Incremental Value Assessment of Ultrasound Super-Resolution Imaging
  Abstract Background. To integrate conventional ultrasound, contrast-enhanced ultrasound (CEUS), and ultrasound super-resolution imaging (US SRI) into an interpretable machine learning model to distinguish benign from malignant breast lesions. Methods. We retro…
  europepmc 2026 置信度 0.80
• Machine learning versus traditional regression models for predicting diabetic retinopathy screening adherence among community-dwelling older adults with diabetes.
  Background Diabetic retinopathy (DR) is a leading cause of preventable vision loss among patients with diabetes, yet screening adherence remains suboptimal. Existing studies have mainly focused on clinical or sociodemographic determinants, with limited evidenc…
  europepmc 2026 置信度 0.80
• Development and Validation of an Interpretable Machine Learning-Based Clinical Prediction Model for Short-Term Mortality in Intracerebral Hemorrhage With Thrombocytopenia: A Multicenter Study.
  Background Intracerebral hemorrhage (ICH) with thrombocytopenia is associated with poor outcomes, but early risk prediction tools for this subgroup are limited. We aimed to develop and externally validate an interpretable machine learning model for predicting …
  europepmc 2026 置信度 0.80
• Machine learning models identify prognostic factors in systemic lupus erythematosus patients with epstein-barr virus infection.
  To identify poor prognostic factors in Epstein-Barr virus (EBV)-positive systemic lupus erythematosus (SLE) using interpretable machine-learning (ML) models.
  pubmed Fang M, Huang M, Ge F, Hu L 等 2026 置信度 0.82
• Machine Learning-Based Prediction of Maternal Morbidity across Heterogeneous Populations in the United States using Sequential Modeling of the All of Us Dataset
  In this work, we demonstrate the unprecedented value of NIH’s “All of Us Research Program” (AoURP) dataset in studying maternal morbidity and building predictive machine learning (ML) models across heterogeneous populations in the United States. We developed r…
  europepmc 2026 置信度 0.80
• Identification of Erectile Dysfunction From Routine Blood Test Data: Development and Validation of a Machine Learning-Based Prediction Model.
  Background Erectile dysfunction (ED) is a prevalent male health disorder and a recognized sentinel marker for cardiovascular disease. Current diagnostic reliance on subjective questionnaires or invasive examinations limits early screening. Aim We aimed to deve…
  europepmc 2026 置信度 0.80
• Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia.
  Acute myeloid leukaemia (AML) is a highly heterogeneous haematologic malignancy in which transfusion support represents an essential component of comprehensive patient care. This review aims to provide an updated synthesis of recent progress in the development…
  europepmc 2026 置信度 0.80
• Longitudinal Transcriptomic Remodeling of Adipose Tissue After Bariatric Surgery Revealed by Differential Expression and Explainable Machine Learning.
  Background: Bariatric surgery improves metabolic health, but long-term transcriptomic remodeling of white adipose tissue (WAT) after Roux-en-Y gastric bypass (RYGB) remains incompletely defined. This study aimed to characterize longitudinal WAT gene-expression…
  europepmc 2026 置信度 0.80
• Fast fourier transform based spectral features for machine learning prediction of gamma passing rates in virtual VMAT PSQA: A feasibility study.
  Purpose This study aimed to evaluate the feasibility of fast Fourier transform (FFT) based spectral features for predicting volumetric modulated arc therapy (VMAT) gamma passing rates (GPRs) in virtual patient-specific quality assurance (PSQA). Methods A total…
  europepmc 2026 置信度 0.80
• Automated deep learning-based segmentation and volumetric analysis of meningiomas.
  Introduction Meningiomas are the most common primary intracranial tumors and are frequently monitored over extended periods. Volumetric assessment typically requires manual segmentation, which is time-consuming and associated with interrater variability. This …
  europepmc 2026 置信度 0.80
• An explainable machine learning model for predicting in-hospital infection in patients with systemic lupus erythematosus.
  Infection is a leading cause of mortality in patients with systemic lupus erythematosus (SLE), yet effective tools for early identification of high-risk patients are lacking. This study aimed to develop an explainable machine learning (ML) model to predict in-…
  europepmc 2026 置信度 0.80
• Preoperative Prediction of Lymphovascular Invasion in Breast Cancer Using Multicenter DCE-MRI Radiomics: Comparative Analysis and External Validation of Ten Machine Learning Algorithms
  Abstract Background: Lymphatic vessel invasion (LVI) is a key histopathological factor driving the metastatic process in invasive breast cancer; however, a definitive diagnosis is typically only possible postoperatively. Imagingomics based on dynamic contrast-…
  europepmc 2026 置信度 0.80
• Development and Validation of a Disability Risk Prediction Model for Older Adults Based on Machine Learning: A Multi-Algorithm Comparison with SHAP Interpretation.
  Objective To develop a predictive model for disability risk in older adults using machine learning algorithms. Methods A convenience sample of 13,809 older adults (aged ≥60 years) was recruited from seven medical institutions, three communities, and five nursi…
  europepmc 2026 置信度 0.80
• Machine Learning Models for Mortality Prediction in Burn Patients: Performance, Reliability, and Clinical Translation.
  Introduction Despite advances in contemporary burn care, the discriminative performance and clinical applicability of traditional prognostic scoring systems may be increasingly limited. Machine learning models have been applied to predict burn mortality; howev…
  europepmc 2026 置信度 0.80
• <i>NBN</i> rs1805794 Polymorphism Increases the Predictive Performance of Machine Learning Models for Multiple Chronic Toxicities in Head and Neck Cancer Survivors Treated with Definitive Radiotherapy ± Chemotherapy.
  Objective: While advancements in radiotherapy and systemic agents have significantly improved survival rates in head and neck squamous cell carcinoma (HNSCC), managing long-term, treatment-induced toxicities remains a critical clinical challenge. This study ai…
  europepmc 2026 置信度 0.80
• Beyond Traditional Covariates: An Interpretable Machine Learning Workflow for Improved Hybrid Pharmacometric Modeling.
  Model-informed precision dosing is often constrained by the limited generalizability of traditional population pharmacokinetic models, especially in critically ill patients. A hybrid machine learning-population pharmacokinetic framework is proposed to improve …
  europepmc 2026 置信度 0.80
• Interpretable Machine Learning Models for Mortality Prediction in Critically Ill Patients with Intra-Aortic Balloon Pump Therapy: Development and Evaluation.
  Background This study aims to develop and validate predictive models to assess mortality risk among patients undergoing intra-aortic balloon pump (IABP) therapy in the intensive care unit (ICU). Method A retrospective analysis was performed on 764 critically i…
  europepmc 2026 置信度 0.80
• Diagnosis of Parkinson's Disease Based on Voice and Speech Using Machine Learning and Deep Learning: A Systematic Umbrella Review.
  Early diagnosis of Parkinson's disease (PD) is complicated. Speech impairment, as an early symptom of PD, offers a noninvasive, scalable biomarker for remote assessment. Speech-based machine learning has shown promise, but methodological quality of existing ev…
  pubmed Farajollahi B, Saffarian A, Habibi SAH, Sheikhtaheri A 2026 置信度 0.82
• Explainable and High-Performance ECG-Informed Machine Learning and Deep Learning Framework for Cardiovascular Risk Prediction.
  Predictive models used for the assessment of cardiovascular disease (CVD) risk must not only be highly accurate but also explainable and reliable for clinical decision-making. In this paper, we present a methodological framework for explainable machine learnin…
  europepmc 2026 置信度 0.80
• Clinical data-driven machine learning for predicting molar-incisor hypomineralization and hypomineralized second primary molars
  Abstract Objectives This study evaluated clinical data machine learning (ML) models for predicting molar-incisor hypomineralization (MIH) and hypomineralized second primary molars (HSPM). Material and methods Data on demographic characteristics, socioeconomic …
  europepmc 2026 置信度 0.80
• Clinical decision framework for sublingual hematoma: literature-based retrospective analysis with machine learning.
  Sublingual hematoma is a rare but life-threatening condition with heterogeneous etiologies that complicate emergency decision-making. This study compared clinical characteristics and management outcomes between implant-induced sublingual hematoma (ISH) and spo…
  europepmc 2026 置信度 0.80
• Association between stomach pain and depression in middle-aged and older chinese adults: Evidence from CHARLS with machine learning and SHAP analysis.
  Background Emerging evidence indicates a bidirectional relationship between gastrointestinal symptoms and psychological distress, yet population-based evidence concerning stomach pain and depression remains scarce. This study investigates the association betwe…
  europepmc 2026 置信度 0.80
• Robotic-referenced automated measurement of acetabular cup orientation on postoperative CT: development and internal validation.
  Accurate measurement of acetabular cup orientation after total hip arthroplasty is essential, but postoperative CT relies on subjective, error-prone manual measurement. Robot-assisted surgery provides intraoperative cup-orientation measurements but is costly a…
  europepmc 2026 置信度 0.80
• Machine Learning-assisted Prediction of Fluid Overload and Its Nursing Implications in Patients With Acute Pancreatitis.
  Fluid overload (FO) is a critical complication in acute pancreatitis (AP), contributing to prolonged hospital stays and increased mortality. Traditional scoring systems like BISAP lack predictive accuracy for FO, placing a burden on nurses managing fluid thera…
  europepmc 2026 置信度 0.80
• A dual-validated machine learning model for predicting 3-year mortality in atypical pulmonary carcinoid, a rare neuroendocrine tumor.
  Objectives Atypical pulmonary carcinoid (AC) is a rare intermediate-grade neuroendocrine tumor with substantial clinical heterogeneity and an unpredictable prognosis. Accurate estimation of fixed-horizon mortality risk remains challenging because of its rarity…
  europepmc 2026 置信度 0.80
• Gerontechnology Acceptance in Thai Older Adult Care Facilities-Sensor Network and Machine Learning Adoption: Mixed Methods Study.
  Background Thailand is undergoing a rapid demographic transition, with an estimated 28% of the population expected to be aged 60 years or older by 2030. This shift creates an urgent demand for technology-enhanced solutions for older adults. Despite growing int…
  europepmc 2026 置信度 0.80
• Machine learning approaches for prediction of epilepsy risk across clinical pathways: a systematic review.
  Objective. Machine learning (ML) and deep learning (DL) models are increasingly being explored for individualised epilepsy risk prediction after a first unprovoked seizure (UFS) and after acute brain insults such as stroke or traumatic brain injury. We systema…
  pubmed Omar AM, Omisade A, Gallivan JP, Winston GP 2026 置信度 0.82
• Manual federated simulation for multiple sclerosis integrating XGBoost algorithm with SHAP explanation.
  Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system, underscoring the importance of early and accurate diagnosis. In this study investigates the predictive modelling of MS progression in patients with Clinically Isolated Synd…
  europepmc 2026 置信度 0.80
• Machine learning-based predictive model of postoperative acute heart failure among elderly hip fracture patients
  Abstract Objective This study enrolled elderly patients aged ≥ 80 years with hip fractures to investigate their clinical characteristics and key predictors of postoperative acute heart failure (AHF). We further developed traditional predictive models and machi…
  europepmc 2026 置信度 0.80
• Comparison of machine learning and logistic regression in predicting mortality from acute poisoning in young adults: a multicenter study identifying herbicide exposure as the predominant risk determinant.
  Objective Compare the effectiveness of machine learning algorithms and traditional logistic regression in predicting the mortality risk of young patients with acute poisoning, and establish a risk stratification nomogram. Methods This multicenter retrospective…
  europepmc 2026 置信度 0.80
• Clinical risk factors and machine learning for venous thromboembolism in lung cancer: an exploratory single-center study.
  Background Venous thromboembolism (VTE) represents a common and severe comorbidity in lung cancer patients. This study aimed to characterize the clinical features and independent risk factors associated with VTE in these individuals, and to evaluate the comple…
  europepmc 2026 置信度 0.80
• Pilot Study Employing a Machine Learning Approach as a Potential Method for Predicting Parkinson's Disease Using Voice as a Digital Biomarker and the SHAP Approach for Feature Engineering.
  Introduction : Voice-based digital biomarkers have emerged as a promising approach for distinguishing individuals with neurodegenerative disorders, particularly Parkinson's disease (PD), from healthy subjects (HS). With the increasing availability of smartphon…
  europepmc 2026 置信度 0.80
• Data-Driven Approaches for Autism Detection: A Comprehensive Review of Machine Learning Algorithms and Datasets.
  Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a broad spectrum of symptoms, which makes timely and accurate diagnosis challenging. The development of machine learning (ML) and deep learning (DL) has created opportunities for aut…
  europepmc 2026 置信度 0.80
• An Introduction to the Machine Learning Lifecycle for Clinical Microbiology.
  Background Clinical microbiology is undergoing rapid transformation driven by modern technologies generating high-volume, high-dimensional, and heterogeneous datasets that exceed the analytical capabilities of traditional rule-based approaches. Artificial inte…
  europepmc 2026 置信度 0.80
• An Optimized and Explainable Machine Learning Framework for Diabetes Prediction Using Marine Predators Algorithm and SHAP.
  Background: Diabetes mellitus affects over 500 million people worldwide, yet many machine learning prediction models remain difficult to interpret, limiting their clinical applicability. This study proposes an explainable machine learning framework integrating…
  europepmc 2026 置信度 0.80
• Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.
  Background/Objectives: Drug-drug interactions (DDIs) are major medication-safety concerns, and experimental testing cannot cover the expanding number of drug pairs. This systematic review evaluates graph-based machine-learning methods for DDI prediction, focus…
  europepmc 2026 置信度 0.80
• Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study.
  Bacterial infections represent a critical threat to neonatal health, accounting for approximately 25% of neonatal mortality globally. Timely and precise diagnosis in infants aged 1 to 90 days is essential to facilitate rapid intervention and prevent severe com…
  europepmc 2026 置信度 0.80
• Artificial Intelligence and Machine Learning Approaches for Cardiovascular Genomics: A State-of-the-Art Review.
  The advent of high-throughput sequencing technologies has revolutionized cardiovascular genetics, generating vast amounts of data. A major challenge in the genomic era is the efficient and accurate identification of causative genetic variants associated with c…
  europepmc 2026 置信度 0.80
• Development and evaluation of machine learning models to predict mechanical restraint and related coercive measures in hospital psychiatry.
  Purpose Coercive practices in psychiatric hospitals present clinical and ethical challenges. Aiming to support prevention, we developed and evaluated machine learning models predicting mechanical restraint and a composite of related coercive measures. Material…
  europepmc 2026 置信度 0.80
• An interpretable multimodal model for early prediction of delayed hematoma progression in frontal lobe contusion: a machine learning approach.
  Background Early identification of delayed hematoma progression (DHP) in patients with frontal lobe contusion remains challenging in emergency settings. This study aimed to develop and externally validate an interpretable multimodal machine-learning model inte…
  europepmc 2026 置信度 0.80
• Machine learning applications in Huntington's disease prognosis: A review.
  Understanding the trajectory of Huntington's disease (HD) is critical for patient stratification and the development of targeted interventions. Traditionally, studies relied on age-CAG models to estimate disease onset and progression, based on the well-establi…
  pubmed Abu Zohair LM, Andriessen R, Mahmoud N, Khalid M 等 2026 置信度 0.82
• Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-<sup>123</sup>I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset.
  Background Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning models have been developed using research datasets such as Parkinson's Precision Medicine Initiative (PPMI) an…
  europepmc 2026 置信度 0.80
• Machine learning for functional outcome prediction after vestibular schwannoma surgery: a systematic review and diagnostic test accuracy meta-analysis.
  Purpose Machine learning (ML) models have been increasingly applied to predict postoperative facial nerve dysfunction and hearing preservation after vestibular schwannoma (VS) surgery. However, reported performance varies substantially, and the overall diagnos…
  europepmc 2026 置信度 0.80