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• Predicting intravenous thrombolysis outcomes in acute ischemic stroke using machine learning.
  Objective Intravenous thrombolysis remains a cornerstone intervention for improving clinical outcomes in patients with acute ischemic stroke (AIS). Accurate prediction of poor functional outcomes following thrombolysis is essential for optimizing individualize…
  europepmc 2026 置信度 0.80
• Machine-Learning-Based Disease Diagnosis and Prediction: Progress, Perspectives, and the Path Forward.
  The convergence of machine learning (ML) and clinical medicine has significantly reshaped modern healthcare [...].
  europepmc 2026 置信度 0.80
• Machine Learning-Based Prediction of Delayed Cerebral Ischemia and Functional Outcome in Aneurysmal Subarachnoid Hemorrhage: The Role of CNS Infection-Related Parameters.
  Background/Objectives : Aneurysmal subarachnoid hemorrhage (aSAH) carries high morbidity and mortality, with delayed cerebral ischemia (DCI) as a major secondary complication. Although infection has increasingly been implicated in DCI pathogenesis, the role of…
  europepmc 2026 置信度 0.80
• Network and machine learning analysis of childhood trauma, mental health, and AI-based emotional support needs in adolescents from underdeveloped regions.
  Background The current intervention efficacy of generative conversational artificial intelligence (GCAI) on overall mental health issues remains limited, which may be related to the complex and unclear relation between GCAI emotional support needs (GCAI-ESN) a…
  europepmc 2026 置信度 0.80
• Predicting ankylosing spondylitis disease activity via patient-reported outcome measures: Building prediction models based on machine learning.
  Objective Disease activity is a critical indicator for monitoring the progression of ankylosing spondylitis (AS), guiding clinical decision-making, and informing treatment plans. Patient-reported outcome measures (PROMs) have gained prominence in AS clinical m…
  europepmc 2026 置信度 0.80
• Machine learning-based prediction of postoperative restenosis risk following direct repair of paediatric coarctation of the aorta.
  Objectives Coarctation of the aorta is a congenital cardiovascular disease with focal aortic luminal narrowing, and paediatric patients face a high postoperative restenosis risk. This study aimed to develop and validate an interpretable machine learning model …
  europepmc 2026 置信度 0.80
• Predicting First-in-Human Pharmacokinetics: Comparative Evaluation of Standard PBPK, High-Throughput PBPK, and Machine Learning.
  Accurate prediction of human pharmacokinetics is essential for selecting safe starting doses in first-in-human (FiH) trials. This study compares the predictive accuracy of three methodologies including standard physiologically based pharmacokinetic (PBPK) mode…
  europepmc 2026 置信度 0.80
• A nomogram integrating machine learning with clinical predictors for osteosarcopenia risk prediction in type 2 diabetes mellitus.
  Objective Type 2 diabetes mellitus (T2DM) predisposes patients to osteosarcopenia, a debilitating condition characterized by concurrent bone loss and muscle wasting. This study aimed to develop and internally validate a nomogram for predicting osteosarcopenia …
  europepmc 2026 置信度 0.80
• Predicting kinesiophobia in knee osteoarthritis: a head-to-head comparison between machine learning and traditional regression models.
  Background Although machine learning (ML) holds promise for improving clinical identification, its application to kinesiophobia in knee osteoarthritis (KOA) is limited. Therefore, this study aimed to develop and compare logistic regression (LR)-based nomograms…
  europepmc 2026 置信度 0.80
• High-risk carotid plaque detection using a novel radio-morphometric analysis of the carotid bifurcation.
  Objective. Current surgical eligibility criteria for the treatment of carotid artery disease, primarily relying on stenosis and image plaque characteristics, are suboptimal, highlighting the unmet need for improved strategies focused on recognition of high-ris…
  europepmc 2026 置信度 0.80
• Whole-genome sequencing reveals lineage-associated drug resistance and enables machine learning-based prediction in <i>Mycobacterium tuberculosis</i> clinical isolates.
  Background Drug-resistant tuberculosis (TB) remains a major obstacle to global TB control, particularly in high-burden settings where timely detection of resistance is limited. This study aimed to integrate whole-genome sequencing and machine learning approach…
  europepmc 2026 置信度 0.80
• Artificial Intelligence for Neonatal and Perinatal Mortality Prevention: A Systematic Review of Machine Learning and Deep Learning Applications.
  Background/Objectives: Maternal, perinatal, and neonatal mortality remain major global health challenges, causing approximately 2.5 million neonatal deaths annually, particularly in low- and middle-income countries (LMICs). Although Artificial Intelligence (AI…
  europepmc 2026 置信度 0.80
• Development and internal validation of an interpretable machine learning model for predicting vancomycin-induced nephrotoxicity in hospitalized children.
  Introduction Vancomycin is widely used for severe Gram‑positive infections in children, but vancomycin‑induced nephrotoxicity (VIN) limits its safe application. Existing prediction models rely primarily on traditional logistic regression with limited discrimin…
  europepmc 2026 置信度 0.80
• Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.
  Purpose The review surveys the type of machine learning approaches currently used in the alcohol literature, reviews challenges in applying machine learning tools to alcohol data, and explores how overcoming these challenges could advance personalized medicine…
  europepmc 2026 置信度 0.80
• Machine Learning to Identify Point-of-Care Ultrasound and Evaluate Standardized Documentation: Retrospective Operational Cohort Study.
  Background Point-of-care ultrasound (POCUS) is integral to obstetrics and gynecology (OBGYN), offering bedside diagnostic and therapeutic advantages. Despite its widespread adoption, accurate documentation and billing remain challenging due to inconsistent wor…
  europepmc 2026 置信度 0.80
• A lightweight machine learning approach for predicting breast cancer risk based on routine clinical indicators in the Chinese population.
  BackgroundConventional breast cancer screening is limited by cost, access, and patient discomfort. Whether a lightweight machine-learning model trained on routine laboratory indicators can support opportunistic screening remains unsettled.MethodsA retrospectiv…
  europepmc 2026 置信度 0.80
• Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.
  Introduction This study evaluated machine learning (ML) models predicting esophageal cancer (EC) treatment outcomes, focusing on data modalities, feature engineering, model frameworks, and validation. Methods Following PRISMA guidelines (PROSPERO: CRD420246199…
  europepmc 2026 置信度 0.80
• Explainable Machine Learning for Predicting Complicated Appendicitis and Expected Hospital Length of Stay in Children: An Exploratory Single-Center Study.
  Background and Objectives : Pediatric appendicitis remains a common surgical emergency, but early risk stratification of complicated appendicitis and expected hospital length of stay (LOS) remains challenging. This study developed and internally evaluated an e…
  europepmc 2026 置信度 0.80
• Risk prediction of non-small cell lung cancer in patients with pulmonary nodules: a single-center cohort study based on six machine learning algorithms.
  Objective Lung cancer is a major global public health concern. Non-small cell lung cancer (NSCLC), the most common subtype, has drawn increasing attention. To achieve early and accurate identification of NSCLC in patients with pulmonary nodules, reliable clini…
  europepmc 2026 置信度 0.80
• Personalized Antibiogram: A Novel Multitask Machine Learning Framework for Simultaneous Prediction of Antimicrobial Resistance Profile With Enhanced Detection of Carbapenem Resistance in Enterobacteriaceae.
  Background Conventional hospital antibiograms summarize aggregated resistance rates, limiting their utility for individualized antimicrobial selection. Existing statistical and machine learning models predict each phenotype separately, ignoring correlations am…
  europepmc 2026 置信度 0.80
• Development and Validation of an XGBoost-Based Machine Learning Model With Nomogram for Predicting Diabetic Peripheral Neuropathy Risk in Type 2 Diabetes Patients.
  Background The study aimed to develop risk stratification models for diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM) using multiple machine learning algorithms, identify the optimal model, and visualize it through a nomogr…
  europepmc 2026 置信度 0.80
• Early identification of vascular cognitive impairment from a multimodal perspective: a combined diagnosis from targeted cognitive assessments, imaging biomarkers, and molecular fluid biomarkers to ecological behavioral characteristics.
  Vascular cognitive impairment (VCI), the second leading cause of dementia, is characterized by heterogeneous pathophysiology and a potentially reversible early phase, underscoring the need for timely identification. This review synthesizes advances across four…
  europepmc 2026 置信度 0.80
• Development and validation of a machine learning-based model for predicting live birth outcomes in patients undergoing frozen-thawed embryo transfer.
  Background Infertility affects millions of people worldwide, imposing substantial psychological, economic, and social burdens on patients. Although frozen-thawed embryo transfer (FET) is increasingly used, the live birth rate remains approximately 40%, highlig…
  europepmc 2026 置信度 0.80
• Machine Learning to Identify Bacteremia and Meningitis in Febrile Infants: A Systematic Review.
  Context The accurate identification of febrile infants who are at risk for invasive bacterial infections (IBIs), ie, bacteremia and meningitis, is essential to reduce morbidity and avoid unnecessary procedures. The role of machine learning (ML) in improving th…
  europepmc 2026 置信度 0.80
• A population-based retrospective machine learning study of COVID-19 severity using integrated clinical and viral genomic data in Jiangsu Province, China.
  Background As coronavirus disease 2019 (COVID-19) has transitioned into an endemic phase characterized by sustained transmission and widespread hybrid immunity, understanding region-specific determinants of severe disease remains important for real-world risk …
  europepmc 2026 置信度 0.80
• Decoding the role of phthalate plasticizers (DBP, DOP, and DEHP) in idiopathic pulmonary fibrosis: an integrated network toxicology and machine learning approach.
  The molecular mechanism of idiopathic pulmonary fibrosis (IPF) caused by phthalate (PAE) is not well understood, presenting notable clinical and toxicological challenges. This research seeks to advance drug toxicology and formulate targeted prevention and trea…
  europepmc 2026 置信度 0.80
• An Exploratory Transcriptomic Classification Model for Psoriasis Based on Apoptosis-Associated and Proliferation-Apoptosis-Coupled Genes Using Explainable Machine Learning.
  This study aimed to integrate apoptosis-associated and proliferation-apoptosis-coupled transcriptomic signatures with explainable machine learning to construct an exploratory molecular classification model for psoriasis. Transcriptomic datasets GSE30999 and GS…
  europepmc 2026 置信度 0.80
• Use of explainable machine learning in risk classification of Cesarean section delivery: A cross-sectional analysis of Demographic and Health Surveys from ten Sub-Saharan African countries (2016-2024).
  Cesarean section (CS) delivery is an important surgical intervention for reducing maternal and neonatal morbidity and mortality when medically indicated; however, substantial inequalities in its utilization persist across Sub-Saharan Africa (SSA). This study e…
  europepmc 2026 置信度 0.80
• Machine learning-driven evaluation of protein kinase D3 as a co-diagnostic biomarker in hepatocellular carcinoma.
  To elucidate the diagnostic value and clinical relevance of protein kinase D3 (PRKD3) in hepatocellular carcinoma (HCC), we analyzed data retrieved from The Cancer Genome Atlas (TCGA) database, which revealed high expression of PRKD3 in HCC tissues. Subsequent…
  europepmc 2026 置信度 0.80
• Prediction of early responses to dupilumab treatment in patients with moderate-to-severe atopic dermatitis by a machine learning model integrating clinical characteristics and serum biomarkers.
  Aim The aim of the study was to evaluate the predictive value of baseline serum periostin and other T helper cell type 2 (Th2)-related biomarkers for early response to dupilumab in patients with moderate-to-severe atopic dermatitis (AD), and to develop a machi…
  europepmc 2026 置信度 0.80
• Decision-Support Framework for Nutritional Risk in Small Cell Lung Cancer: A Time-Series Model Using Imputation Strategies for Incomplete Clinical Data.
  In patients with small cell lung cancer (SCLC), nutritional status is a key determinant of disease progression, treatment tolerance, and prognosis. The prognostic nutritional index (PNI), reflecting both immune and nutritional conditions, is widely used to eva…
  europepmc 2026 置信度 0.80
• Development and validation of an interpretable machine learning model for predicting 5-year major adverse cardiovascular events in patients with coronary artery disease.
  Background Coronary artery disease (CAD) remains a major contributor to global cardiovascular mortality and accurate prognosis is critical for guiding clinical decision-making. This study aimed to develop and validate interpretable machine learning (ML) models…
  europepmc 2026 置信度 0.80
• Machine Learning for Predicting Postoperative Complications After Hypospadias Surgery: A 10-Year Single-Center Retrospective Cohort Study.
  Objectives: Hypospadias is one of the most common congenital malformations of the male genitourinary system, and postoperative complications remain a major concern affecting surgical outcomes and patients' quality of life. Whether machine learning models can e…
  europepmc 2026 置信度 0.80
• Combining structural and microvascular parameters via machine learning for enhanced diagnosis of normal-tension glaucoma among highly myopic eyes: a prospective cross-sectional diagnostic accuracy study.
  Purpose This study aimed to evaluate whether a combination of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters could improve the discrimination between clinically diagnosed normal-tension glaucoma (NTG) and non-glaucomatous high myopia …
  europepmc 2026 置信度 0.80
• Rapid prediction of vancomycin-resistant <i>Enterococcus faecium</i> using MALDI-TOF mass spectrometry and machine learning.
  Background Vancomycin-resistant Enterococcus faecium (VRE fm ) is a World Health Organization priority pathogen, yet conventional phenotypic susceptibility testing requires up to 72 h, delaying targeted antimicrobial therapy. This study aimed to develop an int…
  europepmc 2026 置信度 0.80
• Endothelial activation and stress index associated with in-hospital mortality risk in patients with end-stage renal disease: a retrospective analysis based on the MIMIC database and machine learning model development.
  Background End-stage renal disease (ESRD) is a severe chronic renal disorder with high mortality, requiring dialysis treatment or kidney transplantation. The endothelial activation and stress index (EASIX), reflecting inflammatory status and endothelial dysfun…
  europepmc 2026 置信度 0.80
• Construction and validation of a machine learning-based risk prediction model for venous thromboembolism in older adult patients: A multifactorial analysis of 28,231 cases.
  Venous thromboembolism (VTE) is a leading preventable cause of in-hospital mortality in older adults, yet early risk stratification remains a key clinical challenge. This study aimed to develop and internally validate an explainable machine learning model for …
  europepmc 2026 置信度 0.80
• Predicting Next-Day Passive Suicidal Ideation in At-Risk Youth.
  Introduction Passive suicidal ideation (SI) is a well-established risk factor for suicidal behavior but has received less attention than active SI. Although recent work has leveraged intensive longitudinal data and machine learning (ML) to forecast short-term …
  europepmc 2026 置信度 0.80
• Predicting Complicated Appendicitis: What Can Machine Learning Add?
  Background/Objectives : Early identification of complicated appendicitis in children remains challenging. We developed and internally validated laboratory-based machine-learning models for severity stratification using age and routine admission laboratory data…
  europepmc 2026 置信度 0.80
• Machine learning-based diagnostic models for prevalent metabolic syndrome among patients with disuse muscle atrophy: a comparative study of seven algorithms.
  Background Disuse muscle atrophy (DMA) leads to muscle loss and impaired motor function, while metabolic syndrome (MetS) increases the risk of cardiovascular disease and diabetes. Muscle loss exacerbates insulin resistance, accelerates MetS progression, and cr…
  europepmc 2026 置信度 0.80
• Explainable machine learning for predicting lower extremity deep vein thrombosis in traumatic brain injury patients: development of a SHAP-guided Random Forest model.
  Background Lower extremity deep vein thrombosis (LEDVT) is a common and clinically important complication after traumatic brain injury (TBI). Early identification of patients at increased thrombotic risk remains challenging. We aimed to develop and internally …
  europepmc 2026 置信度 0.80
• An interpretable machine learning model for diabetic foot risk classification in patients with diabetes.
  Diabetic foot is a severe chronic complication of diabetes, mainly resulting from peripheral neuropathy and vasculopathy, and may progress to ulcers, infections, and amputation if not treated in a timely manner. Early identification of patients who already pre…
  europepmc 2026 置信度 0.80
• Development and External Validation of an Interpretable Machine Learning Model Using Routine Blood Biomarkers for Differentiating Malignant Breast Nodules.
  Background Precise identification of malignant breast nodules is critical for clinical intervention. This study aimed to develop and externally validate an interpretable machine learning model using routine blood biomarkers. Methods This retrospective multicen…
  europepmc 2026 置信度 0.80
• A predictive model for early neurological deterioration in medullary infarction based on explainable machine learning.
  Background Medullary infarction is a severe subtype of ischemic stroke. Early neurological deterioration (END) is a common adverse event that can significantly affect clinical outcomes. Objective This study aimed to develop a machine learning model to effectiv…
  europepmc 2026 置信度 0.80
• Machine learning based prediction of antimicrobial resistance <i>in Klebsiella spp</i>.: a five-year retrospective study.
  Introduction Klebsiella species are well-recognized pathogens implicated in both healthcare-associated and community-acquired infections, and they contribute substantially to the global burden of antimicrobial resistance. In this study, we investigated the epi…
  europepmc 2026 置信度 0.80
• Machine Learning Identifies High-Risk Suicide Profiles in a Population-Based Forensic Registry.
  Background: Suicide is a leading cause of preventable death, yet machine learning (ML) analyses of forensic (medico-legal) suicide data are scarce and, to our knowledge, absent for Romania. Population-based forensic registries offer exhaustive, autopsy-confirm…
  europepmc 2026 置信度 0.80
• Noninvasive prediction of severe histopathology in drug-induced liver injury using a dual elastography-based machine learning model.
  Objectives To develop and validate a machine learning (ML) model integrating dual elastography, clinical features, and serum biomarkers for noninvasive prediction of severe drug-induced liver injury (DILI). Materials and methods This prospective multicenter st…
  europepmc 2026 置信度 0.80
• Quality indicators for cancer pain management: natural language processing and machine learning from electronic clinical records.
  Objectives The continual evaluation of quality indicators (QIs) for cancer pain management is essential to maintaining and improving its quality. However, free-text notes in electronic medical records (EMRs) must be reviewed to accurately assess pain managemen…
  europepmc 2026 置信度 0.80
• Development and validation of machine learning models for early diagnosis of hemophagocytic lymphohistiocytosis in pediatric Epstein-Barr virus infection.
  Objective To establish a machine learning (ML) model for the early diagnosis of Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis (EBV-HLH) based on clinical features and to utilize the Shapley Additive Explanations (SHAP) method to interpret th…
  europepmc 2026 置信度 0.80
• Development of a Tongue Image-Based Machine Learning Tool for the Diagnosis of Colorectal Cancer: A Prospective Multicentre Clinical Cohort Study.
  Colorectal cancer (CRC) remains a persistent major global health burden, with traditional diagnostic methods like colonoscopy suffering from suboptimal patient compliance rates. This study develops an intelligent diagnostic model based on tongue images to assi…
  europepmc 2026 置信度 0.80
• Advancing the FAIRness of Multimodal Imaging Research Through the OMOP MI-CDM Framework: A Case Replication Study in Alzheimer's Disease.
  The objective of this study is to demonstrate an end-to-end approach for operationalizing the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in multimodal medical imaging research using standardized data models and reproducible computation…
  europepmc 2026 置信度 0.80
• Prediction of early treatment response to drug-eluting beads chemoembolization for hepatocellular carcinoma using machine learning.
  Background Drug-eluting bead trans-arterial chemoembolization (DEB-TACE) has been extensively employed as a locoregional therapy for hepatocellular carcinoma (HCC). In this work, machine-learning algorithms are used to forecast early treatment response in HCC …
  europepmc 2026 置信度 0.80
• Radiology No-Show Calculator: A Social Determinants of Health-Enriched Machine Learning Prediction Model with Financial Analysis.
  Objective Outpatient radiology appointment no-shows delay timely diagnosis and exacerbate healthcare disparities, while also resulting in a significant financial and operational burden on healthcare systems. Machine learning (ML) models offer a promising tool …
  europepmc 2026 置信度 0.80
• A Clinical and Biomarker-Based Model for Predicting in-Hospital Mortality in Patients Undergoing Extracorporeal Membrane Oxygenation: A Machine Learning-Based Nomogram Study.
  Purpose A nomogram was constructed for predicting in-hospital mortality in patients receiving extracorporeal membrane oxygenation (ECMO) using clinical indicators, to provide a reference for clinical risk stratification. Patients and methods A retrospective co…
  europepmc 2026 置信度 0.80
• Development and prospective validation of a machine learning model for risk stratification of drug-induced liver injury using real-world clinical data.
  Background Drug-induced liver injury (DILI) is difficult to diagnose and manage in routine care because it lacks pathognomonic biomarkers and is often recognized only after clinically meaningful injury has occurred. Existing computational approaches are largel…
  europepmc 2026 置信度 0.80
• How machine learning applied on fMRI could improve the prognosis of the post-traumatic stress disorder: A systematic review.
  Background Post-traumatic stress disorder (PTSD) is a stressor-related disorder that affects a significant proportion of the population worldwide. Despite the neurological nature of this disorder, its functional biomarkers, due to mixed results remain far to b…
  europepmc 2026 置信度 0.80
• Machine learning for postoperative complication prediction and early recurrence risk assessment across cancer types: a systematic review and meta-analysis.
  Background Although machine learning is often used in medical diagnosis, its effectiveness in cancer diagnosis remains uncertain. Objective To explore the ability of machine learning to predict cancer postoperative complications and early recurrence. Methods F…
  europepmc 2026 置信度 0.80
• Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas.
  Objective To analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM), multiple machine learning-based predictive mod…
  europepmc 2026 置信度 0.80
• Machine learning-based prediction of E. coli infection in hospitalized patients using a no-code analytical framework.
  Hospital-acquired infections (HAIs) remain a major global concern, contributing significantly to increased morbidity, mortality, and healthcare costs. Among the causative pathogens, Escherichia coli (E. coli) is one of the most frequently isolated microorganis…
  europepmc 2026 置信度 0.80
• Development and validation of a machine learning-based risk prediction model for PICC-associated bloodstream infections in preterm infants: a retrospective multicenter study.
  Objective To develop and validate a machine learning-based model for predicting the risk of peripherally inserted central catheter (PICC)-associated bloodstream infection (CRBSI) in preterm infants. Methods This retrospective multicenter study included 151 pre…
  europepmc 2026 置信度 0.80
• Development and temporal validation of a prediction model for cognitive impairment in older adults with hearing loss based on the population health risk management framework.
  Background Older adults with hearing loss are at increased risk of cognitive impairment, yet tailored risk stratification tools remain limited. Methods In this cross-sectional study, 524 adults aged ≥60 years with hearing loss were enrolled between June 2023 a…
  europepmc 2026 置信度 0.80
• Machine learning models predicting extubation success in mechanically ventilated patients: a systematic review and meta-analysis.
  Background Optimal timing of extubation in mechanically ventilated patients remains a major challenge in intensive care. Machine learning (ML) models have been increasingly proposed to support clinical decision-making, yet their predictive performance and read…
  europepmc 2026 置信度 0.80
• Machine learning prediction of post-traumatic osteoarthritis based on three-dimensional printing-derived joint congruence biomechanics in ankle fractures.
  Objective Post-traumatic osteoarthritis (PTOA) develops in 20-40% of patients following ankle fracture fixation despite anatomic reduction. This study aimed to develop a machine learning model incorporating three-dimensional (3D) printing-derived joint congrue…
  europepmc 2026 置信度 0.80
• Utilizing Serum Fluorescence Spectra and Machine Learning Algorithms for Efficient Diagnosis of Sheep Brucellosis.
  In sheep, the infectious disease brucellosis is caused by Brucella melitensis. Traditional serological techniques for detecting Brucella in sheep are slow and not very accurate, necessitating a faster and more precise screening method. This study seeks to asse…
  europepmc 2026 置信度 0.80
• Mental Health Risk Detection From Social Media Text Data: A Scoping Review of the Machine Learning Research Landscape.
  Machine learning approaches have been increasingly applied to social media text data for mental health risk detection. However, existing studies vary widely in target outcomes, data sources, labeling strategies, and evaluation practices, and a structured overv…
  europepmc 2026 置信度 0.80
• Development and internal validation of an interpretable machine learning model using first-24-h postpartum nursing variables to predict delayed lactogenesis II after cesarean delivery.
  Background Delayed lactogenesis II is a common early postpartum breastfeeding problem after cesarean delivery and may interfere with the establishment of exclusive breastfeeding. Although demographic and obstetric risk factors have been widely studied, the pre…
  europepmc 2026 置信度 0.80
• A combined model based on clinical, radiomics, and deep transfer learning features for differentiating endometrial hyperplasia with polyps from endometrial cancer.
  Objective This study aimed to develop an ultrasound-based deep learning radiomics nomogram for differentiating endometrial hyperplasia with polyps from endometrial cancer in endometrial lesions and to explore its clinical diagnostic efficacy. Methods We retros…
  europepmc 2026 置信度 0.80
• Predicting Critical Outcomes in Suspected Cardiopulmonary Emergencies Using Dispatch Narratives: Temporal Validation Study.
  Background Early risk stratification in emergency medical services (EMS) is essential for patients presenting with acute cardiopulmonary symptoms, yet prehospital decision-making at the dispatch stage is often based on limited structured information. Free-text…
  europepmc 2026 置信度 0.80
• Explainable XGBoost model and nomogram for risk factor identification and risk prediction in cerebral small vessel disease: a machine learning-based retrospective cohort study.
  Background Cerebral small vessel disease (CSVD) is a common, clinically significant vascular disorder that frequently leads to cognitive impairment, dementia, and poor overall prognosis. Owing to its complex hemodynamic characteristics and multifactorial patho…
  europepmc 2026 置信度 0.80
• Integrating physics guidance into deep learning for cine cardiovascular segmentation.
  Cine cardiovascular magnetic resonance (CMR) provides dynamic sequences for quantifying clinically important indices such as ventricular volumes and ejection fraction. A fast and accurate deep segmentation model is therefore valuable for timely analysis, reduc…
  pubmed Tran-Nguyen MA, Luong QK, Kha MB, Dang TL 2026 Aug 置信度 0.82
• Learning generalizable representations across Heterogeneous Acquisition Environments for Breast Ultrasound Diagnosis.
  Ultrasound image classification remains challenging in clinical practice, given substantial variations in image appearance across devices, acquisition protocols, and operator habits, often unrelated to underlying pathology and associated with inconsistent mode…
  pubmed Wang H, Zhang S, Kong W, Shao W 等 2026 Aug 置信度 0.82
• Fusion of ERP and fNIRS signals for classification of internet gaming disorder in an attention-modulating dot-probe task.
  Internet gaming disorder (IGD) has emerged as a significant behavioral addiction and public health concern. However, its underlying neurobiological mechanisms remain unclear. This study aimed to develop a machine learning approach to evaluate IGD based on mult…
  pubmed Altınkaynak M, Batbat T, Yeşilbaş D, Güven A 等 2026 Oct 置信度 0.82
• V(2)-Former: Towards volumetric framework for instance-level segmentation and prediction of fetal ventriculomegaly in anisotropic MRI.
  Accurate identification and assessment of fetal ventriculomegaly (VM) is crucial for prenatal care. However, conventional diagnosis relies on manual 2D slice-based measurements, which may overlook 3D morphological cues. Existing deep learning approaches typica…
  pubmed Zhang Z, Zhang L, Huang W, Wang Y 等 2026 Sep 置信度 0.82
• Spatial phenotyping of epicardial adipose tissue from cardiac MRI.
  Epicardial adipose tissue (EAT) is increasingly recognized as an important contributor to cardiovascular disease (CVD), but its spatial distribution across the heart remains insufficiently characterized due to limitations in existing segmentation and analysis …
  pubmed Feng F, Long T, Hasaballa AI, Sun X 等 2026 Sep 置信度 0.82
• Machine learning and deep learning for ankle fracture detection in radiographs: A systematic review with narrative synthesis on diagnostic performance and future clinical integration.
  Ankle fractures are a prevalent orthopedic injury with X-rays being the primary diagnostic tool. Errors in radiological interpretation leads to missed fractures and delayed diagnosis. This systematic review with narrative synthesis aims to map the current land…
  pubmed Mangwani J, Jithin SP, Akram N, Ravi L 等 2026 Sep 置信度 0.82
• Can AI-based chatbots enhance patient education and support nursing practice for hip replacement patients? A scoping review.
  Artificial intelligence (AI), particularly machine learning and large language models (LLMs), has seen rapid advancements and widespread adoption over the past decade, transforming many fields, including healthcare. ChatGPT is one of the prominent chatbot inte…
  pubmed Kaur J, Adedoyin F, Budka M, Wainwright TW 2026 Aug 置信度 0.82
• Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review.
  Large language model (LLM)-based conversational agents are increasingly used in health care, yet their capacity to support genuine multiturn dialogue remains underexplored. In oncology, where patients and caregivers experience complex informational and emotion…
  pubmed Jeong Y, Cha H, Suh EE 2026 Jul 21 置信度 0.82
• Prediction of factors contributing to Pain Intensity among low back pain patients: A comparative machine learning frameworks (Random Forest versus XGBoost).
  Predicting pain intensity in patients with low back pain (LBP) remains a complex task due to the biopsychosocial nature of pain. Pain intensity is shaped by multifaceted interactions among demographic, lifestyle, and clinical factors.
  pubmed Algamdi MM, Alghamdi AH 2026 置信度 0.82
• Feature integration of [18F]FDG PET brain imaging using deep learning for sensitive cognitive decline detection.
  Distinguishing individuals with cognitive decline (CD), including early Alzheimer's disease, from cognitively normal (CN) individuals is essential for improving diagnostic accuracy and enabling timely intervention. Positron emission tomography (PET) captures m…
  pubmed Lee Y, Kim S, Kang Y, Alzheimer’s Disease Neuroimaging Initiative 2026 置信度 0.82
• Brain aging patterns among nine neurological disorders: A case-control study.
  The difference between neuroimaging-predicted brain age and chronological age, the predicted age difference (PAD), has been studied as a potential biomarker reflecting individual brain health. Although previous large-scale studies have shown that brain age dev…
  pubmed Liang C, Pearlson G, Bustillo J, Kochunov P 等 2026 Jul 置信度 0.82
• Post-COVID-19 Condition Diagnosis among Older People: Findings from Swedish National Register Data.
  Post-COVID-19 condition (PCC) denotes the persistence of symptoms following Severe Acute Respiratory Syndrome Coronavirus 2 infection. PCC poses a large disease burden worldwide. We aimed to explore the epidemiological characteristics of clinically diagnosed P…
  pubmed Zhang Z, Hoang MT, Wastesson JW, Johnell K 2026 Jul 21 置信度 0.82
• The FACT-Score: Development and Temporal Validation of a Clinical Risk Stratification Tool for 30-Day Complications After Operative Facial Fracture Repair.
  No validated, procedure-specific tool exists for predicting 30-day complications after operative facial fracture repair. This study aimed to develop and temporally validate a clinical risk stratification tool for 30-day complications after operative facial fra…
  pubmed Catic A, Karamitros G, Lineaweaver WC, Lamaris GA 等 2026 Jul 20 置信度 0.82
• Individual-Specific Functional Connectivity-Based State Classification and Prognosis Prediction for Disorders of Consciousness.
  Accurate prognosis and treatment targeting for disorders of consciousness (DOC) remain challenging due to profound neurobiological heterogeneity. Current approaches to DOC state classification and prognostic prediction, which rely on resting-state functional m…
  pubmed Wan Z, Chen G, Li H, Bai Y 2026 置信度 0.82
• Integrated bioinformatics and experimental analysis identify apoptosis- and pyroptosis-related hub genes as candidate diagnostic biomarkers in Alzheimer's disease.
  Apoptosis and pyroptosis-mediated neuronal death represent major pathogenic mechanisms underlying Alzheimer's disease (AD). Given the potential crosstalk between these two forms of cell death, investigation of a single death pathway may be insufficient to iden…
  pubmed Cai Y, Chen Y, Huang G, Ren M 等 2026 Jul 21 置信度 0.82
• Machine learning algorithms for predicting arrhythmic events in Hypertrophic Cardiomyopathy: limited enhancement beyond late gadolinium enhancement.
  We aimed to develop and assess the performance of a Machine learning (ML) model integrating common clinical features to predict arrhythmic events in patients with Hypertrophic Cardiomyopathy (HCM). Post-hoc analysis of an international multicenter registry of …
  pubmed Certo Pereira J, Amador R, Vieira A, Almeida Carvalho R 等 2026 Jul 21 置信度 0.82
• Moving on in hydrocephalus imaging: from 2D to 3D biomarkers.
  Linear two-dimensional indices such as the Evans index, callosal angle, and fronto-occipital horn ratio remain the clinical standard for hydrocephalus assessment, yet are limited by measurement variability, insensitivity to spatial CSF redistribution, and redu…
  pubmed Da Mutten R, Turczynski Holmgren R, Edström E, Elmi-Terander A 等 2026 Jul 21 置信度 0.82
• Predicting the absence of World Health Organization fungal priority pathogens in agricultural soils using machine learning and ITS metabarcoding.
  Environmental fungal pathogens relevant to human and animal health pose significant risks, particularly in regions with intensive farming and climate variability. Several taxa detected in Thailand's clinical and environmental samples, such as Candida tropicali…
  pubmed Ibrahim SNMM, Jaikla C, Pombubpa N 2026 Jul 21 置信度 0.82
• Artificial intelligence for pediatric neuroimaging.
  Artificial intelligence (AI) is revolutionizing health care, particularly in radiology for which large retrospective electronic datasets are naturally suited to training large machine and deep learning models. To date, the vast majority of United States Food a…
  pubmed Ho ML 2026 Jul 21 置信度 0.82
• AI-assisted TURP slide review: a multi-reader study of efficiency, diagnostic performance, and human-AI error patterns.
  Transurethral resection of the prostate (TURP) specimens represent a low-prevalence, high-volume diagnostic task in which pathologists must identify rare malignant foci within large amounts of predominantly benign tissue. Although artificial intelligence (AI) …
  pubmed Pascarella CB, Giobbe M, Maffei E, Ruotolo R 等 2026 Jul 21 置信度 0.82
• Novel Musculoskeletal Hypotheses in the Armed Services Trauma and Rehabilitation Outcome (ADVANCE) Cohort: Development and Application of Sparse Group Factor Analysis Methodology.
  Musculoskeletal conditions are a leading global cause of disability, yet the factors influencing long-term musculoskeletal health, particularly following trauma, remain incompletely understood. Machine learning could be applied to identify previously unknown p…
  pubmed Watson FCE, Ferreira FS, Kadirvelu B, Bennett AN 等 2026 Jul 21 置信度 0.82
• COLOSSUS-AI: A Pilot Study on Artificial Intelligence Driven Prediction of Postoperative Outcomes in Colorectal Surgery.
  Background: Postoperative complications remain a major source of morbidity after colorectal surgery and are associated with prolonged hospitalization, reintervention, intensive care unit admission, and increased mortality. Early identification of high-risk pat…
  pubmed Botoncea M, Cosma CD, Nicolescu CL, Butiurca VO 等 2026 Jun 置信度 0.82
• A Locally Executable AI System for Improving Preoperative Patient Communication: Multidomain Clinical Evaluation.
  Patients undergoing invasive procedures frequently experience anxiety and often have unanswered questions regarding the procedure. Although large language models show considerable promise for supporting patient communication in many cases, their deployment in …
  pubmed Sato M, Nagata S, Ohnuma M, Takahashi H 等 2026 Jul 21 置信度 0.82
• Medical AI Agents for Clinical Decision Support: Viewpoint Using the Planning, Action, Reflection, and Memory (PARM) Analytical Lens.
  Medical AI agents are emerging as a new generation of clinical decision support systems, moving beyond static prediction toward multistep, workflow-oriented assistance. This Viewpoint argues that agentic architectures incorporating planning, action, reflection…
  pubmed Dinc R, Ardic N 2026 Jul 21 置信度 0.82
• Stress CMR ischemic burden and long-term mortality in left ventricular systolic dysfunction: a multicenter registry study.
  Stress perfusion cardiovascular magnetic resonance (CMR) integrates assessment of myocardial ischemia, scar, and function. Its incremental prognostic value in patients with left ventricular (LV) systolic dysfunction remains uncertain.
  pubmed Pfeffer A, Garot J, Duhamel S, Garot P 等 2026 Jul 21 置信度 0.82
• Structured Clinical Input-Guided Large Language Model Workflow for Acute Ischemic Stroke Discharge Education: A Multicenter Feasibility Study.
  Large language models (LLMs) may support patient education, but their clinical use remains challenging. We aimed to evaluate the preliminary feasibility of a structured clinical input-guided LLM workflow for generating discharge education drafts for patients w…
  pubmed Yin J, Wang W, Wu L, Li Z 等 2026 Sep 置信度 0.82
• Artificial Intelligence-Based Analysis of Coronary Atherosclerotic Plaque on Intravascular Ultrasound: A Systematic Review and Meta-Analysis.
  Coronary atherosclerotic plaque detection is essential for the diagnosis and management of coronary artery disease. Although intravascular ultrasound (IVUS) enables detailed plaque assessment, its clinical use is limited by time-consuming interpretation and ob…
  pubmed Eini P, Serpoush H, Rezayee M, Kassulke M 2026 Jul 置信度 0.82
• Automated Differentiation of Oral Red-White Lesions: An Interpretable Deep Learning Approach Combining Ensemble Architectures and Saliency Maps.
  Oral Potentially Malignant Disorders (OPMDs), including Leukoplakia and Erythroplakia, carry significant risks of malignant transformation. Early differentiation from confounding inflammatory conditions like Oral Lichen Planus (OLP) and Candidiasis is critical…
  pubmed Koochaki M, Mousavie A, Basirat M, Hendi A 等 2026 Aug 置信度 0.82
• Development of a Predictive Model for Transfusion-Related Acute Lung Injury Based on Neutrophil Extracellular Traps (NETs).
  Patients receiving massive transfusion after acute hemorrhage are at risk for transfusion-related acute lung injury (TRALI), a severe complication. Neutrophil extracellular traps (NETs) play a key role in acute lung injury. This study aimed to explore the link…
  pubmed Wang Q, Li Z, Shao J, Qiang X 等 2026 置信度 0.82
• Scalable, context-sensitive psychiatric assessment with large language models and brief diaries.
  Accurate psychiatric assessment requires understanding a person's unique experience within their psychosocial context. Clinical interviews have been the gold standard for assessment as the only methods capable of this complex task, but they are time and resour…
  pubmed Ringwald WR, Taxali A, Angstadt M, Vize CE 等 2026 Jul 21 置信度 0.82
• Tailored injectable nanocomposite hydrogels for high-performance biomedical applications.
  Conventional hydrogel systems for biomedical applications face critical limitations in mechanical robustness, therapeutic functionality, and responsiveness to physiological stimuli, hindering their translation to precision medicine. The rational integration of…
  pubmed Rathee G, Singh NK, Kohli S, Deshmukh K 等 2026 Aug 25 置信度 0.82