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• Effect of Data Heterogeneity in Clinical MALDI-TOF Mass Spectra Profiles on Direct Antimicrobial Resistance Prediction through Machine Learning
  Abstract The matrix-assisted laser desorption-ionization time-of-flight mass spectrometry has become a powerful tool for accurate species identification in routine diagnostic microbiology. Recently, the application of machine learning models with MALDI-TOF mas…
  crossref Youngjun Park, Michael Weig, Christine Noll, Oliver Bader 等 2024-10-20T13:20:13Z 置信度 0.82 artificial intelligencecomputer sciencecs.lgbiological sciences not elsewhere classifiedmachine learning
• The Intersection of Data Governance and Machine Learning in Clinical Trials
  The Intersection of Data Governance and Machine Learning inClinical Trials
  crossref Ammar Hassan 2023-09-07T05:03:10Z 置信度 0.70
• Managing Postembolization Syndrome Through a Machine Learning–Based Clinical Decision Support System
  Although transarterial chemoembolization has improved as an interventional method for hepatocellular carcinoma, subsequent postembolization syndrome is a threat to the patients' quality of life. This study aimed to evaluate the effectiveness of a clinical deci…
  crossref Minkyeong Kang, Myoung Soo Kim 2024-09-26T17:00:23Z 置信度 0.82 medicinehealth caremedlineintensive care medicineinternal medicine
• Unlocking the future of leadership: Navigating human-machine collaboration in the age of machine learning
  I n an era where prolonged stability often masks the underlying fragility of our society, awareness of these vulnerabilities tends to diminish.The rapid pace of technological advancements and the increasing complexities of our global landscape present unpreced…
  crossref Christopher Peterka 2024-08-14T09:57:55Z 置信度 0.82 computer scienceartificial intelligencetransformative learningengineering ethicsbusiness
• Enhancing Clinical Research with Synthetic Patient Data: Leveraging ChatGPT for Improved Diagnostic Models
  Recent strides in machine learning (ML) and generative artificial intelligence (GenAI) are transforming clinical research, opening new possibilities for privacy-conscious, data-driven insights into diagnosis, treatment and patient care.In clinical research, da…
  crossref Mahnoor Mughal 2024-12-23T06:09:24Z 置信度 0.82 computer sciencedata sciencetransformative learninghealth caregenerative grammar
• Benchmarking Machine Learning Missing Data Imputation Methods in Large-Scale Mental Health Survey Databases
  Abstract Databases with mental and behavioral health surveys suffer from missingness when participants skip the entire survey, affecting the data quality and sample size. We investigated the missing data patterns and evaluate the imputation performance in Simo…
  crossref Preethi Prakash, Kelly Street, Shrikanth Narayanan, Bridget A. Fernandez 等 2024-05-15T02:41:15Z 置信度 0.82 mental healthpsychologyneurosciencemedlinemedicine
• Machine-Learning-Assisted Impedance Component Analysis Enables Standardizable Surface Protein Analysis of Extracellular Vesicles Using Engineered Nanovesicles.
  Characterizing surface protein heterogeneity on extracellular vesicles remains challenging but essential for understanding their biological functions and clinical applications. Here, this study introduces an integrated platform that combines engineered cell-de…
  europepmc 2026 置信度 0.94 cs.lgbiologyartificial intelligencecomputational biologydrug discovery
• Machine Learning Models for Predicting Pain, Fatigue, Depression, Anxiety, and Malnutrition in Cancer Patients: A Systematic Review and Meta-Analysis.
  Introduction Cancer-related symptoms including pain, fatigue, depression, anxiety, and malnutrition drive poor quality of life and adverse clinical outcomes in cancer patients. While machine learning (ML) models are increasingly developed to predict these symp…
  europepmc 2026 置信度 0.94 medicinemedlinescience policysystematic reviewbiotechnology
• Integrating Environmental Exposure Profiles with Temporal Transcriptomics: An Explainable Machine Learning Model for Cardiovascular Disease Risk Prediction and Biological Validation.
  Background Cardiovascular disease (CVD) pathogenesis is strongly associated with environmental exposures and metabolic factors. This study aimed to investigate the relationship between urinary levels of volatile organic compounds (VOCs), heavy metals, and seru…
  europepmc 2026 置信度 0.94 medicinebiological sciences not elsewhere classifiedbiologybiotechnologybiological sciences
• Comparison of Machine Learning Models for Predicting Recurrent Lumbar Disc Herniation After Percutaneous Endoscopic Lumbar Discectomy.
  Background : Recurrent lumbar disc herniation (rLDH) significantly impairs outcomes following percutaneous endoscopic lumbar discectomy (PELD). Accurate individualized risk prediction remains challenging. This study aimed to develop and compare multiple machin…
  europepmc 2026 置信度 0.94 medicineartificial intelligencebiotechnologycomputer sciencecs.lg
• Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis.
  Hirschsprung's disease (HD) is characterised by absence of ganglion cells in the distal large intestine, requiring accurate histopathological diagnosis. Conventional diagnostic methods are time-consuming, subjective, and demand specialised expertise. While art…
  europepmc 2026 置信度 0.94 medicineartificial intelligencecomputer sciencemedlinemedical physics
• From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024).
  Background The diagnosis of attention deficit hyperactivity disorder (ADHD) has traditionally relied on subjective clinical interviews. Recent years have witnessed a paradigm shift toward objective, data-driven diagnostics powered by artificial intelligence (A…
  europepmc 2026 置信度 0.94 artificial intelligencemedicinecomputer sciencedata sciencepsychology
• Identification and risk-factor analysis for individuals at high risk for keratoconus via machine learning and logistic regression.
  Purpose To evaluate keratoconus (KC) risk factors and to develop a machine-learning (ML) model for KC and myopia classification. Methods In this retrospective single-center cross-sectional study, demographic and lifestyle data from patients with KC and individ…
  europepmc 2026 置信度 0.94 medicineinternal medicinemedlinebiological sciences not elsewhere classifiedscience policy
• Interpretable Machine Learning for 30-Day Mortality in PIVSR: A Cohort Study.
  Objective Post-infarction ventricular septal rupture (PIVSR) is a fatal mechanical complication of acute myocardial infarction. We aimed to develop and interpret a machine learning (ML) model to predict 30-day mortality using routinely available clinical varia…
  europepmc 2026 置信度 0.94 medicinebiological sciences not elsewhere classifiedinternal medicineartificial intelligencecancer
• Integrating MR dynamic radiomics and clinical parameters for machine learning-based prediction of short-term response to induction chemotherapy in nasopharyngeal carcinoma.
  Background and purpose Accurate prediction of short-term response to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC) remains a critical unmet clinical need. We aimed to construct a multimodal, interpretable machine learning (ML) framework integr…
  europepmc 2026 置信度 0.94 medicineartificial intelligenceinternal medicinecomputer sciencebiological sciences not elsewhere classified
• Interpretable Machine Learning for Predicting Suboptimal 12-Month Growth Response to Recombinant Human Growth Hormone in Children with Idiopathic Short Stature: A Dual-Center External Validation Study.
  Background/Objectives : Individual responses to recombinant human growth hormone (rhGH) therapy in children with idiopathic short stature (ISS) vary substantially, limiting pretreatment decision-making. This study aimed to develop and externally validate an in…
  europepmc 2026 置信度 0.94 medicineartificial intelligencemachine learningcs.lginternal medicine
• Machine learning prediction of postoperative pulmonary infection in patients who underwent thoracoscopic lung cancer resection: a retrospective case-control study.
  Background Accurate identification of patients at high risk of pulmonary infection after thoracoscopic lung cancer resection is important for timely and targeted preventive measures. Methods for determining the risk of pulmonary infection after thoracoscopic l…
  europepmc 2026 置信度 0.94 medicineinternal medicinecancerbiological sciences not elsewhere classifiedintensive care medicine
• FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data.
  Background/Objectives : The precise identification of Alzheimer's disease (AD) stages through clinical data is crucial for early diagnosis and suitable therapy. This classification remains troublesome due to overlap in cognitive profiles across different phase…
  europepmc 2026 置信度 0.94 artificial intelligencemachine learningcomputer sciencecs.lgcomputer and information sciences
• Development of a prediction model for infectious mononucleosis using machine learning algorithms based on blood cell analysis parameters.
  Background Infectious mononucleosis (IM) presents with nonspecific clinical manifestations, leading to frequent misdiagnosis or delayed diagnosis, and is associated with potentially severe complications. Existing etiological diagnostic methods are characterize…
  europepmc 2026 置信度 0.94 medicinecomputer scienceartificial intelligencecs.lgbiological sciences not elsewhere classified
• MACHINE LEARNING VERSUS PENALISED LOGISTIC REGRESSION FOR PREDICTING IN-HOSPITAL MORTALITY IN RUPTURED ABDOMINAL AORTIC ANEURYSM: A COMPARISON USING A UNIFORM VALIDATION FRAMEWORK.
  Background Ruptured abdominal aortic aneurysm (rAAA) remains associated with substantial in-hospital mortality. Although machine-learning methods can model complex nonlinear relationships between admission characteristics and outcome, their incremental value o…
  europepmc 2026 置信度 0.94 medicineartificial intelligenceinternal medicineintensive care medicinebiological sciences not elsewhere classified
• Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.
  Background Significant liver fibrosis is often clinically silent but predicts adverse outcomes. We developed and externally validated an interpretable machine-learning (ML) framework using readily obtainable demographic, anthropometric, and clinical variables …
  europepmc 2026 置信度 0.94 medicineinternal medicineintensive care medicinemedlineartificial intelligence
• A Multidisciplinary Team-Based Large Language Model Framework for Predicting Postoperative Neurological Complications in Acute Type A Aortic Dissection: Model Development and Validation Study.
  Background Postoperative neurological complications (PNCs) after acute type A aortic dissection (ATAAD) surgery are clinically emergent and require multidimensional perioperative risk assessment. Large language models (LLMs) have shown potential in clinical pr…
  europepmc 2026 置信度 0.94 medicinecs.lgbiotechnologycs.aiinternal medicine
• Machine learning classification of prevalent chronic disease using multidimensional social, behavioral, and psychological determinants: A cross-sectional analysis of the 2024 national health interview survey.
  Background Chronic diseases account for approximately 90% of the $4.5 trillion annual healthcare expenditure in the United States. While traditional clinical risk factors have been extensively studied, the predictive utility of multidimensional social determin…
  europepmc 2026 置信度 0.94 medicinepsychologyscience policybiotechnologybiological sciences not elsewhere classified
• Machine learning-based prediction model for adverse pregnancy outcomes in women with gestational diabetes mellitus.
  Aims/introduction Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications. Investigating clinical risk factors for adverse pregnancy outcomes in women with GDM would help predict and prevent neonatal complications. We developed …
  europepmc 2026 置信度 0.94 medicinebiotechnologyinternal medicineartificial intelligencecs.lg
• A Systematic Review of Deep Learning and Machine Learning Applications in Longitudinal Multimodal Clinical Data.
  Artificial intelligence (AI) is increasingly applied in healthcare to support diagnosis and treatment by leveraging longitudinal multimodal data, despite persistent challenges such as missing data and heterogeneity. This systematic review, conducted in accorda…
  europepmc 2026 置信度 0.94 medicineartificial intelligencemedlinesystematic reviewhealth care
• Plasma Amino Acid Signatures Associated with Disease Progression and Hypertension in Autosomal Dominant Polycystic Kidney Disease: A Targeted Metabolomics and Machine Learning Approach.
  Background: Autosomal dominant polycystic kidney disease (ADPKD) is a clinically heterogeneous disorder often leading to end-stage renal disease (ESRD). Prognostication of disease progression remains a major clinical challenge. This study aimed to identify pla…
  europepmc 2026 置信度 0.94 medicineinternal medicinediseasepharmacologybiology
• Unsupervised Ensemble Learning for Active Drug-Induced Liver Injury Surveillance: Integrating Pharmacokinetic Burden With Enzyme Trajectories.
  Purpose Traditional drug-induced liver injury (DILI) surveillance relying on static laboratory thresholds frequently misses early kinetic evolution. To address this, a fundamentally drug-agnostic unsupervised ensemble framework, integrating pharmacokinetic (WC…
  europepmc 2026 置信度 0.94 medicinecomputer sciencescience policybiologycs.ai
• Construction and Validation of a Machine Learning Model Based on Clinical and Microbiomic Features for Predicting High Mucus Secretion in COPD.
  Objective To evaluate clinical and airway microbiome features of excessive mucus secretion (CMH) in COPD progression and apply machine learning for CMH status identification. Methods A total of 319 COPD patients from Changzhi People's Hospital (May 2020-March …
  europepmc 2026 置信度 0.94 medicineinternal medicinescience policybiological sciences not elsewhere classifiedintensive care medicine
• Novel indicator for colonoscopy insertion difficulty and its application in predictive modelling: a multicentre prospective study protocol in China.
  Background Colonoscopy is a cornerstone for the screening and diagnosis of colorectal diseases. Colonoscopy insertion difficulty is associated with examination quality, procedural efficiency and patient comfort. Cecal intubation time (CIT) is commonly used as …
  europepmc 2026 置信度 0.94 medicinepsychologymedicine and health sciencesmedlinesocial and behavioral sciences
• Machine-Learning Prediction of Contrast-Induced Encephalopathy After Neurointerventional Procedures.
  CIE is a rare yet severe complication following neurointerventional procedures, for which reliable noninvasive predictive tools remain lacking. This study aimed to develop and validate a ML model for predicting CIE using perioperative clinical and procedural d…
  europepmc 2026 置信度 0.94 medicineinternal medicinebiotechnologyartificial intelligencescience policy
• An Interpretable Machine Learning Model for Predicting in-Hospital Progression in Initially Mild Hypertriglyceridemia-Induced Acute Pancreatitis Using Clinical and Non-Contrast CT Features.
  Introduction Early identification of patients with initially mild hypertriglyceridemia-induced acute pancreatitis (HTG-AP) who are at risk of an unstable disease course remains challenging using conventional assessment alone. This study aimed to develop an int…
  europepmc 2026 置信度 0.94 medicineinternal medicineartificial intelligenceintensive care medicinemedical physics
• Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.
  Objective To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a p…
  europepmc 2026 置信度 0.94 medicineinternal medicinephysical medicine and rehabilitationartificial intelligencemedline
• Optimizing Laboratory Autoverification in Complete Blood Count Testing Using Machine Learning: A Performance Evaluation Study.
  Background Autoverification improves laboratory efficiency by reducing manual reviews; however, rule-based systems may lack flexibility, particularly in complete blood count (CBC) testing. Machine learning (ML) can potentially enhance performance by recognizin…
  europepmc 2026 置信度 0.94 medicinecomputer sciencescience policybiological sciences not elsewhere classifiedinternal medicine
• Study on multi-modal feature fusion machine learning classification model of Chinese and Western medicine for depressive symptoms in middle-aged and older patients with chronic low back pain.
  Objective To construct a machine learning classification model integrating multi-modal features of Chinese and Western medicine for screening the risk of depressive symptoms in middle-aged and older patients with chronic low back pain (CLBP). Methods A cross-s…
  europepmc 2026 置信度 0.94 medicinescience policyinternal medicinebiological sciences not elsewhere classifiedcancer
• The role of "red flags" in the diagnostic work-up of hereditary transthyretin amyloidosis: a study using a machine-learning approach.
  Introduction Hereditary transthyretin amyloidosis (ATTRv) is a rare progressive, potentially life-threatening multisystem disorder caused by mutations in the transthyretin (TTR) gene, with variable penetrance and heterogeneous phenotypes, often leading to diag…
  europepmc 2026 置信度 0.94 medicineartificial intelligencecomputer sciencebiological sciences not elsewhere classifiedpsychology
• Machine Learning Prediction of Local Failure in GI Brain Metastases on the Day of Gamma Knife Radiosurgery.
  Background Time to local failure after stereotactic radiosurgery (SRS), including Gamma Knife radiosurgery (GKRS), for gastrointestinal (GI) brain metastases is difficult to predict and may vary substantially across tumors and patients. We developed and intern…
  europepmc 2026 置信度 0.94 medicineinternal medicinebiological sciences not elsewhere classifiedcancercomputer science
• Construction and Validation of a 90-Day Mortality Risk Prediction Model Based on Interpretable Machine Learning for Acute Ischemic Stroke Patients Undergoing Mechanical Thrombectomy.
  Background/Objectives : The accurate prediction of postprocedural mortality is critical for clinical decision-making; however, research on mortality risk models for patients undergoing mechanical thrombectomy remains limited. This study aimed to develop and va…
  europepmc 2026 置信度 0.94 medicineartificial intelligencebiological sciences not elsewhere classifiedcomputer scienceinternal medicine
• Development of an interpretable machine learning model for predicting intestinal necrosis in older patients with incarcerated hernia: A retrospective cohort study.
  ObjectiveTo develop a machine learning model for predicting intestinal necrosis in older patients with incarcerated hernia and interpret the model using SHapley additive exPlanations (SHAP) to assist clinicians in early identification of intestinal necrosis an…
  europepmc 2026 置信度 0.94 medicinebiological sciences not elsewhere classifiedinternal medicineintensive care medicineartificial intelligence
• Machine learning-derived serum carbon dioxide trajectories in predicting prognosis of patients with acute aortic dissection.
  JOURNAL/mgres/04.03/01612956-990000000-00114/figure1/v/2026-08-19T154102Z/r/image-tiff Venous partial pressure of carbon dioxide (PvCO2) indicates postoperative adverse outcomes in patients with acute aortic dissection, and its trajectories over time affecting…
  europepmc 2026 置信度 0.94 medicineinternal medicinebiotechnologymedlineintensive care medicine
• Integrating Complete Blood Count Parameters with Demographic Characteristics for Obstructive Sleep Apnea Prediction in Chinese Adults: A Machine Learning Approach.
  Purpose To develop and validate machine learning models integrating complete blood count (CBC) parameters with demographic characteristics for obstructive sleep apnea (OSA) risk stratification in adults with suspected OSA referred to a tertiary sleep clinic in…
  europepmc 2026 置信度 0.94 medicineartificial intelligencecomputer sciencebiotechnologypsychology
• Predictive Risk Models for Frailty Onset in Older Adults: A Scoping Review of Methodological Trends, Model Performance, and Clinical Translation Gap.
  Purpose Identifying older adults at risk of frailty is crucial for early intervention. Although numerous prediction models have emerged, no scoping review has systematically mapped their methodological trends and barriers to clinical implementation. This scopi…
  europepmc 2026 置信度 0.94 medicinemedlineartificial intelligencesystematic reviewmedicine and health sciences
• Prediction of pediatric lower urinary tract dysfunction using uroflowmetry data and machine learning with explainable AI techniques.
  Purpose To develop and evaluate a machine learning-based approach for the interpretation of uroflowmetry in pediatric lower urinary tract dysfunction, aiming to reduce interobserver variability and improve diagnostic consistency while maintaining clinical inte…
  europepmc 2026 置信度 0.94 medicineartificial intelligencemedlinecs.lgscience policy
• 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 0.94 medicineinternal medicinephysical medicine and rehabilitationintensive care medicinemedline
• 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 0.82
• 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 0.94 medicinebiological sciences not elsewhere classifiedinternal medicinecancerscience policy
• 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 0.94 psychologymedicinemental healthpsychological interventionmedline
• 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 0.94 medicineartificial intelligenceinternal medicinebiological sciences not elsewhere classifiedbrain-computer interface
• 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 0.94 medicinemedlineinternal medicinebiological sciences not elsewhere classifiedscience policy
• 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 0.94 medicinecs.lgbiotechnologymachine learningartificial intelligence
• 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 0.94 medicineinternal medicinemedlineartificial intelligencecancer
• 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 0.94 medicineartificial intelligencescience policybiological sciences not elsewhere classifiedbiotechnology
• 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 0.94 medicineinternal medicineintensive care medicinescience policymedline
• 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 0.94 medicinebiological sciences not elsewhere classifiedbiologybiotechnologygenetics
• 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 0.94 medicinemedlinesystematic reviewhealth carescience policy
• 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 0.94 medicineartificial intelligencecomputer sciencescience policybiological sciences not elsewhere classified
• 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 0.94 medicineartificial intelligencecomputer sciencepsychologydata science
• 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 0.94 medicinepsychologymedlinescience policybiological sciences not elsewhere classified
• 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 0.94 medicinecancerscience policymedlineintensive care medicine
• 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 0.94 medicineartificial intelligencecomputer sciencemachine learningmedline
• 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 0.94 medicineartificial intelligenceinternal medicinescience policycomputer science
• 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 0.94 medicineinternal medicineartificial intelligenceintensive care medicinecancer
• 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 0.94 medicineartificial intelligencebiological sciences not elsewhere classifiedbiotechnologyinternal medicine
• 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 0.94 medicineartificial intelligencecomputer scienceinternal medicinebiological sciences not elsewhere classified
• 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 0.94 medicinecognitiondiseaseinterpretabilityintensive care medicine
• 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 0.94 medicineartificial intelligenceinternal medicinebiological sciences not elsewhere classifiedbiotechnology
• 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 0.94 medicineartificial intelligencemedlinecomputer sciencemachine learning
• 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 0.94 medicineartificial intelligencebiological sciences not elsewhere classifiedbiotechnologyscience policy
• 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 0.94 pharmacologymedicinecancerbiochemistrygenetics
• 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 0.94 medicineartificial intelligencemachine learningbiological sciences not elsewhere classifiedcs.lg
• 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 0.94 medicineartificial intelligencecomputer sciencemachine learningcomputer and information sciences
• 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 0.94 medicineinternal medicinebiotechnologycancerintensive care medicine
• 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 0.94 medicineartificial intelligenceinternal medicineintensive care medicinephysical medicine and rehabilitation
• 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 0.94 medicinebiological sciences not elsewhere classifiedinternal medicinebiotechnologycancer
• 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 0.94 medicineinternal medicinecancerartificial intelligencebiological sciences not elsewhere classified
• 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 0.94 medicineartificial intelligencecomputer sciencemedlinescience policy
• 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 0.94 medicineartificial intelligencecomputer scienceinternal medicinemachine learning
• 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 0.94 medicineartificial intelligencemachine learningcs.lgcomputer science
• 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 0.94 medicineinternal medicineartificial intelligenceintensive care medicinebiological sciences not elsewhere classified
• 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 0.94 medicineinternal medicineintensive care medicineartificial intelligencebiological sciences not elsewhere classified
• 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 0.94 medicineartificial intelligenceinternal medicinecs.lgbiology
• 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 0.94 medicinescience policyartificial intelligencebiological sciences not elsewhere classifiedinternal medicine
• 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 0.94 medicineartificial intelligencebiological sciences not elsewhere classifiedcomputer sciencescience policy
• 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 0.94 medicineinternal medicinephysical medicine and rehabilitationmedlinebiological sciences not elsewhere classified
• 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 0.94 medicineartificial intelligencecomputer scienceinternal medicinebrain-computer interface
• 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 0.94 medicineartificial intelligencebiological sciences not elsewhere classifiedmedlinecancer
• 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 0.94 medicineinternal medicineartificial intelligencebrain-computer interfacemachine learning
• 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 0.94 medicineartificial intelligencebiological sciences not elsewhere classifiedbiotechnologycomputer science
• 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 0.94 medicineartificial intelligenceinternal medicinepsychologygeology
• 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 0.94 medicineinternal medicinebiological sciences not elsewhere classifiedintensive care medicinephysical medicine and rehabilitation
• 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 0.94 medicinemedlinescience policyinternal medicinebiological sciences not elsewhere classified
• 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 0.94 artificial intelligencemedicinemachine learningcomputer sciencecs.lg
• 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 0.94 medicineartificial intelligencecomputer and information sciencescancercs.lg
• 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 0.94 medicineartificial intelligencescience policymedical physicscomputer science
• 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 0.94 medicinemedlinecancerinternal medicineartificial intelligence
• 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 0.94 medicinebiological sciences not elsewhere classifiedartificial intelligencebiotechnologyinternal medicine
• 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 0.94 medicineinternal medicineartificial intelligenceintensive care medicinecancer
• 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 0.94 medicineinternal medicineartificial intelligencemedlineintensive care medicine
• 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 0.94 medicinepsychologybiological sciences not elsewhere classifiedscience policymedline
• 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 0.94 medicinemedlineinternal medicinemedical physicspharmacology
• 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 0.94 medicinepsychologyartificial intelligencescience policybiotechnology