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• Artificial Intelligence in Engineering
  Artificial intelligence (AI) has moved past its primitive stages and is now poised to revolutionize various fields, making it a disruptive technology. This technology is expected to completely transform traditional engineering in design, electrical, communicat…
  openalex Mohamed Khaleel, Abdussalam Ali Ahmed, Abdulgader Alsharif 2023-03-13 置信度 0.72 Artificial intelligenceComputer scienceEngineering design processApplications of artificial intelligenceManagement science
• The Ethics of Artificial Intelligence for Intelligence Analysis: a Review of the Key Challenges with Recommendations
  Intelligence agencies have identified artificial intelligence (AI) as a key technology for maintaining an edge over adversaries. As a result, efforts to develop, acquire, and employ AI capabilities for purposes of national security are growing. This article re…
  openalex Alexander Blanchard, Mariarosaria Taddeo 2023-04-01 置信度 0.72 Key (lock)AccountabilityEngineering ethicsPolitical scienceComputer science
• Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review
  Background: Significant advancements in the field of information technology have influenced the creation of trustworthy explainable artificial intelligence (XAI) in healthcare. Despite improved performance of XAI, XAI techniques have not yet been integrated in…
  openalex Jinsun Jung, Hyungbok Lee, Hyunggu Jung, Hyeoneui Kim 2023-05-01 置信度 0.72 Quality (philosophy)Health careTask (project management)Identification (biology)Field (mathematics)
• Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?
  Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from "training data," that can be subseque…
  openalex Anastasia Fotaki, Esther Puyol‐Antón, Amedeo Chiribiri, René M. Botnar 等 2022-01-10 置信度 0.72 BiobankArtificial intelligenceField (mathematics)Data scienceDeep learning
• Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management
  Companies neither fully exploit the potential of Artificial Intelligence (AI), nor that of Machine Learning (ML), its most prominent method. This is true in particular of marketing, where its possible use extends beyond mere segmentation, personalization, and …
  openalex Gioia V. Volkmar, Peter Mathias Fischer, Sven Reinecke 2022-06-01 置信度 0.72 MarketingDelphi methodPersonalizationKnowledge managementExploit
• Transforming Health Care With Artificial Intelligence: Redefining Medical Documentation
  The growing burden of medical documentation, particularly with the widespread adoption of electronic health records (EHRs), has contributed to physician burnout and decreased job satisfaction.1 The intricate structure of medical notes, combined with the time-c…
  openalex Archana Reddy Bongurala, Dhaval Save, Ankit Virmani, Rahul Kashyap 2024-05-22 置信度 0.72 DocumentationHealth careMedicineNursingMedical education
• Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery
  This article proposes a new grand challenge for AI: to develop an AI system that can make major scientific discoveries in biomedical sciences and that is worthy of a Nobel Prize. There are a series of human cognitive limitations that prevent us from making acc…
  openalex Hiroaki Kitano 2016-03-01 置信度 0.72 Scientific discoveryIntuitionData scienceComputer scienceProcess (computing)
• Capabilities of GPT-4 on Medical Challenge Problems
  Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency ex…
  openalex Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan 等 2023-03-20 置信度 0.72 Benchmark (surveying)LicensureUnited States Medical Licensing ExaminationComputer scienceCalibration
• Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review
  Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost…
  openalex Miguel Jiménez Pérez, Rocío González Grande 2020-09-27 置信度 0.72 Hepatocellular carcinomaMedicineCarcinomaPathologyOncology
• Deep Learning Models for Retinal Blood Vessels Segmentation: A Review
  This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis. Many eye diseases often lead to blindness in the absence of proper clinical diagnosis and medical treatment. For example, diabetic retinopat…
  openalex Toufique Ahmed Soomro, Ahmed J. Afifi, Lihong Zheng, Shafiullah Soomro 等 2019-01-01 置信度 0.72 Deep learningComputer scienceArtificial intelligenceSegmentationImage segmentation
• Artificial Intelligence and Machine Learning Applications in Antimicrobial Resistance Research: A Systematic Review
  Abstract Background Antimicrobial resistance (AMR) represents an existential global health crisis, causing over one million deaths yearly all over the world. Conventional culture-based susceptibility testing requires 48–72 hours, an interval insufficient for g…
  preprints 2026 置信度 0.74
• Explainable machine learning for clinical phenotyping and mortality prediction in critically ill patients with cancer
  Abstract Critically ill patients with cancer are clinically heterogeneous, limiting the prognostic performance of conventional severity scores. This multicenter retrospective study used MIMIC-IV for model derivation and the eICU Collaborative Research Database…
  preprints 2026 置信度 0.74
• Development and Validation of Interpretable Machine Learning Models for Low Birth Weight in Ethiopia: A Secondary Analysis of the Ethiopian Demographic and Health Survey
  Background Low birth weight remains a primary driver of neonatal and infant mortality in Ethiopia. Machine learning models can assist early risk identification, yet clinical adoption is often limited by black box algorithms and late pregnancy predictor variabl…
  preprints 2026 置信度 0.74
• Comparative Performance of Classical Statistical and Machine Learning Models for Melanoma Classification
  Early and accurate classification of melanoma is essential for improving patient outcomes and supporting clinical decision-making. Although numerous predictive models have been proposed, comparisons between classical statistical approaches and modern machine l…
  preprints 2026 置信度 0.74
• Development and Validation of Machine Learning Models for Predicting 13 or More Sections in Mohs Micrographic Surgery
  Background Cases requiring 13 or more tissue sections in Mohs micrographic surgery (MMS) demand extended operative time, additional resources, and often specialised closure techniques. Pre-operative identification of such cases would improve surgical schedulin…
  preprints 2026 置信度 0.74
• Development and Validation of a Machine Learning Model Integrating SPECT MPI Multiparametric Features for Risk Prediction in Coronary Artery Disease with Preserved Ejection Fraction
  Abstract Objective Early identification of high-risk patients with coronary artery disease (CAD) and preserved left ventricular ejection fraction (LVEF) is essential for optimizing management. This study aimed to develop a machine learning model integrating cl…
  preprints 2026 置信度 0.74
• Sexual Dysfunction Risk Assessment in Peritoneal Dialysis: A Machine Learning Study
  Abstract Background: Sexual dysfunction (SD) is a common yet underrecognized complication in patients receiving peritoneal dialysis (PD). We investigated the prevalence of SD and its associated factors, and developed interpretable machine learning models to pr…
  preprints 2026 置信度 0.74
• A Machine Learning Approach for Predicting 28-Day ICU Mortality in ARDS Patients Based on the New Global Definition
  Abstract Background Acute respiratory distress syndrome (ARDS) is a life-threatening form of acute diffuse lung injury that can be triggered by various factors, including pneumonia, sepsis, trauma, and other etiologies. The definition of ARDS has undergone mul…
  preprints 2026 置信度 0.74
• Feature-Optimized Hybrid AI Models for Explainable and Generalizable Epilepsy Prediction
  Abstract Epilepsy a neurological condition where individuals experience recurring seizures, which developing accurate, timely prediction methods for the purpose of optimal clinical care. Machine learning has achieved some positive results in this area; however…
  preprints 2026 置信度 0.74
• How Best to Explain Machine Learning Models to Clinicians: A User Study of Explanation Types
  Background Explanations play a crucial role in helping clinicians understand how black-box machine learning models make predictions in clinical settings. Several different types of explanations have been developed, each corresponding to a unique approach for c…
  preprints 2026 置信度 0.74
• Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia
  Acute myeloid leukemia (AML) produces more molecular, imaging, and clinical data per patient than any hematologist can hold in mind at once, and each revision of the WHO, ICC, and European LeukemiaNet (ELN) frameworks adds to the load. Artificial intelligence …
  preprints 2026 置信度 0.74
• Machine Learning Model Integrating Clinical Features, CT Radiomics, and Transfer Learning for Predicting Aggressive Recurrence of Hepatocellular Carcinoma
  Abstract Objective To predict aggressive recurrence of hepatocellular carcinoma (HCC) after curative resection, this study aimed to develop a preoperative noninvasive prediction model by integrating clinical features, radiomics, and transfer learning–based dee…
  preprints 2026 置信度 0.74
• Machine learning approach to evaluate the significance of clinical factors and laboratory markers in predicting postpartum hemorrhage
  Abstract Background Identifying risk factors is the crucial step in preventing postpartum hemorrhage (PPH). Integrating laboratory, demographic, and real-time intrapartum data into predictive models allows clinical teams to move from broad risk categories to s…
  europepmc 2026 置信度 0.80
• Machine Learning-Based Identification of Key Predictors of Estimated Glomerular Filtration Rate in Older Taiwanese Women
  Abstract Background : Estimated glomerular filtration rate (GFR) is widely used to assess kidney function but is primarily derived from serum creatinine, which is affected by age, sex, muscle mass, nutrition, inflammation, and fluid status. We aimed to identif…
  preprints 2026 置信度 0.74
• Development of a Predictive Model for Lymph Node Metastasis in Endometrial Cancer Based on Integrated Clinical Characteristics, Serum Tumor Biomarkers, and Related Factors
  Abstract Objective To investigate the expression levels of serum tumor biomarkers, including carbohydrate antigen 125 (CA125), human epididymis protein 4 (HE4), and carbohydrate antigen 19 − 9 (CA199), as well as other related clinical characteristics in patie…
  preprints 2026 置信度 0.74
• Multimodal Machine Learning for Predicting Outcomes in the PASS-01 Trial of Systemic Therapy for Metastatic Pancreatic Cancer
  Purpose Modified FOLFIRINOX (FFX) and gemcitabine plus nab-paclitaxel (GNP) are standard first-line treatments for metastatic pancreatic ductal adenocarcinoma (PDAC), but no validated biomarker guides treatment selection. We developed MULTIPL, a multimodal mac…
  preprints 2026 置信度 0.74
• Estimating VO2max in patients with obesity using machine learning: A retrospective observational cohort study
  Abstract This study investigated the use of machine learning models to estimate maximal oxygen consumption (VO 2max ) in adults with obesity, aiming to create an accessible alternative to traditional VOmax tests that require intensive protocols and specialized…
  preprints 2026 置信度 0.74
• Validation of clinical diagnosis and machine learning classification of cognitive impairment
  Structured Abstract INTRODUCTION Cognitive syndrome diagnosis (Normal, Mild Cognitive Impairment (MCI), Dementia) is important for summarizing disease status and predicting future progression. Machine learning approaches to classification might substitute for …
  preprints 2026 置信度 0.74
• Construction and Cross-Cohort Validation of Survival Prediction Models for Prostate Cancer: An Interpretable Machine Learning Study Based on SEER and Local Chinese Clinical Data
  Abstract Objective This investigation sought to develop interpretable machine learning(ML) frameworks for forecasting overall survival (OS) in prostate cancer patients, thereby enhancing prognostic transparency to facilitate individualized clinical decision-ma…
  preprints 2026 置信度 0.74
• Predicting Human Salmonella Antimicrobial Resistance from Poultry Antibiotic Residues: A Simulation-Based Machine Learning Proof-of-Concept with One Health Integration
  Abstract Background. Antimicrobial resistance (AMR) in foodborne pathogens such as Salmonella enterica represents a critical One Health challenge. Sub-therapeutic antibiotic residues in poultry meat are hypothesized to exert continuous selective pressure. Yet,…
  preprints 2026 置信度 0.74
• Adversarial Validation Reveals Diagnostic Workflow Leakage in PCOS Machine Learning Models
  Background Machine-learning models for polycystic ovary syndrome (PCOS) and other conditions frequently report near-perfect diagnostic performance, but retrospective datasets assembled from routine clinical practice can encode diagnostic-group membership in ho…
  preprints 2026 置信度 0.74
• Single-Molecule Proteomics via a Dynamic Translocase and Physics-Informed Machine Learning
  Single-molecule protein sequencing promises to democratize clinical proteomics, but platforms retrofitting static DNA-sequencing nanopores face a fundamental biophysical bottleneck: they only measure one-dimensional excluded volume. Consequently, these static …
  preprints 2026 置信度 0.74
• Platelet-to-Lymphocyte Ratio-Driven Machine Learning Model for Predicting In-Hospital Major Adverse Cardiovascular Events After Percutaneous Coronary Intervention in Acute Myocardial Infarction: A Retrospective Cohort Study
  Abstract Background Accurate risk assessment of major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) is central to optimizing clinical decision-making. Although inflamm…
  preprints 2026 置信度 0.74
• Development and preliminary external validation of a machine learning-based model for predicting 28-day mortality in non-HIV patients with Pneumocystis jirovecii pneumonia: a two-centre retrospective study
  Abstract Background Non-HIV patients with Pneumocystis jirovecii pneumonia (PJP), particularly those with immunocompromising conditions, are at risk of rapid clinical deterioration and short-term death. Early prognostic assessment remains difficult because out…
  preprints 2026 置信度 0.74
• A Fractured Promise: How Socioeconomic Status Determines the Mental Health Returns of Higher Education
  Abstract Background Education is a key socioeconomic determinant of health. This study aimed to investigate the association between education level and the risk of depression among U.S. adults, elucidate its mediating mechanisms, and develop an interpretable m…
  preprints 2026 置信度 0.74
• Development and Validation of Machine Learning Models for Early Prediction of Methicillin-resistant Staphylococcus aureus-associated Sepsis
  Abstract Background Sepsis secondary to methicillin-resistant Staphylococcus aureus (MRSA) infection is associated with considerable morbidity and mortality. Early stratification of patients at high risk remains essential for optimizing clinical outcomes. The …
  preprints 2026 置信度 0.74
• Development of a Multiclass Predictive Baseline for Diverticulum Ultrasound Imaging Types Using Nonlinear Machine Learning
  Abstract Context: Diverticulum anomalies exhibit a range of serious clinical manifestations of a similar nature requiring accurate imaging classification to implement appropriate and targeted management. However, clinical data are often characterized by severe…
  preprints 2026 置信度 0.74
• Development and internal validation of a pre-operative machine-learning model for prolonged postoperative stay in older adults undergoing colorectal cancer surgery
  Abstract Purpose Older adults undergoing colorectal cancer surgery frequently have frailty, multimorbidity and social-discharge complexity, and prolonged admission exposes them to hospital-associated disability, deconditioning and delirium. We developed and in…
  preprints 2026 置信度 0.74
• Machine Learning-Based Early Detection of Heart Disease Risk Using Self-Reported Non-Clinical Variables
  Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for accessible tools for early risk assessment. This study investigates the use of established machine learning (ML) techniques to predict heart disease risk from sel…
  preprints Fernando Martín-Rodríguez, Monica Fernandez-Barciela, Ainhoa Morales-Fernendez, Maria Marante Boado 2026 置信度 0.74
• Interpretable machine learning analysis of apelin-12 for major adverse cardiovascular events after primary PCI in ST-elevation myocardial infarction
  Abstract Major adverse cardiovascular events (MACE) remain common after successful primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI), and conventional regression-based models may miss non-linear biomarker-risk asso…
  preprints 2026 置信度 0.74
• Singular model selection for trustworthy label-free classifier evaluation
  Abstract Trustworthy deployment of a machine learning classifier requires honest uncertainty about its measured performance, including the common case where that performance is estimated without ground-truth labels by pooling several imperfect raters or models…
  preprints 2026 置信度 0.74
• Multiscale Multimodal Connectomic Signatures of Methamphetamine- Predominant Use and Methadone-Maintained Status: mCCA+jICA Fusion and Interpretable Machine Learning
  Abstract Background: Methamphetamine-predominant substance use may involve distributed alterations in structural, functional, dynamic, and topological brain networks. Methods: We analyzed multimodal magnetic resonance imaging data from 110 adult men: 35 health…
  preprints 2026 置信度 0.74
• Revisiting Semantic Markers of Schizophrenia: A Machine Learning Analysis of Verbal Fluency Features
  Abstract Schizophrenia is a chronic mental illness that affects thought processes, emotional regulation, and behavior. Language impairment is one its suspected cognitive signs, including abnormalities in semantic organization and verbal fluency. Prior research…
  preprints 2026 置信度 0.74
• Applying Machine Learning for the Prediction of Tuberculosis Among People Living with HIV from Health Facilities in Addis Ababa, Ethiopia
  Abstract Background Tuberculosis (TB) is a major global public health problem and one of the leading causes of death worldwide, particularly in the era of Human Immunodeficiency Virus (HIV). TB is among the leading causes of death for people living with HIV (P…
  preprints 2026 置信度 0.74
• Machine learning models for predicting multidrug-resistant organism infections after invasive procedures in intensive care unit patients: a retrospective study based on the MIMIC-IV database
  Abstract Objective This study aimed to develop and compare five machine learning models utilizing the MIMIC-IV database to identify key risk factors for multidrug-resistant organism (MDRO) infection in patients with invasive procedures. Methods A cohort of 23 …
  preprints 2026 置信度 0.74
• A Two-Stage Ensemble Machine Learning Pipeline for Breast Cancer Diagnosis from Digital Mammograms
  Background: Breast cancer is the most common cancer among women, and early detection through mammography is essential for reducing mortality. Artificial intelligence can support radiologists by improving diagnostic accuracy. Aim: To develop and evaluate a two-…
  preprints 2026 置信度 0.74
• Machine Learning-Supported Efficient VTE Risk Assessment using Routinely Collected Electronic Health Record Data
  Venous thromboembolism (VTE) is a leading cause of preventable inpatient mortality, while the real-world performance of mandated risk assessment and the potential for automating using electronic health record (EHR) data remain unclear. We analysed 577,904 admi…
  europepmc 2026 置信度 0.80
• Integrative Transcriptomic Analysis and Machine Learning Identify Robust Gene Expression Signatures for Cardiovascular Disease Classification
  Abstract BACKGROUND: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide and contribute substantially to the global health burden. Early diagnosis and the identification of reliable molecular biomarkers are critical for improving dis…
  preprints 2026 置信度 0.74
• Development and External Validation of a Treatment-Adjusted Machine Learning Model to Support Risk-Informed Group-Based Depression Care for People Living with HIV in Uganda
  Background: Group-based depression care is widely used in HIV services in Uganda, yet some patients remain symptomatic following treatment. We developed and externally validated a treatment-adjusted machine learning model to support risk-informed group-based d…
  preprints 2026 置信度 0.74
• Machine Learning-Based Risk Prediction Model for Sleep Disorders in Middle-Aged and Older Adults at High Altitudes
  Abstract This study developed and validated a machine learning model to predict sleep disorder risk in middle-aged and older adults in high-altitude regions. We enrolled 673 participants aged ≥45 years from Xining, Qinghai, China, collecting data on sociodemog…
  preprints 2026 置信度 0.74
• Development and External Validation of an Interpretable Machine-Learning Model for Predicting Synchronous Distant Metastasis in Invasive Bladder Cancer: A SEER Population-Based Study
  Abstract Background Synchronous distant metastasis—metastasis present at diagnosis—drastically worsens survival in bladder cancer, yet accurate pre-treatment prediction tools are lacking. We developed and validated a machine-learning model to predict synchrono…
  preprints 2026 置信度 0.74
• Early Detection of Erythropoietic Protoporphyria Using Sequential Machine Learning on Longitudinal Electronic Health Records
  Objective Erythropoietic protoporphyria (EPP) is a rare photodermatosis marked by multi-year diagnostic delays. We developed and externally validated machine learning models to identify patients with EPP earlier from longitudinal electronic health record (EHR)…
  preprints 2026 置信度 0.74
• Concentric Hypertrophy as a Prognostic Factor and Mediator of Diabetes-Related Risk in HFpEF: Development and Validation of Prediction Models Based on Cox Regression and Machine Learning Approaches Across Two Randomized Controlled Trials
  Abstract ‌Background: ‌ Although the prognostic significance of left ventricular mass index (LVMi) and relative wall thickness (RWT) has been established across a spectrum of cardiovascular diseases, their value in predicting outcomes in patients with heart fa…
  preprints 2026 置信度 0.74
• Cerebrospinal Fluid Chloride as an Independent Prognostic Biomarker in Cryptococcal Meningitis: A Dual-Center Externally Validated Machine Learning Study with SHAP-Based Interpretability
  Abstract Background Cryptococcal meningitis (CM) carries a high 10-week mortality rate, necessitating accurate early prognostic tools. While cerebrospinal fluid chloride (CSF-Cl) is a routine test, its independent prognostic value in CM remains ill-defined. Fu…
  preprints 2026 置信度 0.74
• Application Study of Interpretable Machine Learning Models for Predicting Postoperative Refracture After Vertebral Augmentation in Osteoporotic Vertebral Compression Fractures
  Abstract Background Osteoporotic vertebral compression fractures (OVCFs) are severe osteoporosis complications; vertebral augmentation is the preferred minimally invasive treatment, but postoperative refracture risk exists. Traditional logistic regression fail…
  preprints 2026 置信度 0.74
• Machine learning-based clinical, protemic and pathomics model to predict overall survival and therapeutic benefit in patients with cervical cancer
  Abstract Background The tumor-node-metastasis (TNM) staging system has limitations in predicting prognosis and guiding personalized therapy for cervical cancer (CC). This study aimed to develop a cost-effective, machine learning (ML)-based multi-modal prognost…
  preprints 2026 置信度 0.74
• A Weighted PRS for Ischemic Heart Disease: Candidate-Gene Associations and an Evaluation of Epistasis and Machine-Learning Approaches
  Abstract Ischemic heart disease (IHD) carries a substantial polygenic component, but candidate-gene and polygenic-score evidence remains scarce in South Asians. This Pakistani case–control study evaluated eight candidate variants, their aggregation into conven…
  preprints 2026 置信度 0.74
• A Cost-Sensitive and Explainable Evaluation Framework for Chronic Kidney Disease Prediction Models
  Abstract This paper evaluates Logistic Regression, Random Forest and Extreme Gradient Boosting models for CKD prediction using a framework that incorporates threshold optimization, cost-sensitive analysis and interpretability techniques to assess clinical depl…
  preprints 2026 置信度 0.74
• Prognostic Nutritional Index and Machine Learning for Mortality Prediction in Sepsis-Associated Acute Kidney Injury: A MIMIC-IV Cohort Study
  Abstract Objective The Prognostic Nutritional Index has demonstrated prognostic value in diverse clinical populations, yet its precise role in sepsis-associated acute kidney injury remains incompletely characterized. This study aimed to investigate the associa…
  preprints 2026 置信度 0.74
• Adaptive Agentic AI-Driven Women's Digital Twin for Personalized High-Risk Pregnancy Prediction and Clinical Decision Support
  High-risk pregnancy remains a major global public health challenge due to its association with increased maternal and neonatal morbidity and mortality. Existing prediction methods primarily rely on static clinical data and conventional machine learning techniq…
  preprints 2026 置信度 0.74
• Prognostic Utility of the Endothelial Activation and Stress Index in Critically Ill Patients with Heart Failure and Acute Kidney Injury: A Dual-Cohort and Machine Learning Validation Study
  Abstract Background Concomitant heart failure (HF) and acute kidney injury (AKI) in critically ill patients is associated with high mortality, underscoring the need for accurate early risk stratification. The Endothelial Activation and Stress Index (EASIX), de…
  preprints 2026 置信度 0.74
• Evaluation of risk prediction models for coronary artery disease: a systematic review, meta- analysis, and machine learning validation
  Abstract Background : Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide, and early diagnosis is essential to improving patient outcomes. Risk prediction models for CAD are increasingly recognized for their potential role in…
  preprints 2026 置信度 0.74
• A Machine Learning-Based Model for Cardiovascular Disease Risk Stratification in Middle-Aged and Older Adults with Diabetes: A Cross-Sectional Study based on CHARLS
  Abstract Background cardiovascular disease(CVD) is the leading cause of mortality among patients with diabetes mellitus(DM).The primary aim of this study was to develop a machine learning-based risk assessment model for CVD in the diabetic population.The model…
  preprints 2026 置信度 0.74
• Interpretable machine-learning models for Guillain-Barré syndrome subtype assignment using acute-phase nerve conduction study: a multicenter study
  Abstract Background Guillain-Barré syndrome (GBS) comprises clinically and electrophysiologically heterogeneous subtypes whose assignment may vary according to nerve conduction study (NCS) timing, serological availability, and examiner expertise. Interpretable…
  preprints 2026 置信度 0.74
• Predictors of Visual Acuity, Intraocular Pressure, and Pain in Neovascular Glaucoma: A Mixed-Effects Model and Machine Learning Cohort Analysis
  Background/Aims Neovascular glaucoma (NVG) is a severe, secondary glaucoma. This study aimed to identify factors associated with vision, intraocular pressure (IOP), and ocular pain outcomes. Methods The cohort included all patients diagnosed with NVG during 20…
  europepmc 2026 置信度 0.80
• Antimicrobial Stewardship in the Era of AI: A Head-to-Head Comparison of a Machine Learning HTL Algorithm and Large Language Models in Real-World Infectious Disease Cases
  Background: Persistently, antimicrobial resistance is one of the major global public health challenges, associated with 4.95 million deaths in 2019, of which 1.27 million were directly attributable to resistant bacterial infections. Among all types, urinary tr…
  preprints 2026 置信度 0.74
• Velocity Reflection Index and Chronological Age: Confounder-Adjusted Statistical and Machine-Learning Analyses of Carotid Doppler Waveforms
  Purpose To determine whether the velocity reflection index (VRI) is the carotid Doppler waveform feature most strongly associated with chronological age after adjustment for sex and exercise habit, and whether its feature ranking remains stable across cross-va…
  preprints 2026 置信度 0.74
• Artificial Intelligence and Machine Learning Applications in Antimicrobial Resistance Research: A Systematic Review
  Abstract Background Antimicrobial resistance (AMR) represents an existential global health crisis, causing over one million deaths yearly all over the world. Conventional culture-based susceptibility testing requires 48–72 hours, an interval insufficient for g…
  preprints 2026 置信度 0.74
• Beyond Diagnosis: A Scoping Review of Machine Learning and NLP for Multilevel Mental Health Factors in Population Crises
  Background: Natural disasters, pandemics, armed conflict, and economic upheaval inflict lasting harm on mental wellbeing, yet what determines who recovers and who deteriorates remains poorly understood across crisis types. Objectives: This review maps how cris…
  preprints 2026 置信度 0.74
• Statistical and Machine Learning Assisted Tribological Characterization of Mg (97.65Wt. %)-Ag (2.03Wt. %) - Mn (0.29Wt. %) - Zr (0.03Wt. %) Alloy for Orthopedic Osteosynthesis Implants
  Abstract Biodegradable magnesium (Mg)-based alloys have emerged as promising materials for temporary orthopedic osteosynthesis implants owing to their favorable mechanical properties, biocompatibility, and ability to degrade naturally after bone healing, there…
  preprints 2026 置信度 0.74
• Implementation and Study on Liver Cirrhosis Disease Diagnosis Prediction Using a Method for Machine Learning Algorithms: A Comparative Approach Analysis
  Abstract Background: We explored anonymized patient records from the 'Aadarshvelu Liver-Cirrhosis-Stage-Classification' Kaggle dataset, which is rich in varied laboratory and clinical details. By visualizing model performance through confusion matrices, we unc…
  preprints 2026 置信度 0.74
• Classifying CRISPR-Cas9 Off-Target Cleavage Sites from GUIDE-seq Data: A Class-Imbalanced Machine Learning Benchmark
  Off-target cleavage is a central safety concern for CRISPR-Cas9 genome editing, particularly in therapeutic applications where unintended double-strand breaks carry clinical risk. We benchmarked five machine learning classifiers — logistic regression on mismat…
  preprints 2026 置信度 0.74
• A Comprehensive Evaluation of Distributional Shift and Concept Drift Across Preprocessing Configurations, Domain Adaptation, and Representation Learning in GDM Prediction
  Abstract Gestational diabetes mellitus (GDM) screening tools trained on single-population cohorts routinely fail when deployed across clinical settings, yet the literature largely benchmarks models on internal test splits while treating cross-cohort evaluation…
  preprints 2026 置信度 0.74
• Prediction of New Onset Atrial Fibrillation in Patients with Acute Myocardial Infarction Using Artificial Intelligence and Machine Learning Algorithms
  Background: /Objectives: New-onset atrial fibrillation is a common complication of acute myocardial infarction and is associated with increased mortality, heart failure, and recurrent cardiovascular events. Prediction models for the occurrence of atrial fibril…
  preprints 2026 置信度 0.74
• Desilting 4.0: A Sky–Land–Water–AI Digital-Twin Framework for Low-Emission Multi-Contaminant Co-Solidification and Machine-Learning Foresight in El Niño-Ready Andean Rivers and Lagoons
  Abstract Descolmatation (desilting) of Andean rivers and lagoons in Peru routinely encounters sediment that is jointly loaded with heavy metals and organic pollutants, a co-contamination pattern that conventional single-binder, single-contaminant remediation d…
  preprints 2026 置信度 0.74
• Predicting Under-Five Child Stunting Using Machine Learning and Probability Calibration: A Nationally Representative 2024/25 EDHS Study from Ethiopia
  Abstract Background Child stunting remains a critical public health challenge in Ethiopia. Standard parametric frameworks fail to capture complex non-linear interactions among multidimensional determinants. This study evaluated six supervised machine learning …
  europepmc 2026 置信度 0.80
• Forecasting Trajectories of Physiological Mechanics with Sparse Clinical Data Using a Data Assimilation and Machine Learning Hybrid
  Clinical decisions for determining optimal patient-specific interventions are complicated prediction tasks that rely on health care professionals’ understanding of physiological mechanisms and their dynamics. These decisions are challenged by (a) observational…
  preprints 2026 置信度 0.74
• Machine learning-based prediction of preeclampsia: a comparative study on model effectiveness
  Abstract Background: Preeclampsia remains a leading cause of maternal and perinatal morbidity in low- and middle-income countries (LMICs). As preparatory methodological work for an AI-powered population-based birth cohort in Sri Lanka, this study systematicall…
  preprints 2026 置信度 0.74
• Clinlabomics-Based Machine Learning Models for Risk Warning of Multi-Type Gastrointestinal Cancers
  Abstract Gastrointestinal malignancies pose a major public health challenge in China, where the majority of patients are diagnosed late and face poor survival outcomes. A clinlabomics data-based auxiliary screening tool could address this gap, yet previous stu…
  preprints 2026 置信度 0.74
• PredictStroke: A Two-Stage Modular System Combining Machine Learning Ensemble Risk Prediction and Multimodal Symptom Triage Support
  Abstract Introduction Early stroke detection and long-term risk assessment are critical components of preventative health and acute triage. Existing artificial intelligence solutions typically focus on either predictive modeling using tabular data or real-time…
  preprints 2026 置信度 0.74
• Beyond BMI: Chinese Visceral Adiposity Index (CVAI) improves machine learning prediction of 9-year cardiometabolic multimorbidity in the CHARLS cohort
  Abstract Background Driven by rapid population aging and evolving lifestyle patterns across China, middle-aged and older individuals are at substantially elevated risk of cardiovascular disease (CVD) and diabetes mellitus (DM). The two disorders often co-occur…
  preprints 2026 置信度 0.74
• Integrating Triaxial IMU Sensors and Ensemble Learning for Effective Parkinson Disease Severity Classification
  Abstract Parkinson disease (PD) is a progressive neurodegenerative disease that can have a significant impact on motor performance, resulting in the appearance of symptoms such as tremors, rigidity, postural instabilities and bradykinesia. Timely clinical trea…
  preprints 2026 置信度 0.74
• Machine Learning Models for Osteoporosis Prediction: A Systematic Review and Meta-Analysis
  ABSTRACT Purpose The application of machine learning (ML) to osteoporosis prediction has expanded rapidly, yet no comprehensive meta-analysis has synthesized the discriminative performance of these models across all ML categories, data types, and validation st…
  preprints 2026 置信度 0.74
• Machine Learning Prognostic Stratification for Treatment Decision Support in Elderly Gastric Adenocarcinoma: A SEER Analysis with External Clinical Validation
  Abstract Background : Elderly patients (≥65) constitute >60% of gastric adenocarcinoma cases yet are underrepresented in clinical trials. Machine learning survival models may capture complex prognostic interactions, but comprehensive benchmarking with external…
  preprints 2026 置信度 0.74
• An Explainable AI-Driven Classification Framework for Parkinson’s Disease Detection via Acoustic Speech Features: A Comparative Machine Learning Study
  Parkinson’s Disease (PD) is a progressive neurodegenerative disorder which significantly affects motor function, daily coordination and verbal communication. Speech-based biomarkers provide a non-invasive and scalable approach to early detection, as dysphonia …
  preprints 2026 置信度 0.74
• Machine Learning-Based Diagnostic Model for Preoperative Anxiety in Elderly Patients with Hip Fractures and SHAP Analysis
  Abstract Aims Preoperative anxiety significantly affects the treatment prognosis of elderly patients with hip fractures. Nevertheless, owing to a combination of factors — such as patients' low educational attainment and trauma-related pain — the diagnosis and …
  preprints 2026 置信度 0.74
• User Interfaces in Machine Learning-Based Decision Support for Emergency Department Triage – A Systematic Review
  Abstract Background Triaging is a promising application for machine learning-based clinical decision support systems (CDSSs). However, despite considerable research attention, their real-world impact remains limited, with low adoption and implementation challe…
  preprints 2026 置信度 0.74
• Development of a Prognostic Index for Clear Cell Renal Cell Carcinoma Using Machine Learning and Multi-omics Analysis to Enhance Clinical Outcomes and Drug Sensitivity
  Abstract Background Clear cell renal cell carcinoma (KIRC) represents the major malignant subtype of kidney cancer and shows pronounced biological heterogeneity. For patients with advanced disease, therapeutic resistance and the lack of stable prognostic marke…
  preprints 2026 置信度 0.74
• Machine Learning Predicts Acute Kidney Injury in Paraquat Poisoning
  Abstract Background: Acute kidney injury (AKI) is a common and fatal complication of paraquat (PQ) poisoning, with incidence of 40-70% and high mortality. This study aimed to develop and validate a machine learning model to predict AKI after PQ poisoning using…
  preprints 2026 置信度 0.74
• Predicting gait patterns from actionable impairments in Duchenne muscular dystrophy: A Machine Learning and Explainable Artificial Intelligence study
  Background Prolonging ambulation is an important treatment goal in children with Duchenne muscular dystrophy (DMD). Clinical management targets ‘actionable’ (i.e., modifiable) impairments, such as progressive muscle weakness and contractures, that underlie gai…
  preprints 2026 置信度 0.74
• Predictive Value of Nutritional and Inflammatory Factors for Liver Fibrosis Stage Classification Among HBV-Exposed Individuals
  Abstract Background Liver fibrosis is a major contributor to liver-related morbidity and mortality among individuals with previous or current hepatitis B virus (HBV) exposure. Nutritional and inflammatory factors have been implicated in liver fibrosis, however…
  preprints 2026 置信度 0.74
• Machine Learning for Predicting Delayed Graft Function and Graft Survival After Kidney Transplantation: A Systematic Review and Meta-Analysis
  Background: /Objectives: Machine-learning (ML) models have been proposed to improve prediction of delayed graft function (DGF) and graft survival after kidney transplantation. Whether they provide better predictive performance than conventional regression rema…
  preprints 2026 置信度 0.74
• Development and external validation of a machine-learning model for echocardiography-detected left ventricular systolic dysfunction in patients with sepsis: a dual-center retrospective cohort study
  Abstract Background Echocardiography-detected left ventricular systolic dysfunction (LVSD) occurs in some patients with sepsis, but retrospective data cannot reliably establish sepsis as its cause. We developed and externally validated a machine-learning model…
  preprints 2026 置信度 0.74
• Development and internal validation of a machine learning-based risk stratification tool for sarcopenia in Chinese older adults: findings from the CHARLS study
  Abstract Background: Sarcopenia is a major geriatric syndrome with increasing prevalence in aging populations. Although individual biomarkers such as C-reactive protein (CRP) and hemoglobin (Hb) have been associated with sarcopenia, their predictive performanc…
  preprints 2026 置信度 0.74
• Label-Free Diagnosis of Colorectal Cancer Using a SERS Platform Based on a Modified Glass Fiber Filter Membrane Combined with Machine Learning
  Abstract Non-invasive screening is critically important for the early diagnosis of colorectal cancer (CRC). Surface-enhanced Raman scattering (SERS) has shown great potential in liquid biopsy, yet it still faces the challenge of poor signal reproducibility in …
  preprints 2026 置信度 0.74
• Bayesian Machine Learning for Precision Oncology: Integrating Clinical, Genomic, and Imaging Data for Personalized Cancer Prognosis
  Abstract Precision oncology aims to improve cancer prognosis and treatment decision-making by leveraging diverse patient-specific data sources. However, effectively integrating clinical, genomic, and imaging information remains challenging due to data heteroge…
  preprints 2026 置信度 0.74
• Early Mortality Prediction in Upper Gastrointestinal Bleeding Using Explainable Machine Learning: Comparison with Established Clinical Risk Scores
  Abstract Background Early risk stratification in upper gastrointestinal bleeding (UGIB) is essential for emergency department (ED) triage, treatment planning, and mortality reduction. Although conventional clinical scores are widely used, they may not fully ca…
  preprints 2026 置信度 0.74
• Interpretable Machine Learning and Point-of-Care Digital Risk Stratification for Pneumothorax Following Lung Tumor Ablation: A Multicenter Validation Study
  Abstract Pneumothorax requiring chest tube drainage complicates 10% to 15% of percutaneous lung tumor ablation procedures. The current lack of individualized risk prediction necessitates uniform, reactive post-procedural surveillance, which fails to optimize h…
  preprints 2026 置信度 0.74
• A software package for simple and rigorous survival machine learning analysis in biomedical research
  Survival analysis is a fundamental technique in biomedical research for modeling time-to-event data. It enables the identification of prognostic factors in disease, compares survival outcomes across treatment groups, and performs targeted treatment selection. …
  preprints 2026 置信度 0.74
• Explainable Machine Learning Models for Alzheimer’s Diagnosis Using Routine and Low-Cost Clinical Data
  Emerging as a significant global health challenge, Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that causes memory loss and cognitive decline. Despite the ever-increasing waiting time for a specialist diagnosis, the need for a cost-effe…
  preprints 2026 置信度 0.74