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• ADC Map Radiomics for Non-Invasive Detection of BRCA1/2 Mutation Status in Prostate Cancer: A Machine Learning-Based Radiogenomics Study
  Abstract Purpose Conventional multiparametric magnetic resonance imaging (mpMRI) parameters have limited ability to identify prostate cancer (PCa) patients harboring BRCA1/2 mutations. We investigated whether radiomic features extracted from apparent diffusion…
  europepmc 2026 置信度 0.80
• Interpretable Machine Learning Enables Preoperative Physiologic Risk Stratification for Dysphagia After Anti-Reflux Surgery
  Abstract Postoperative dysphagia remains one of the most clinically significant complications following anti-reflux surgery, yet existing preoperative risk stratification approaches incompletely integrate esophageal motility, reflux burden, and esophagogastric…
  preprints 2026 置信度 0.74
• Machine Learning Versus Self-Supervised Transfer Learning for 30-Day Readmission Prediction in Diabetic Patients
  Abstract Background: Hospital readmission within 30 days of discharge is a closely watched indicator of care quality and a substantial driver of avoidable healthcare cost, and diabetes is among the chronic conditions most strongly associated with early readmis…
  preprints 2026 置信度 0.74
• Machine learning-based neuroimaging for prediction of deep brain stimulation outcomes in movement disorders: Systematic review and meta-analysis
  Background Deep Brain Stimulation (DBS) surgery is a treatment of choice for movement disorders, and utilizes an implanted electrical pulse generator that administers electrical stimulation to designated brain regions responsible for motor control. The preoper…
  preprints 2026 置信度 0.74
• Artificial Intelligence for Predicting Lymph Node Metastasis in Prostate Cancer: An evidence-based analysis of Machine Learning and Deep Learning Using CT and MRI
  Abstract Background Preoperative identification of lymph node metastasis (LNM) is pivotal for tailoring surgical strategies in prostate cancer. Current radiologic assessment is limited by low sensitivity for micro-metastasis. Artificial intelligence (AI) inclu…
  preprints 2026 置信度 0.74
• Combined triglyceride–glucose and frailty index (TyGFI) and risk of endometrial cancer in U.S. women aged ≥45: NHANES 2011–2018 analysis integrating data engineering and machine learning with logistic modeling
  The authors have withdrawn this manuscript because the number of positive cases in the original data is too small, leading to major flaws in the statistical analysis and rendering the findings and conclusions unreliable. Therefore, the authors do not wish this…
  preprints 2026 置信度 0.74
• A Quantum-Inspired Ensemble Learning Framework for Multi-Class Respiratory Disease Diagnosis
  Abstract Accurate multi-class diagnosis of respiratory diseases using clinical tabular data remains a challenging problem in medical decision support systems due to complex non-linear feature dependencies, severe class imbalance, and limited interpretability o…
  preprints 2026 置信度 0.74
• Primary Care Gaps in Stroke-Prevention Risk-Factor Control Among Young Black and Hispanic Adults: A Cross-Sectional Machine-Learning Analysis of NHANES (1999–2018)
  Abstract Background. Young Black and Hispanic adults in the United States bear a disproportionate burden of cardiovascular stroke-risk factors. Using the National Health and Nutrition Examination Survey (NHANES), we quantified racial and ethnic disparities in …
  preprints 2026 置信度 0.74
• Application of Multifeature EEG Analysis and Machine Learning in the Investigation of Neurobiological Mechanisms of Schizophrenia
  Abstract Schizophrenia (SC) is a severe mental disorder characterized by complex pathological mechanisms and clinical manifestations, which poses significant challenges for diagnosis and treatment. This study aimed to explore the neural mechanism abnormalities…
  preprints 2026 置信度 0.74
• Improving ART Retention Through Machine Learning–Guided Targeting of Interventions: A Monte Carlo Simulation Study in Lilongwe, Malawi
  Retention in antiretroviral therapy (ART) care remains a major challenge in high-burden settings such as Malawi, where substantial loss to follow up undermines treatment outcomes and long-term epidemic control. Although machine learning models can accurately i…
  preprints 2026 置信度 0.74
• Explainable Artificial Intelligence for Early Heart Disease Prediction Using Ensemble Machine Learning Models
  Heart disease remains one of the leading causes of mortality worldwide, creating an urgent need for accurate and interpretable predictive systems that can support early diagnosis and clinical decision-making. Recent advances in machine learning have demonstrat…
  preprints 2026 置信度 0.74
• Machine learning–based 18F-FDG PET/MR fusion radiomics for predicting EGFR mutation in lung cancer
  Abstract Background This study aimed to develop a machine learning–based radiomics model based on 18F‑FDG PET/MR images, to predict EGFR mutation status and mutation abundance in lung cancer, and to explore the value of multimodal radiomic features in clinical…
  preprints 2026 置信度 0.74
• Integrating Predictive Modeling into Viral Test Stewardship: HHV-7 as a Use Case
  Abstract Human herpesvirus 7 (HHV-7) is a common pediatric virus whose clinical relevance is often underestimated, resulting in heterogeneous testing practices and variable diagnostic yield. Improving diagnostic decision-making requires the ability to identify…
  preprints 2026 置信度 0.74
• Explainable Machine Learning and Temporal Calibration Drift of a TyG- BMI-Based Model for Early Prediction of Gestational Diabetes Mellitus
  Abstract Background First-trimester prediction of gestational diabetes mellitus (GDM) is useful only when risk estimates remain accurate over time and are interpretable enough for clinical discussion. Many GDM prediction studies emphasize discrimination, where…
  europepmc 2026 置信度 0.80
• Development and validation of a machine learning-based risk stratification model for 30-day mortality in sepsis patients with gastrointestinal bleeding
  Abstract Background Sepsis complicated by gastrointestinal bleeding (GIB) is associated with high mortality in the intensive care unit (ICU). Current prognostic tools lack specificity for this dual comorbidity, making early and accurate risk stratification a m…
  preprints 2026 置信度 0.74
• Medical Contingency Security (MCS): An N-1 Reliability Criterion for Failure-Resilient Clinical Prediction
  Clinical prediction systems are commonly optimized for intact-input discrimination, while safety engineering in other high-consequence fields asks a different question: what remains safe when one credible component fails? This paper proposes Medical Contingenc…
  preprints 2026 置信度 0.74
• Rapid diagnosis of fever etiology using wearable temperature monitoring and machine learning
  Introduction Distinct temperature patterns have long been recognized to correlate with fevers of differing etiologies. While the use of wearable sensors for high-frequency temperature monitoring (HFTM) on a near minute-by-minute basis has been shown to detect …
  preprints 2026 置信度 0.74
• Efficacy and safety of Rhodiola crenulata injection in the treatment of acute coronary syndrome: A meta-analysis with machine learning
  Abstract Background: Acute coronary syndrome (ACS) remains a leading cause of cardiovascular death and disability. Despite increasingly comprehensive guideline-directed therapy and the widespread use of contemporary revascularization, substantial unmet clinica…
  preprints 2026 置信度 0.74
• Interpretable Radiomics-Based Machine Learning for Non-Invasive Prediction of Tertiary Lymphoid Structure Status in Pancreatic Ductal Adenocarcinoma
  Abstract Background Tertiary lymphoid structures (TLS) represent organized ectopic lymphoid aggregates within the tumor microenvironment and are increasingly recognized as markers of local antitumor immune organization. In pancreatic ductal adenocarcinoma (PDA…
  preprints 2026 置信度 0.74
• Diagnostic Prediction of Attention-Deficit/Hyperactivity Disorder in Children Using AULA: A Virtual Reality-Based Continuous Performance Task
  Abstract Accurately diagnosing Attention-Deficit/Hyperactivity Disorder (ADHD) remains challenging due to symptom variability, reliance on subjective reports, and limited ecological validity of traditional tools. This study investigates the diagnostic utility …
  preprints 2026 置信度 0.74
• Risk stratification for the rapid pain progression phenotype in knee osteoarthritis using interpretable multimodal machine learning: Development in the Osteoarthritis Initiative and external evaluation in the Prospective Cohort of Osteoarthritis from A Coruña
  ABSTRACT Objective To develop an interpretable multimodal machine-learning model for risk stratification of the rapid pain progression phenotype in knee osteoarthritis and to evaluate its performance in the independent PROCOAC cohort. Methods An elastic-net lo…
  preprints 2026 置信度 0.74
• CardioTrust: Trustworthy Retrieval Augmented Clinical Decision Support for Personalized Cardiovascular Disease Prediction
  Abstract Cardiovascular disease (CVD) remains a leading cause of global mortality, demanding timely and precise risk stratification alongside transparent decision support. While machine learning models excel at risk prediction, they lack clinical interpretabil…
  preprints 2026 置信度 0.74
• Leveraging Machine Learning Approaches to Identify Health-Related Social Needs Screening from Electronic Health Records
  ABSTRACT Health-related social needs (HRSNs), such as housing instability, food insecurity, and transportation challenges, are nonmedical factors associated with poorer health and well-being. Screening for unmet HRSNs is a critical step towards identifying at-…
  preprints 2026 置信度 0.74
• Machine Learning-Based Prediction of Sleep Quality in Patients with Multiple Sclerosis
  Background: /Objectives: Sleep disturbances are common but frequently underrecognized in multiple sclerosis (MS), independently predicting reduced quality of life and worsening fatigue, cognitive impairment, and depression. While pain, nocturia, fatigue, and m…
  preprints 2026 置信度 0.74
• A Multimodal Multiomics Machine Learning (MMM) approach for biomarker discovery and acceleration of clinical trial readiness for childhood-onset neurological disorders
  Background: Childhood neurodegenerative disorders are usually rare, genetic, and life-limiting. Whilst targeted approaches present huge potential, significant hurdles include disease rarity, geographical dispersion of patients, funding, clinical trial design, …
  preprints 2026 置信度 0.74
• Utilizing Machine Learning to Classify Type of Heartbeat in Athletes from Electrocardiogram Data
  Abstract Sudden cardiac events in athletes, though rare, carry severe consequences and underscore the need for accurate and scalable screening methods. Electrocardiogram (ECG) analysis is a cornerstone of cardiac evaluation, but widespread implementation in at…
  preprints 2026 置信度 0.74
• Comparative Evaluation of Machine Learning and Deep Learning Models for Early Prediction of Severe Acute Pancreatitis: A Multi-Model Study Using the 2012 Revised Atlanta Classification
  Background Acute pancreatitis (AP) is a common gastrointestinal emergency with a subset of patients progressing to severe acute pancreatitis (SAP), which carries substantial morbidity and mortality. Current clinical severity scores such as BISAP, APACHE II, Ra…
  preprints 2026 置信度 0.74
• Multimodal Radiogenomic Machine Learning for Biochemical Recurrence Prediction Following Radical Prostatectomy Using PSMA-PET, mpMRI, and the Decipher Genomic Classifier
  Background Biochemical recurrence (BCR) occurs in up to 40% of men following radical prostatectomy (RP). Current risk models rely primarily on clinicopathologic variables and may not fully capture the biological heterogeneity associated with recurrence. The De…
  preprints 2026 置信度 0.74
• Integrated Clinical and Proteomic Precision Subgrouping for Severe Dengue Endotype Signature
  Background Severe dengue remains difficult to predict because patients with different clinical trajectories may present with overlapping features, and conventional severity classifications may not fully capture underlying biological heterogeneity. In this stud…
  preprints 2026 置信度 0.74
• Predicting tuberculosis treatment relapse among geriatric patient using machine learning algorithms in a high Tuberculosis-burden Ghanaian setting.
  Abstract Background, the world health organization estimated in 2020 of 5.8 million people newly diagnosed with Tuberculosis, predicting tuberculosis treatment relapse is crucial for improving patient treatment outcomes, studies have explored the factors conne…
  preprints 2026 置信度 0.74
• Management of Prediction and Classifying of Wound Healing Results in Plastic and Reconstructive Surgery Based on Machine Learning Models
  Postoperative wound healing complications present a major challenge in plastic and reconstructive surgery, prolonging recovery and impairing outcomes. Early risk identification is difficult due to complex interactions among clinical, laboratory, and molecular …
  preprints 2026 置信度 0.74
• An Explainable Hybrid of Classical and Quantum Support Vector Machine Models for Maternal Mental Health Risk Prediction Using Multicountry Data
  Abstract Maternal mental health disorders remain a significant global public health challenge, particularly in low- and middle-income countries where limited mental health resources and inadequate screening systems contribute to underdiagnosis and delayed inte…
  preprints 2026 置信度 0.74
• Machine Learning Models Combining Multimodal Ultrasound and Clinical Factors for Predicting Ischemic Stroke Risk in Patients with Carotid Plaques
  Abstract Background Carotid plaque vulnerability is a key predictor of ischemic stroke (IS). We aimed to evaluate the performance of machine learning models combining multimodal ultrasound and clinical features to assess IS risk in patients with carotid plaque…
  europepmc 2026 置信度 0.80
• An Interpretable Machine Learning Framework for Predicting Disease Progression in Early-Stage Cardiovascular-Kidney-Metabolic(CKM) Syndrome: A Prospective Cohort Study
  Abstract Objective: Cardiovascular-kidney-metabolic (CKM) syndrome constitutes a continuum of metabolic, renal, and cardiovascular dysfunction, yet practical tools for stratifying progression risk in its early, modifiable stages remain absent. We aimed to deve…
  preprints 2026 置信度 0.74
• Explainable Longitudinal Machine Learning for Dementia Progression Using Cognitive and MRI Biomarkers
  Dementia is a progressive neurological condition characterized by cognitive decline and structural brain changes that evolve. Longitudinal modeling of these changes is important for improving disease monitoring, identifying progression patterns, and supporting…
  preprints 2026 置信度 0.74
• A machine learning-based risk model for loss of functional teeth in older adults: development and validation
  Abstract Background Functional tooth loss (FTL) is a significant public health challenge among older adults, with documented adverse effects on oral function, quality of life, and systemic health outcomes. Current risk assessment tools, largely derived from cr…
  preprints 2026 置信度 0.74
• A Leakage-Aware Machine Learning Framework for Multiclass Comorbidity Classification in Fibromyalgia: Integrating Genetic Feature Selection, Nested Cross-Validation, and Explainable AI
  Abstract Fibromyalgia frequently co-occurs with disc herniation and inflammatory bowel syndrome, and the overlapping symptom profiles of these conditions complicate differential diagnosis. This paper proposes a machine learning framework for three-class comorb…
  preprints 2026 置信度 0.74
• Development and external validation of deep learning models for spontaneous preterm birth prediction from mid-trimester cervical ultrasound
  Preterm birth is the leading cause of neonatal death. Despite sustained efforts to identify high-risk women in the mid-trimester, accurate prediction remains difficult. Quantitative cervical ultrasound texture has been proposed as a predictor of spontaneous pr…
  preprints 2026 置信度 0.74
• Predicting In-Hospital Mortality in ICU Patients with Intracerebral Hemorrhage: A Machine Learning Study Using Dynamic Time-Series Features with Dual-Database Validation
  Abstract Background ICH has high ICU mortality, yet conventional scores rely on static data and miss early dynamics. We developed and validated machine learning models using 24-hour time-series features to predict in-hospital mortality. Methods This TRIPOD + A…
  preprints 2026 置信度 0.74
• First-24-hour machine learning for 30-day mortality prediction in ICU trauma patients: development in MIMIC-III and cross-database evaluation in MIMIC-IV
  ICU trauma patients are clinically heterogeneous, and early mortality risk stratification may support monitoring and resource allocation. We developed machine learning models for 30-day mortality prediction using information recorded during the first 24 hours …
  preprints 2026 置信度 0.74
• Explainable Machine Learning Model for Prostate Cancer Risk Prediction Using Clinical and Polygenic Risk Features in a Nationwide Korean Cohort
  Abstract Background Early prediction of prostate cancer risk is critical for timely diagnosis and treatment. While prostate-specific antigen (PSA) testing is widely used in screening, its limited specificity highlights the need for complementary biomarkers. We…
  preprints 2026 置信度 0.74
• Machine Learning Approaches for Intelligent Dental Implant Planning and Crown Morphology Prediction
  Dental implant therapy requires precise planning to ensure functional performance, aesthetic outcomes, and long-term clinical success. Conventional implant planning and crown design often rely on clinician experience, radiographic interpretation, and manual me…
  preprints 2026 置信度 0.74
• Integrating Deep Learning and Radiomics in Chest CT for Risk-Adapted Management of Early-Stage Lung Adenocarcinoma: A Dual-Prediction Model
  Abstract Background To develop and validate a CT-based hybrid model for preoperative prediction of invasive tumor features (visceral pleural invasion, lymphovascular invasion, or spread through air spaces) and occult lymph node metastasis (OLNM) in clinical ea…
  preprints 2026 置信度 0.74
• Beyond the Red Complex: <i>De Novo</i> Marker Discovery Uncovers Novel Periodontitis-Associated Taxa and Enables Non-Invasive Machine Learning Diagnosis
  Background Periodontitis affects over 1 billion people worldwide, yet diagnosis relies on clinical measures that capture tissue destruction rather than underlying microbial dysbiosis. Most microbial-biomarker studies use 16S rRNA sequencing or reference-databa…
  preprints 2026 置信度 0.74
• CardioSafeAI: An Explainable Federated Machine Learning Framework with Multi-Stage Hybrid Feature Selection for Cardiovascular Disease Prediction
  cardiovascular disease (CVD) is a leading cause of global mortality and may progress without obvious symptoms. This study proposes CardioSafeAI, an explainable federated learning framework for predicting ten-year coronary heart disease risk from a public datas…
  preprints 2026 置信度 0.74
• Interpretable Machine Learning for Subtype-Specific Prognosis in Sudden Hearing Loss
  Abstract Background Sudden sensorineural hearing loss (SSNHL) is a highly heterogeneous condition with unpredictable recovery. We aimed to develop and validate an interpretable, subtype-specific machine learning (ML) framework for prognostic assessment using t…
  preprints 2026 置信度 0.74
• Spatial and Machine Learning Analysis of Breast and Cervical Cancer Screening Uptake in Ghana: Evidence from the 2022 Ghana Demographic and Health Survey
  Background Breast and cervical cancer screening in Ghana remains low, and several analyses of the Ghana Demographic and Health Survey (GDHS) have already shown that wealth, education, and place of residence pattern who gets screened [1–3]. Whether this pattern…
  preprints 2026 置信度 0.74
• Non-Invasive Embryo Quality Assessment via Matrix-Optimized Untargeted LC-MS Metabolomics of Spent Embryo Culture Media and Weighted Ensemble Machine Learning
  Background Non-invasive embryo quality assessment is a critical unmet need in assisted reproductive technology (ART). Preimplantation genetic testing for aneuploidy (PGT-A) is effective but requires invasive biopsy that may compromise embryo viability. Metabol…
  preprints 2026 置信度 0.74
• Feasibility of Real-Time AI Models For Clinical Tasks Involving Tetanus: Study Protocol
  Early prediction of disease progression in tetanus enables timely intervention which can improve outcome. We have developed a machine learning model to predict transition to severe tetanus. The model uses continuous pulse plethysmography waveforms recorded fro…
  preprints 2026 置信度 0.74
• Markerless Video-Based Gait Analysis for Motor Phenotyping in Individuals with Schizophrenia Using Interpretable Machine Learning
  Background: /Objectives: Motor abnormalities are frequently observed in schizophrenia, but accessible methods for objective gait quantification remain limited. This exploratory controlled-setting study examined whether markerless smartphone-based video analysi…
  preprints 2026 置信度 0.74
• Prediction of Pediatric Lower Urinary Tract Dysfunction Using Uroflowmetry Data and Machine Learning with Explainable AI Techniques
  Abstract 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 clin…
  preprints 2026 置信度 0.74
• Integrating Salivary Microbiome Profiles with Clinical and Behavioral Data for Caries Risk Prediction in School-Age Children
  Abstract Background: Dental caries remains a major public health problem among school children, and evidence show the contribution of oral microbiota dysbiosis in the disease onset and progression. However, integrative predictive models incorporating oral micr…
  preprints 2026 置信度 0.74
• Clinical Readiness of Machine-Learning Risk Models in Non-Variceal Upper Gastrointestinal Bleeding: A Systematic Review and Network Meta-Analysis
  Abstract Risk scores guide discharge, urgent endoscopy, transfusion, and monitoring in non-variceal upper gastrointestinal bleeding (NV-UGIB), but their performance relative to machine learning (ML) models remains unclear. We reviewed 26 studies and performed …
  preprints 2026 置信度 0.74
• Development and Evaluation of a Nurse-Centered Machine Learning Tool for Pressure Injury Risk Assessment in Adult ICU Patients: Retrospective Cohort Study
  Abstract Background Pressure injuries are a common complication in intensive care units, and existing risk assessment tools often rely on subjective judgment with inconsistent predictive performance. Recent machine learning models have shown promise, but many …
  preprints 2026 置信度 0.74
• Enhancing Explainability in AI Models for Blood Glucose Prediction in Diabetes Patients
  Abstract Background Accurate short-term blood glucose prediction can help reduce adverse glycemic events and improve diabetes management. Although machine learning models have shown promising performance for glucose forecasting, their clinical adoption remains…
  preprints 2026 置信度 0.74
• A Multi-Schedule Machine Learning Pipeline for Medicare Reimbursement Change Prediction and Operational Risk Stratification
  FeePredict is a three-stage random forest machine learning framework to simultaneously predict whether Medicare reimbursement rates for specific procedures will change, in which direction they will change, and by how much. FeePredict was applied to the four ma…
  preprints 2026 置信度 0.74
• Development and Validation of an Interpretable Machine Learning Model for Depression Risk Stratification in Middle-Aged and Older Patients with Gastrointestinal Diseases: A Cross-Sectional Study Using CHARLS Data
  Abstract Background Depression is highly prevalent in middle-aged and older patients with gastrointestinal diseases (GID) and severely impairs their quality of life and treatment outcomes. This cross-sectional study aimed to develop and validate an interpretab…
  preprints 2026 置信度 0.74
• Artificial Intelligence for ICU Mortality Prediction:Comparative Machine Learning Models and the Role of Surgical Patients
  Abstract Purpose: To compare artificial intelligence and machine-learning models for in-hospital mortality prediction in adult intensive care unit (ICU) patients and examine the contribution of surgical status to model behavior. Methods: This retrospective sin…
  preprints 2026 置信度 0.74
• Structured-to-Narrative Representation Framework for Next-Day Discharge Prediction Using Structured EHR Data and Clinical Language Models
  Abstract Objective Accurate prediction of next-day hospital discharge after elective spine surgery is critical for optimizing inpatient operations and perioperative care coordination. This study introduces a structured-to-narrative modeling framework that tran…
  preprints 2026 置信度 0.74
• A systematic analysis of machine learning pipelines for robust antimicrobial resistance prediction
  Motivation Antimicrobial resistance (AMR) has been identified as a top global public health threat. Accurate AMR phenotype prediction from whole-genome sequencing data is an essential tool for accelerating clinical decision-making and mitigating resistance spr…
  preprints 2026 置信度 0.74
• Endothelial Activation and Stress Index as a Prognostic Marker and Machine Learning Predictor of Mortality in Mechanically Ventilated ICU Patients: A Multicenter Retrospective Cohort Study
  Abstract Background The Endothelial Activation and Stress Index (EASIX), derived from lactate dehydrogenase, serum creatinine, and platelet count, has been associated with adverse outcomes in hematological and cardiological populations, but its prognostic valu…
  preprints 2026 置信度 0.74
• Machine learning models to improve targeting of blood culture testing
  Background Bloodstream infections are a major cause of mortality, yet the primary testing method, blood cultures, have low positivity ( Methods In this retrospective cohort study, we used routinely collected clinical and laboratory data available around cultur…
  europepmc 2026 置信度 0.80
• Machine learning for early prediction of 90-day mortality in ICU patients with HBV-related liver disease
  Abstract Patients with HBV-related liver disease requiring intensive care are at high risk of short-term mortality, and early prognostic stratification using information available within the first 24 hours of ICU admission remains clinically important. This re…
  preprints 2026 置信度 0.74
• Efficacy and safety of Rhodiola crenulata injection in the treatment of acute coronary syndrome: A meta-analysis with machine learning
  Abstract Background: Acute coronary syndrome (ACS) remains a leading cause of cardiovascular death and disability. Despite increasingly comprehensive guideline-directed therapy and the widespread use of contemporary revascularization, substantial unmet clinica…
  preprints 2026 置信度 0.74
• Explainable machine learning reveals the challenges of predicting new-onset motor complications in Parkinson’s disease
  Predicting new-onset motor fluctuations and levodopa-induced dyskinesias (LID) is crucial for optimizing Parkinson’s disease management. To establish a transparent prognostic framework, we applied explainable machine learning to real-world, multicentric clinic…
  preprints 2026 置信度 0.74
• Development and validation of a machine learning-based model incorporating systemic inflammatory, nutritional, and coagulation parameters for predicting metastasis in lung cancer
  Abstract Purpose This study aimed to develop and validate a novel machine learning-based model for predicting distant metastasis in lung cancer, uniquely incorporating multidimensional pretreatment indicators, namely markers of systemic inflammation, tumor sta…
  preprints 2026 置信度 0.74
• Personalized planning of cardiac resynchronization therapy through integration of coronary sinus geometry, clinical data, digital twins, and machine learning: visualization, stratification, and optimization
  Background Cardiac resynchronization therapy (CRT) fails in 30% of patients, often due to suboptimal left ventricular pacing site (LVPS) selection. Current practice lacks tools for pre-procedural, patient-specific LVPS optimization within the accessible corona…
  preprints 2026 置信度 0.74
• MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer’s Disease Neuroimaging Initiative
  ABSTRACT Background Efficient cerebral microhemorrhage (MCH) monitoring is critical for anti-amyloid therapy safety due to ARIA-H risk. We developed MCH-Guard, a multimodal machine-learning framework, to stratify MCH risk using ADNI data (N=813). Methods Neste…
  preprints 2026 置信度 0.74
• Interpretable machine learning prediction of in-hospital mortality in ICU patients with cancer and sepsis using first-day data: Development using MIMIC-IV and external validation in eICU-CRD
  Background: Critically ill patients with cancer and sepsis have high in-hospital mortality, but externally validated prediction models are limited. Objective: To develop and externally validate an interpretable machine learning framework using first-day intens…
  preprints 2026 置信度 0.74
• Genomic subtypes inferred from clinical sequencing provide significant prognostic stratification in metastatic breast cancer
  Purpose The 11 Integrative Cluster (IntClust) genomic subtypes of breast cancer have both prognostic and predictive value but require integrated DNA copy-number and gene expression profiling, which are not routinely used in clinical care. We tested whether Int…
  preprints 2026 置信度 0.74
• Spatial transcriptomics and machine learning define exhaustion-like bone marrow T-cell islands associated with myeloma progression and clinical risk
  Background Multiple myeloma (MM) progression is accompanied by remodeling of the bone marrow immune microenvironment. Local interactions among malignant plasma cells, stromal cells, myeloid cells, and immune cells not only support tumor cell survival, expansio…
  preprints 2026 置信度 0.74
• An Explainable AI Framework Integrating Survival Analysis and Machine Learning for COX Inhibitor Evaluation
  Abstract Even though enzymes such as cyclooxygenase (COX) are fundamental in the modulation of inflammation and pain, fragmented analytical methodologies and reduced model interpretability remain a bottleneck to predictive assessment of COX inhibitors. To fill…
  preprints 2026 置信度 0.74
• Life-Stage Heterogeneity in the Mental Health Treatment Gap: An Unsupervised Machine Learning Profiling of Symptomatic US Adults
  Background Despite a rising global psychiatric burden, a treatment gap persists where the majority of symptomatic individuals remain unmedicated. Traditional epidemiological analyses treat this untreated population as a single, uniform block, obscuring specifi…
  preprints 2026 置信度 0.74
• Development and Bidirectional Cross-Database Validation of a Machine Learning Model for Predicting Invasive Fungal Infection in Critically Ill Patients
  Abstract Background Invasive fungal infection (IFI) is a life-threatening complication in critically ill patients, yet externally validated, generalizable machine learning (ML) tools for early risk stratification remain scarce. We developed and rigorously vali…
  preprints 2026 置信度 0.74
• Development and Validation of a Machine Learning-Based Clinical Prediction Model for Liver Metastasis in Stage III–IV Non-Small Cell Lung Cancer
  Abstract Liver metastasis is among the major determinants of prognosis in non-small cell lung cancer (NSCLC), and early identification of high-risk patients has direct implications for clinical management. We retrospectively enrolled 849 patients with stage II…
  preprints 2026 置信度 0.74
• Prediction of post-operative delirium with machine learning in abdominal surgery with comorbidity indices and laboratory values
  Background Postoperative delirium (POD) is a complication associated with most types of surgery, and is associated with a number of detrimental effects. Therefore, it is of interest to determine which patients may be at higher risk of POD so that mitigating st…
  preprints 2026 置信度 0.74
• Machine Learning Approaches for Intelligent Dental Implant Planning and Crown Morphology Prediction
  Dental implant therapy requires precise planning to ensure functional performance, aesthetic outcomes, and long-term clinical success. Conventional implant planning and crown design often rely on clinician experience, radiographic interpretation, and manual me…
  preprints 2026 置信度 0.74
• Machine-learning MRI stratification of genetic frontotemporal dementia for clinical trial enrichment
  BACKGROUND Genetic frontotemporal dementia (FTD) shows large differences in symptom profiles, brain atrophy patterns, and progression rate, making clinical trials difficult to design and power. There is a need for biomarkers that can model disease progression,…
  preprints 2026 置信度 0.74
• Development and Validation of a Machine Learning Model to Predict Prognosis in Patients with Advanced Head and Neck Cancer
  Importance Prognostic tools beyond staging are needed to guide treatment and counseling in head and neck squamous cell carcinoma (HNSCC). Objective To develop and externally validate a machine learning model predicting survival in advanced HNSCC using routinel…
  preprints 2026 置信度 0.74
• Association Between the Hemoglobin-to-Red Cell Distribution Width Ratio and Risk of Type 2 Diabetic Kidney Disease: A Machine Learning Study
  Abstract Background Hemoglobin (Hb) and red cell distribution width (RDW) are well-established risk factors for the onset and progression of diabetic kidney disease (DKD). Consequently, this study investigated the clinical association between the hemoglobin-to…
  preprints 2026 置信度 0.74
• The Analysis of Sepsis Vascular Response Index Based on Machine Learning
  Abstract Purpose: This study aims to explore the clinical value of monitoring the vascular response index and other hemodynamic parameters using the Pulse Indicator Continuous Cardiac Output (PICCO) monitoring device in the treatment of septic shock patients. …
  preprints 2026 置信度 0.74
• Machine Learning–Derived Cardiometabolic Risk Burden Identified from Health Checkup Data in a Japanese Population
  Abstract Background Conventional cardiometabolic risk classifications rely on predefined diagnostic thresholds and often fail to capture the continuous, multifactorial, and heterogeneous nature of risk in the general population. In particular, individuals with…
  preprints 2026 置信度 0.74
• External Validation of Machine Learning Models for Traumatic Brain Injury: Performance, Efficiency, and Carbon Imprint
  ABSTRACT Background Machine learning (ML) models for traumatic brain injury (TBI) prediction increasingly demand extensive data, computational resources, and energy consumption, yet simpler models may offer comparable clinical benefit with lower barriers to de…
  preprints 2026 置信度 0.74
• A Pilot Study of an Interpretable Machine Learning Model for Fetal Birth Weight Estimation: Standardizing Accuracy Across Sonographer Experience
  Abstract Background Accurate prediction of fetal birth weight is essential for antenatal management; however, traditional formulas demonstrate limited precision and are susceptible to label leakage. This study aimed to develop a prediction model based on 30 le…
  preprints 2026 置信度 0.74
• Identifying cognitive impairment in older adults using machine learning on combined fNIRS and motion data during an upper extremity dual task function
  Abstract It is critical that dementia clinical interventions begin early in the disease progression to be effective. Similar clinical manifestations may be observed in both cognitively healthy older adults and older adults with early-stage cognitive impairment…
  preprints 2026 置信度 0.74
• CARDIAX-NNFE A Cardiac Mechanics SciML Framework
  One goal of Scientific Machine Learning (SciML) is to advance traditional scientific computing frameworks with modern machine learning tools. This includes extending established methods, such as the finite element method, with cardiac function applications due…
  preprints 2026 置信度 0.74
• 5th International Conference on Machine Learning and Applications-Cover
  crossref 2011-06-08T12:59:25Z 置信度 0.70
• Behavioral biometrics dentification based on machine learning methods
  This essay aims to develop a machine learning model that is able to build an initial advantage for behavioral biometrics. While behavioral biometrics have theoretically shown to be advantageous over traditional identification methods like passwords and physica…
  crossref Yi Zhang 2022-04-29T20:07:24Z 置信度 0.70
• Archetypal analysis for machine learning
  Archetypal analysis (AA) proposed by Cutler and Breiman in [1] estimates the principal convex hull of a data set. As such AA favors features that constitute representative 'corners' of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys …
  crossref Morten Morup, Lars Kai Hansen 2010-10-08T09:32:30Z 置信度 0.70
• Machine-learning-based channel estimation
  Wireless communication has been a highly active research field. Channel estimation technology plays a vital role in wireless communication systems. Channel estimates are required by wireless nodes to perform essential tasks such as precoding, beamforming, and …
  crossref Yue Zhu, Gongpu Wang, Feifei Gao 2019-07-04T13:28:42Z 置信度 0.70
• 5th International Conference on Machine Learning and Applications-TOC
  crossref 2011-06-08T08:59:25Z 置信度 0.70
• Machine learning workflows and types
  crossref Hoss Belyadi, Alireza Haghighat 2021-04-23T14:17:26Z 置信度 0.70
• Algorithmic Foundations of Machine and Deep Learning
  crossref Vinod Kumar Khanna 2026-02-06T23:10:06Z 置信度 0.70
• A Machine Learning-Based Framework for Image Quality Inspection of Automotive Metal Stamping Parts
  Traditional quality control for automotive metal stamping parts relies heavily on Checking Fixtures (C/Fs).These fixtures are custom-engineered for individual components, resulting in high initial design and manufacturing costs, limited versatility, and time-c…
  crossref 2025-12-24T07:10:23Z 置信度 0.70
• Shallow Learning Versus Deep Learning in Speech Recognition Applications
  crossref Nasmin Jiwani, Ketan Gupta 2024-10-12T18:01:28Z 置信度 0.70
• Riitta Katila Discusses Research Methods Using Machine Learning to Study Public/Private Firm Collaborations
  crossref 2023-03-24T08:33:30Z 置信度 0.70
• First Order Regression
  crossref Aram Karalič, Ivan Bratko 2002-12-22T04:48:21Z 置信度 0.70
• Machine Learning with Unsupervised Quantum Models
  Machine Learning (ML) is the process of training machines to analyze and learn from provided data. It can be categorized into three main types: supervised, unsupervised, and reinforcement learning. Unsupervised learning, a method that allows the discovery of u…
  crossref Prianka Ramachandran Radhabai, Sathya Karunanidhi, Shreyanth Srikanth 2024-10-01T19:47:03Z 置信度 0.70
• Optical random projections for large scale machine learning
  Light propagation in disordered media can be seen as a linear operation on fields : a multiplication by a random matrix, between a set of input modes (for instance pixels of an SLM) and output modes (for instance pixels of a camera). This operation, akin to a …
  crossref Sylvain Gigan 2024-06-18T23:09:32Z 置信度 0.70
• Concept Formation During Interactive Theory Revision
  crossref Stefan Wrobel 2003-04-04T16:55:36Z 置信度 0.70