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• Machine learning classification of schizophrenia and social anhedonia based on facial expressions.
  Background and hypothesis Schizophrenia (SCZ) is characterized by deficits in emotional expression, with facial expressions serving as potential markers for diagnosis. Leveraging machine learning and computerized facial analysis, this study aimed to identify f…
  europepmc 2026 置信度 0.80
• The application of traditional machine learning and deep learning with EEG for mild cognitive impairment: a bibliometric analysis.
  Objective This study uses bibliometric methods to analyze the status of electroencephalography (EEG) and machine learning applications in mild cognitive impairment (MCI) research. Methods Relevant literature was searched in the four major English databases (We…
  europepmc 2026 置信度 0.80
• Transdiagnostic clinical correlates for behaviors of concern in pediatric neurodevelopmental disorders: a retrospective chart review and machine learning analysis.
  Introduction Behaviors of concern, such as aggression and self-injury, are common in children with neurodevelopmental disorders and contribute substantially to caregiver stress and health system burden. However, most evidence on behaviors is drawn from diagnos…
  europepmc 2026 置信度 0.80
• Development and Validation of an Interpretable Machine Learning Model for Predicting In-Hospital Mortality in Diabetic Patients with Sepsis-Associated Acute Kidney Injury.
  BackgroundSepsis-associated acute kidney injury (SA-AKI) is a common and severe complication in critically ill patients, with poor prognosis. Diabetes may further increase adverse outcomes through infection susceptibility, immune dysfunction, and renal vulnera…
  europepmc 2026 置信度 0.80
• Development and internal validation of a machine-learning-based risk identification model for diabetic kidney disease in patients with type 2 diabetes.
  Background Diabetic kidney disease (DKD) is a major microvascular complication of type 2 diabetes mellitus (T2DM) and the leading cause of end-stage renal disease in China. Limited disease awareness and insufficient early screening tools hinder timely interven…
  europepmc 2026 置信度 0.80
• A Scoping Review of Machine Learning-Based Prediction of Alzheimer's Disease Using Blood Biomarkers.
  Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder of late life that causes cognitive and functional decline and substantial mortality. Machine learning (ML) is increasingly used to discover patterns in clinical and biomarker data …
  europepmc 2026 置信度 0.80
• Machine Learning Analysis of Single-Cell Transcriptomics Identifies a B-Cell-Associated Fibroblast Signature Predictive of Renal Fibrosis Severity.
  Background Renal fibrosis represents the final common pathway of chronic kidney disease (CKD); however, both its definitive diagnostic biomarkers and the principal cellular mediators driving its progression remain incompletely characterized. Objective To deriv…
  europepmc 2026 置信度 0.80
• The role of catastrophizing in anxiety and depression symptom scores among knee osteoarthritis patients: a multimodal assessment with EEG and machine learning.
  To examine dimensional associations between anxiety- and depression-related symptom severity, pain catastrophizing, and resting-state EEG features in patients with knee osteoarthritis (KOA). Resting-state EEG spectral power (delta, theta, alpha, beta) was anal…
  europepmc 2026 置信度 0.80
• Machine learning prediction of 30-day mortality in coronary artery disease: a retrospective multicenter study using electronic health records.
  Background Coronary artery disease (CAD) is the leading cause of death globally and a major contributor to hospital readmission. This study aimed to predict 30-day mortality in patients hospitalized with acute and chronic CAD using a structured machine learnin…
  europepmc 2026 置信度 0.80
• Machine learning for prediction of newly diagnosed atrial fibrillation after emergency percutaneous coronary intervention during hospitalization in patients with acute ST-segment elevation myocardial infarction: a multi-center prospective study.
  Background In-hospital complications after emergency PCI in acute STEMI are important, especially newly diagnosed atrial fibrillation and are associated with hemodynamic instability, heart failure, stroke and mortality. There are complex nonlinear interactions…
  europepmc 2026 置信度 0.80
• A Clinically Aligned Two-Stage Machine Learning Framework for Predicting Hungry Bone Syndrome After Parathyroidectomy.
  Background Hungry bone syndrome (HBS) is a frequent and clinically significant complication following parathyroidectomy (PTX) in patients with secondary hyperparathyroidism (SHPT), often leading to prolonged hypocalcaemia and increased healthcare burden. Exist…
  europepmc 2026 置信度 0.80
• Slide-DML: sliding-window-based estimation of heterogeneous treatment effects.
  Objective. Heterogeneous treatment effect (HTE) estimation is essential for understanding individual differences in physiological responses and supporting personalized healthcare. However, existing non-parametric HTE estimation methods often rely on complex pa…
  europepmc 2026 置信度 0.80
• Osteoporosis prediction in primary Sjögren's syndrome: development and external validation of a machine-learning comparison model.
  Objectives Osteoporosis and fragility fractures are clinically important complications of primary Sjögren's syndrome (pSS) that may accelerate functional decline and excess mortality. In practice, osteoporosis risk is often assessed using general-population to…
  europepmc 2026 置信度 0.80
• Explainable machine learning for diagnosing severe <i>Mycoplasma pneumoniae</i> pneumonia in children: model development and internal validation.
  Background Mycoplasma pneumoniae pneumonia (MPP) is common in children, but severe MPP (SMPP) may progress rapidly and is difficult to distinguish from non-severe disease because clinical, laboratory, and radiographic findings overlap. We aimed to develop and …
  europepmc 2026 置信度 0.80
• Machine Learning Enables Rapid Prediction of Acid-Reducing Agent Drug Interactions: A Streamlined Complement to PBPK Modeling.
  pH-dependent drug-drug interactions (DDIs) commonly occur when acid-reducing agents (ARAs) are co-administered with weakly basic drugs. Although physiologically based pharmacokinetic (PBPK) modeling effectively evaluates such DDIs, its use is limited by relian…
  europepmc 2026 置信度 0.80
• Clinical Specialty Expansion of AI-Enabled and Machine Learning-Enabled Medical Devices Authorized by the US Food and Drug Administration From 1995 to 2025: Longitudinal Content Analysis.
  Background The US Food and Drug Administration (FDA) has authorized AI-enabled and machine learning (ML)-enabled medical devices since 1995 and maintains a public registry of these authorizations. Prior analyses report that radiology dominates this landscape, …
  europepmc 2026 置信度 0.80
• Identification of suicidal ideation in major depressive disorder: A machine learning approach with multimodal digital features.
  Current detection models for suicidal ideation (SI) among depressed patients have primarily relied on clinical and biological features. This study aims at enhancing the real-world applicability of these models by using multimodal digital features. Patients sel…
  europepmc 2026 置信度 0.80
• Prediction of severe sepsis-associated acute kidney injury incorporating immune-inflammatory profiles: development and validation of a machine learning model in a multicenter prospective cohort study.
  Introduction Severe sepsis-associated acute kidney injury (SA-AKI) is a prevalent and life-threatening complication in critically ill patients, leading to increased mortality and a heightened risk of chronic kidney dysfunction. Current prediction models for se…
  europepmc 2026 置信度 0.80
• Can GPT Be Used as an Alternative Prediction Model to Traditional Machine Learning and Neural Networks on Low-Volume Clinical Data?
  Background and Objective Early and reliable disease prediction from structured clinical data remains challenging when datasets are small, highly imbalanced, and contain limited positive disease cases. Conventional machine learning (ML) and deep learning approa…
  europepmc 2026 置信度 0.80
• Immune-inflammatory and metabolic signatures for osteoporosis risk stratification in primary Sjögren's syndrome: development and internal validation of an interpretable machine-learning model.
  Background Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease. Osteoporosis (OP) is a common complication in patients with pSS (pSS-OP), which significantly affects their quality of life and prognosis. Currently, there is a lack of efficient and…
  europepmc 2026 置信度 0.80
• Explainable machine learning model for in-hospital hypoglycemia risk in patients with latent autoimmune diabetes in adults.
  Background Latent autoimmune diabetes in adults (LADA) is characterized by progressive β-cell impairment and severe glycemic lability, predisposing patients to in-hospital hypoglycemia. Few tailored risk-stratification models exist for this population. This st…
  europepmc 2026 置信度 0.80
• An explainable machine learning framework integrating clinical and lipidomic signatures for early severity stratification of hypertriglyceridemic pancreatitis.
  Background Hypertriglyceridemic pancreatitis (HTGP) is increasingly recognized as a major etiology of acute pancreatitis and is associated with a higher risk of early severe progression, systemic complications, and mortality compared with other etiologies. How…
  europepmc 2026 置信度 0.80
• Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.
  In recent years, interest in machine learning applications has grown rapidly, particularly in the medical domain, where large amounts of data are available for training these models. This review focuses on the potential of machine learning for early diagnosis …
  europepmc 2026 置信度 0.80
• A machine learning-based risk prediction model for Hospitalized patients with deep vein thrombosis.
  Background Deep vein thrombosis (DVT) is a common thrombotic condition with substantial morbidity when not identified early. Machine learning (ML)-based predictive models may improve early identification of patients at high risk for DVT, but few clinically app…
  europepmc 2026 置信度 0.80
• Better Discrimination, Unchanged Practice: Are Machine Learning Models Ready to Replace Established Risk Scores in Cardiac Surgery? A Narrative Review.
  Preoperative risk stratification underpins consent, treatment selection, and quality benchmarking in cardiac surgery, a task served for two decades by regression-derived scores such as the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II)…
  europepmc 2026 置信度 0.80
• A Validated, explainable machine learning-based preoperative risk model for microvascular invasion in hepatocellular carcinoma.
  Purpose This investigation aimed to develop and validate a diagnostic algorithm for preoperatively assessing the likelihood of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Methods Clinical and pathological information of patien…
  europepmc 2026 置信度 0.80
• Optical hand kinematics for machine learning-based screening of carpal tunnel syndrome.
  Carpal tunnel syndrome is usually assessed by clinical examination and electrodiagnostic testing, but these methods may not always be practical for rapid screening. In this work, it was explored whether hand motion recorded with a low-cost optical camera could…
  pubmed Çelikbaş Ş, Akgündoğdu A, Gündüz A 2026 置信度 0.82
• Building an early warning model for the risk of suicide attempt in people with depression based on machine learning: a single-centre study.
  Background Attempted suicide is one of the most serious clinical consequences for patients with depression, and early identification of high-risk individuals is crucial for preventing suicide deaths. Traditional clinical assessment often relies on subjective j…
  europepmc 2026 置信度 0.80
• Dataset-structured evidence synthesis for machine-learning prediction models: a methodological framework and worked example
  Abstract Background Systematic reviews of machine-learning prediction models often pool performance estimates by publication or algorithm label. This can misrepresent the evidence when algorithms share participants, benchmark datasets recur across reports, int…
  europepmc 2026 置信度 0.80
• A Machine Learning-Based Model to Predict Overactive Bladder Risk Among US Women: Evidence From the National Health and Nutrition Examination Survey 2011-2018.
  Background Overactive bladder (OAB) is a prevalent condition, particularly among women, characterized by urinary urgency, often accompanied by frequency and nocturia. Traditional risk prediction methods for OAB are limited, as they fail to fully integrate mult…
  europepmc 2026 置信度 0.80
• Utilizing Deep Learning-based Computed Tomography Fractional Flow Reserve on Coronary Artery Disease Diagnosis and Treatment: 1-year Clinical Application From a Chinese Major Hospital.
  Purpose Machine learning-based coronary computed tomography fractional flow reserve (CT-FFR) holds great potential for assessing coronary ischemic status. The current literature lacks a comprehensive description of the routine implementation of CT-FFR in real …
  europepmc 2026 置信度 0.80
• From clinical and typical separation to autism spectrum disorder and global developmental delay differentiation: Interpretable eye-tracking-based machine learning.
  In clinical practice, objectively distinguishing children with autism spectrum disorder (ASD) from those with typical development (TD) and other neurodevelopmental conditions with overlapping symptoms, such as global developmental delay (GDD), is critical for …
  europepmc 2026 置信度 0.80
• Machine learning models for risk prediction of diabetic retinopathy in Fujian eye study.
  Background Diabetic Retinopathy (DR) is among the most severe microvascular complications of diabetes, leading to visual impairment and diminished quality of life. This study developed and compared multiple machine learning models for DR risk prediction using …
  europepmc 2026 置信度 0.80
• Identification of Hub Gene Characteristics and Immune Landscapes Across Aging Subtypes in Atherosclerosis: A Machine Learning-Based Multi-Omics Study With Experimental Verification.
  Aging is a major risk factor for atherosclerosis (AS), but the aging-associated molecular characteristics and immune heterogeneity of AS remain incompletely understood. This study aimed to identify aging-related hub genes and characterize immune landscapes acr…
  europepmc 2026 置信度 0.80
• Bayesian Machine Learning for Estimating Optimal Dynamic Treatment Regimes With Ordinal Outcomes.
  Dynamic treatment regimes (DTRs) are sequences of decision rules designed to tailor treatments based on patients' treatment history and evolving disease status. Ordinal outcomes frequently serve as primary endpoints in clinical trials and observational studies…
  europepmc 2026 置信度 0.80
• A novel AI system for preliminary triage support in single-tooth edentulous spaces using intraoral images.
  This study aimed to demonstrate the integration of deep learning (DL) and machine learning (ML) using only occlusal photographs to provide preliminary triage support between orthodontic and prosthodontic treatments in cases requiring alternatives to implant-ba…
  europepmc 2026 置信度 0.80
• Machine Learning-Based Prediction of Lung Cancer Risk in COPD Patients Using Clinical Severity Indicators and Comorbidity Profile
  Abstract Lung cancer remains a major cause of cancer-related mortality worldwide. It is clinically relevant in patients with chronic obstructive pulmonary disease (COPD), in whom shared risk factors and biological mechanisms may increase susceptibility. Early …
  europepmc 2026 置信度 0.80
• Identifying Risk Factors for Caregiver Burden in Neurological Disorders Using Machine Learning.
  Background: Caregiver burden represents a multidimensional syndrome influenced by patient-related, relational, and contextual factors in neurological disorders. Although stroke, Parkinson's disease (PD), and Alzheimer's disease (AD) differ in clinical trajecto…
  europepmc 2026 置信度 0.80
• Development of a machine learning-based noninvasive diagnostic model for liver fibrosis in metabolic-associated steatotic liver disease.
  Background To address the lack of simple tools for assessing fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD), this study develops a machine learning-based diagnostic model to identify patients at high risk of significant fibrosis a…
  europepmc 2026 置信度 0.80
• Resting-state fMRI-based machine learning for predicting SSRI treatment response in major depressive disorder.
  Background Major Depressive Disorder (MDD) is a prevalent mental health condition with significant societal impact. Although prior research has highlighted the brain changes modulated by antidepressant therapy, their efficacy and effectiveness are debated. The…
  europepmc 2026 置信度 0.80
• "A COMPUTATIONAL FRAMEWORK FOR MODERN HERBAL DRUG DEVELOPMENT": ARTIFICIAL INTELLIGENCE – DRIVEN PHARMACOVIGILANCE
  Herbal medicines are extensively utilized across the globe and constitute an important component of traditional and complementary healthcare practices. However, their safety is often inadequately monitored due to variability in composition, lack of standardiza…
  datacite Dr. Sethuramani A.1* , Thangam V.2 , Thirupavai B.2 , Umayambigai R.2 , Thillaisathana T.2, Dharani S.2 , Ganesh S.3, Aarthy S.3 2026 置信度 0.66
• PHARMACOLOGICAL APPROACHES TO DRUG REPURPOSING: MECHANISMS, STRATEGIES, AND CLINICAL APPLICATIONS
  Drug repurposing has emerged as a strategic approach to accelerate therapy development by identifying novel uses for existing drugs, offering reduced development time, lower costs, and established safety profiles. This review provides a comprehensive analysis …
  datacite Md. Al Amin1,2*, Joy Sarker2, Sree Karma Tigga2, Md. Ismail Kabir1 2026 置信度 0.66 Drug repurposing, Drug repositioning, Mechanism-based strategy, Phenotype-driven strategy, Computational pharmacology, Translational pharmacology.
• PHARMACOLOGICAL APPROACHES TO DRUG REPURPOSING: MECHANISMS, STRATEGIES, AND CLINICAL APPLICATIONS
  Drug repurposing has emerged as a strategic approach to accelerate therapy development by identifying novel uses for existing drugs, offering reduced development time, lower costs, and established safety profiles. This review provides a comprehensive analysis …
  datacite Md. Al Amin1,2*, Joy Sarker2, Sree Karma Tigga2, Md. Ismail Kabir1 2026 置信度 0.66 Drug repurposing, Drug repositioning, Mechanism-based strategy, Phenotype-driven strategy, Computational pharmacology, Translational pharmacology.
• COMPREHENSIVE REVIEW ON AI IN THE PHARMACEUTICAL INDUSTRY: NEW IDEAS, USES, AND WHAT THE FUTURE HOLDS
  AI is changing the pharmaceutical industry by making drug discovery, development, manufacturing, and patient care more efficient, accurate, and cost-effective. Conventional pharmaceutical processes frequently entail significant time consumption, elevated costs…
  datacite Aby Augustine*1, Sruthi E. J.1 2026 置信度 0.66
• COMPREHENSIVE REVIEW ON AI IN THE PHARMACEUTICAL INDUSTRY: NEW IDEAS, USES, AND WHAT THE FUTURE HOLDS
  AI is changing the pharmaceutical industry by making drug discovery, development, manufacturing, and patient care more efficient, accurate, and cost-effective. Conventional pharmaceutical processes frequently entail significant time consumption, elevated costs…
  datacite Aby Augustine*1, Sruthi E. J.1 2026 置信度 0.66
• Comparison of Machine Learning Approaches for Optimizing HIV Medication Outcomes
  This study explores how machine learning can support better treatment decisions for people living with HIV. Using a large clinical dataset of over 283,000 patient records from treatment facilities in eastern Nigeria, four supervised learning models were compar…
  datacite Okwuba, Praise 2026 置信度 0.66
• Comparison of Machine Learning Approaches for Optimizing HIV Medication Outcomes
  This study explores how machine learning can support better treatment decisions for people living with HIV. Using a large clinical dataset of over 283,000 patient records from treatment facilities in eastern Nigeria, four supervised learning models were compar…
  datacite Okwuba, Praise 2026 置信度 0.66
• AI Companion Mortality Database: Documented Deaths Associated with Conversational AI Systems (2023–2026)
  Superseded by version 3.5.3 (10.5281/zenodo.22063180). In this version, the derived files platform-analysis.csv and timeline.json were generated from v3.5.0 and do not reflect v3.5.2 changes (ChatGPT third-party fatalities 15 → 16; Margaux Whittemore outcome a…
  datacite Karman, Hunter 2026 置信度 0.66 AI safetyconversational AIchatbotsmortalitysuicide
• AI Companion Mortality Database: Documented Deaths Associated with Conversational AI Systems (2023–2026)
  A public, source-verified register of deaths in which AI chatbot interaction was alleged as a contributing factor, March 2023 through August 2026. Version 3.5.3 documents 35 fatalities across 24 incidents (occurrences of harm) on 8 tracked platforms: 18 AI use…
  datacite Karman, Hunter 2026 置信度 0.66 AI safetyconversational AIchatbotsmortalitysuicide
• TLMM v7.3: Adaptive Intervention Framework for Causal Decision Support in the Extended Amyloid–Blood Flow (ABF) Causal Cascade
  Topological Latent Manifold Model (TLMM) v7.3 extends the validated causal-inference framework of TLMM v7.2 by introducing an Adaptive Decision Layer for personalized, counterfactually evaluated, and safety-constrained adaptive intervention while preserving th…
  datacite Okino, Koji 2026 置信度 0.66 Topological Latent Manifold ModelTLMMAmyloid–Blood FlowABFAlzheimer's Disease
• TLMM v7.3: Adaptive Intervention Framework for Causal Decision Support in the Extended Amyloid–Blood Flow (ABF) Causal Cascade
  Topological Latent Manifold Model (TLMM) v7.3 extends the validated causal-inference framework of TLMM v7.2 by introducing an Adaptive Decision Layer for personalized, counterfactually evaluated, and safety-constrained adaptive intervention while preserving th…
  datacite Okino, Koji 2026 置信度 0.66 Topological Latent Manifold ModelTLMMAmyloid–Blood FlowABFAlzheimer's Disease
• Topological Latent Manifold Model (TLMM) v7.4: A Mechanistic, Falsifiable Framework for Closed-Loop Sequential Adaptive Intervention and Large-Scale Outcome Evaluation
  Topological Latent Manifold Model (TLMM) v7.4 is a mechanistically grounded methodological framework for closed-loop sequential adaptive intervention and large-scale outcome evaluation, developed in the context of Alzheimer's disease. This release introduces a…
  datacite Okino, Koji 2026 置信度 0.66 Topological Latent Manifold ModelTLMMAppearance-Behavior FrameworkABFMechanistic Machine Learning
• Topological Latent Manifold Model (TLMM) v7.4: A Mechanistic, Falsifiable Framework for Closed-Loop Sequential Adaptive Intervention and Large-Scale Outcome Evaluation
  Topological Latent Manifold Model (TLMM) v7.4 is a mechanistically grounded methodological framework for closed-loop sequential adaptive intervention and large-scale outcome evaluation, developed in the context of Alzheimer's disease. This release introduces a…
  datacite Okino, Koji 2026 置信度 0.66 Topological Latent Manifold ModelTLMMAppearance-Behavior FrameworkABFMechanistic Machine Learning
• An Integrated BioMEMS and Machine Learning Framework for Continuous Multi-Modal Physiological Monitoring with a Focus on Pediatric Applications
  This master's thesis develops and evaluates an integrated framework that unites biomedical microelectromechanical systems (BioMEMS) with machine learning (ML) for continuous, multi-modal physiological monitoring, with a focus on pediatric applications. It addr…
  datacite James-Knowles, Destinie A. 2026 置信度 0.66 Biomedical EngineeringMachine LearningHealth Equity
• An Integrated BioMEMS and Machine Learning Framework for Continuous Multi-Modal Physiological Monitoring with a Focus on Pediatric Applications
  This master's thesis develops and evaluates an integrated framework that unites biomedical microelectromechanical systems (BioMEMS) with machine learning (ML) for continuous, multi-modal physiological monitoring, with a focus on pediatric applications. It addr…
  datacite James-Knowles, Destinie A. 2026 置信度 0.66 Biomedical EngineeringMachine LearningHealth Equity
• Intelligent Decision Support System for Healthcare
  The advanced technologies of the Internet of Things (IoT) provide modern environments support for medical applications and produce a vast amount of various types of health care data, such as sensors data, clinical data, omic data, and later transfer these data…
  datacite Anup Patnaik 2024 置信度 0.66
• Intelligent Decision Support System for Healthcare
  The advanced technologies of the Internet of Things (IoT) provide modern environments support for medical applications and produce a vast amount of various types of health care data, such as sensors data, clinical data, omic data, and later transfer these data…
  datacite Anup Patnaik 2024 置信度 0.66
• AN ADVANCED DEEP LEARNING AND COMPUTER VISION BASED SYSTEM FOR LIVER TUMOR DETECTION AND LOCALIZATION WITH MACHINE LEARNING
  The proposed system focuses on developing an intelligent medical imaging and predictive platform for liver tumor detection localization and disease analysis using advanced computational techniques. The framework integrates deep learning computer vision and mac…
  datacite Ramesh Patil, Tuba Shazmeen 2026 置信度 0.66 Liver Tumor DetectionDeep LearningComputer VisionMachine LearningCT
• AN ADVANCED DEEP LEARNING AND COMPUTER VISION BASED SYSTEM FOR LIVER TUMOR DETECTION AND LOCALIZATION WITH MACHINE LEARNING
  The proposed system focuses on developing an intelligent medical imaging and predictive platform for liver tumor detection localization and disease analysis using advanced computational techniques. The framework integrates deep learning computer vision and mac…
  datacite Ramesh Patil, Tuba Shazmeen 2026 置信度 0.66 Liver Tumor DetectionDeep LearningComputer VisionMachine LearningCT
• Machine learning for early prediction of subsequent stage 2-3 acute kidney injury in critically ill adults: hospital-disjoint internal-external validation — reproducibility materials
  Source-only code, parameterized SQL, analysis specifications, disclosure-safe aggregate outputs, final publication figures, and documentation aligned with the A25 submission-locked manuscript. Scientific results are unchanged. No patient-level data or Data Use…
  datacite Yilmaz, Cagdas, Akdagli, Ali 2026 置信度 0.66 acute kidney injuryKDIGO stage 2-3critical caremachine learninghospital-disjoint internal-external validation
• Machine learning for early prediction of subsequent stage 2-3 acute kidney injury in critically ill adults: hospital-disjoint internal-external validation — reproducibility materials
  Source-only code, parameterized SQL, analysis specifications, disclosure-safe aggregate outputs, final publication figures, and documentation aligned with the A25 submission-locked manuscript. Scientific results are unchanged. No patient-level data or Data Use…
  datacite Yilmaz, Cagdas, Akdagli, Ali 2026 置信度 0.66 acute kidney injuryKDIGO stage 2-3critical caremachine learninghospital-disjoint internal-external validation
• Machine learning for early prediction of subsequent stage 2-3 acute kidney injury in critically ill adults: hospital-disjoint internal-external validation — reproducibility materials
  Source-only code, parameterized SQL, analysis specifications, disclosure-safe aggregate outputs, final publication figures, and documentation aligned with the A25 submission-locked manuscript. Scientific results are unchanged. No patient-level data or Data Use…
  datacite Yilmaz, Cagdas, Akdagli, Ali 2026 置信度 0.66 acute kidney injuryKDIGO stage 2-3critical caremachine learninghospital-disjoint internal-external validation
• PROSPECTS OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF TEMPOROMANDIBULAR JOINT DISORDERS (TMJD)
  Temporomandibular joint disorders (TMJD) represent a group of complex musculoskeletal and neuromuscular conditions affecting the temporomandibular joint (TMJ), masticatory muscles, and associated structures. Accurate diagnosis of TMJD remains challenging due t…
  datacite Axmedova Malika Qilichovna 2026 置信度 0.66
• PROSPECTS OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF TEMPOROMANDIBULAR JOINT DISORDERS (TMJD)
  Temporomandibular joint disorders (TMJD) represent a group of complex musculoskeletal and neuromuscular conditions affecting the temporomandibular joint (TMJ), masticatory muscles, and associated structures. Accurate diagnosis of TMJD remains challenging due t…
  datacite Axmedova Malika Qilichovna 2026 置信度 0.66
• Comparison of Ensemble Method Performance in Classifying Blood Sugar Levels Output from Non-Invasive Device
  Diabetes Mellitus (DM) is a persistent health issue in many countries and is a leading cause of heart disease, kidney failure, and blindness The International Diabetes Federation (IDF) estimated in 2019 that at least 463 million people worldwide aged 20-79 suf…
  datacite Nurrizqi, Alfi Indah, Erfiani, Soleh, Agus Mohamad 2024 置信度 0.66 Blood Glucose; Ensemble Learning; Non-Invasive Device; Rotation Forest
• Comparison of Ensemble Method Performance in Classifying Blood Sugar Levels Output from Non-Invasive Device
  Diabetes Mellitus (DM) is a persistent health issue in many countries and is a leading cause of heart disease, kidney failure, and blindness The International Diabetes Federation (IDF) estimated in 2019 that at least 463 million people worldwide aged 20-79 suf…
  datacite Nurrizqi, Alfi Indah, Erfiani, Soleh, Agus Mohamad 2024 置信度 0.66 Blood Glucose; Ensemble Learning; Non-Invasive Device; Rotation Forest
• ATLAS-A: ECG metadata controls
  This archive contains version 1.0.0 of ATLAS-A: ECG metadata controls, the reproducibility package for a study evaluating whether demographic and anthropometric metadata improve multi-label 12-lead ECG classification beyond ECG-only models. The archive include…
  datacite Segnane, Ezyn, Ommane, Younes, Peluffo-Ordóñez, Diego H., Mouadili, Maryam 等 2026 置信度 0.66 Reproducible researchPatient-disjoint evaluationCODE-15%PTB-XLConduction disorders
• ATLAS-A: ECG metadata controls
  This archive contains version 1.0.0 of ATLAS-A: ECG metadata controls, the reproducibility package for a study evaluating whether demographic and anthropometric metadata improve multi-label 12-lead ECG classification beyond ECG-only models. The archive include…
  datacite Segnane, Ezyn, Ommane, Younes, Peluffo-Ordóñez, Diego H., Mouadili, Maryam 等 2026 置信度 0.66 Reproducible researchPatient-disjoint evaluationCODE-15%PTB-XLConduction disorders
• Artificial Intelligence and Machine Learning in Pharmacovigilance: Adverse Drug Reaction Detection and Signal Management under ICH-GCP Compliance
  Pharmacovigilance (PV) is the science and activities relating to the detection, assessment, understanding, and prevention of adverse drug reactions (ADRs) and other drug-related problems. The exponential growth in global drug utilisation combined with the prol…
  datacite Sanjay R*, Hemaprasath M, Vedhanayagi Gunasekaran, Madhavan P, Hariharasudhan B, Koushik Kumaran E P, SarathKumar R 2026 置信度 0.66 pharmacovigilance; adverse drug reaction; machine learning; natural language processing; signal detection; ICH-GCP; drug safety; deep learning; MedDRA; benefit-risk assessment
• Artificial Intelligence and Machine Learning in Pharmacovigilance: Adverse Drug Reaction Detection and Signal Management under ICH-GCP Compliance
  Pharmacovigilance (PV) is the science and activities relating to the detection, assessment, understanding, and prevention of adverse drug reactions (ADRs) and other drug-related problems. The exponential growth in global drug utilisation combined with the prol…
  datacite Sanjay R*, Hemaprasath M, Vedhanayagi Gunasekaran, Madhavan P, Hariharasudhan B, Koushik Kumaran E P, SarathKumar R 2026 置信度 0.66 pharmacovigilance; adverse drug reaction; machine learning; natural language processing; signal detection; ICH-GCP; drug safety; deep learning; MedDRA; benefit-risk assessment
• Left ventricular hypertrophy as a predictor of adverse maternal and neonatal outcomes in chronic hypertension in pregnancy: a multicenter study
  The impact of chronic hypertension (CHTN) combined with left ventricular hypertrophy (LVH) on adverse maternal and fetal pregnancy outcomes remains unclear. This multicenter retrospective cohort study included pregnant women with CHTN from four tertiary hospit…
  datacite Bei Zhang, Guilong Pan, Wei Wang, Kai Zhang 等 2026 置信度 0.66 MedicineCell BiologyPhysiologyPharmacologyBiotechnology
• Left ventricular hypertrophy as a predictor of adverse maternal and neonatal outcomes in chronic hypertension in pregnancy: a multicenter study
  The impact of chronic hypertension (CHTN) combined with left ventricular hypertrophy (LVH) on adverse maternal and fetal pregnancy outcomes remains unclear. This multicenter retrospective cohort study included pregnant women with CHTN from four tertiary hospit…
  datacite Bei Zhang, Guilong Pan, Wei Wang, Kai Zhang 等 2026 置信度 0.66 MedicineCell BiologyPhysiologyPharmacologyBiotechnology
• AI-Based Healthcare Monitoring System for Parkinson's Disease
  This research report details the development of an intelligent monitoring system for Parkinson's Disease. By leveraging machine learning and AI algorithms, the system analyzes patient data to provide accurate diagnostic insights and continuous health tracking.…
  datacite Alouss, Esraa 2026 置信度 0.66 Artificial intelligenceArtificial IntelligenceArtificial Intelligence/standardsArtificial Intelligence/classificationMachine Learning/classification
• AI-Based Healthcare Monitoring System for Parkinson's Disease
  This research report details the development of an intelligent monitoring system for Parkinson's Disease. By leveraging machine learning and AI algorithms, the system analyzes patient data to provide accurate diagnostic insights and continuous health tracking.…
  datacite Alouss, Esraa 2026 置信度 0.66 Artificial intelligenceArtificial IntelligenceArtificial Intelligence/standardsArtificial Intelligence/classificationMachine Learning/classification
• Artificial Intelligence in Healthcare and Human Development: A Scientific and Contemporary Research Perspective (2026)
  Artificial Intelligence (AI) has emerged as a transformative force in healthcare and human development, reshaping diagnosis, treatment, preventive care, rehabilitation, mental wellness, and lifelong human capability enhancement. Recent advances in machine lear…
  datacite Prof. (Dr.) Deepak Shivlingrao Phulari 2026 置信度 0.66
• Artificial Intelligence in Healthcare and Human Development: A Scientific and Contemporary Research Perspective (2026)
  Artificial Intelligence (AI) has emerged as a transformative force in healthcare and human development, reshaping diagnosis, treatment, preventive care, rehabilitation, mental wellness, and lifelong human capability enhancement. Recent advances in machine lear…
  datacite Prof. (Dr.) Deepak Shivlingrao Phulari 2026 置信度 0.66
• ToxKAN-Dataset
  Motivation: Toxicity assessment is essential for drug development and clinical safety. However, a prevalent phenomenon in computational toxicology is the trade-off between predictive performance and mechanistic interpretability, where conventional machine lear…
  datacite Ziqing, Zhang 2026 置信度 0.66
• ToxKAN-Dataset
  Motivation: Toxicity assessment is essential for drug development and clinical safety. However, a prevalent phenomenon in computational toxicology is the trade-off between predictive performance and mechanistic interpretability, where conventional machine lear…
  datacite Ziqing, Zhang 2026 置信度 0.66
• Systems Biology in Biomedicine: Decoding Complex Diseases Through Network-Based Approaches
  This extensive review details the transformative impact of systems biology and network medicine on understanding and treating complex human diseases. Moving beyond traditional reductionist paradigms that focus on single causative agents, systems biology concep…
  datacite systems biological science 2026 置信度 0.66 systems biologynetwork medicinemulti-omics integrationbiomarker discoveryartificial intelligence
• Systems Biology in Biomedicine: Decoding Complex Diseases Through Network-Based Approaches
  This extensive review details the transformative impact of systems biology and network medicine on understanding and treating complex human diseases. Moving beyond traditional reductionist paradigms that focus on single causative agents, systems biology concep…
  datacite systems biological science 2026 置信度 0.66 systems biologynetwork medicinemulti-omics integrationbiomarker discoveryartificial intelligence
• Wizako-01/Transfer-learning-model: Stroke Risk Prediction App
  This repository contains a machine learning-based stroke risk prediction application developed using patient clinical and demographic data. The project covers the full machine learning workflow, including data preprocessing, exploratory analysis, model trainin…
  datacite Jonathan Wisdom 2026 置信度 0.66
• Multivariate Analysis of Nutritional Variables and Inflammatory Biomarkers Using AI Models for Clinical Cancer Risk Prediction
  The study showcases the application of complex machine learning models that study inflammation and nutrition together for high accuracy early cancer risk prediction. With the help of statistical preprocessing and feature nonlinearity, together with other machi…
  datacite P, Nirmala, C, Priyanka, Gupalo, Sergey, RN, Jegathambigai 等 2026 置信度 0.66 Cancer Risk Prediction; Inflammatory Biomarkers; Nutritional Variables; Machine Learning; Deep Learning
• Multivariate Analysis of Nutritional Variables and Inflammatory Biomarkers Using AI Models for Clinical Cancer Risk Prediction
  The study showcases the application of complex machine learning models that study inflammation and nutrition together for high accuracy early cancer risk prediction. With the help of statistical preprocessing and feature nonlinearity, together with other machi…
  datacite P, Nirmala, C, Priyanka, Gupalo, Sergey, RN, Jegathambigai 等 2026 置信度 0.66 Cancer Risk Prediction; Inflammatory Biomarkers; Nutritional Variables; Machine Learning; Deep Learning
• A Comprehensive Review of Machine Learning Techniques for Early Diagnosis of Cardiovascular Disease
  Heart diseases (CVDs) continue to be significant data has enabled the development of advanced ML models causes of morbidity in the world. mortality, and emphasizing the necessity of early, correct and prognostic and diagnostic systems that are ethically right.…
  datacite Mohd Hamid Azeez, Prof. Dr. Jameel Ahmad, Mr. Balmukund Maurya 2026 置信度 0.66 Cardiovascular DiseaseChronic Kidney DiseaseMachine LearningDeep LearningExplainable AI
• A Comprehensive Review of Machine Learning Techniques for Early Diagnosis of Cardiovascular Disease
  Heart diseases (CVDs) continue to be significant data has enabled the development of advanced ML models causes of morbidity in the world. mortality, and emphasizing the necessity of early, correct and prognostic and diagnostic systems that are ethically right.…
  datacite Mohd Hamid Azeez, Prof. Dr. Jameel Ahmad, Mr. Balmukund Maurya 2026 置信度 0.66 Cardiovascular DiseaseChronic Kidney DiseaseMachine LearningDeep LearningExplainable AI
• Clinical Assessment Methods for Early Autism Spectrum Disorder in Paediatrics Using Explainable Artificial Intelligence and Quantum Machine Learning - Figure 3
  Figure 3 illustrates the yearly distribution of dataset modalities, including EEG, MRI, multimodal, and facial datasets, across the reviewed studies.
  datacite Priya Shanthini D.r, Vasu Koduri, Naveen Maddukuri, M Kalpana Chowdary 等 2026 置信度 0.66
• Clinical Assessment Methods for Early Autism Spectrum Disorder in Paediatrics Using Explainable Artificial Intelligence and Quantum Machine Learning - Figure 3
  Figure 3 illustrates the yearly distribution of dataset modalities, including EEG, MRI, multimodal, and facial datasets, across the reviewed studies.
  datacite Priya Shanthini D.r, Vasu Koduri, Naveen Maddukuri, M Kalpana Chowdary 等 2026 置信度 0.66
• AutoRespire: A Multimodal AI-Driven Healthcare System for Tuberculosis and Pneumonia Classification
  This paper presents AutoRespire, a multimodal AI-driven healthcare system designed for the detection of Tuberculosis and Pneumonia. The system integrates chest X-ray image analysis with clinical symptom data to improve diagnostic accuracy. Advanced machine lea…
  datacite M, M.Siva Krishna, Priyanka, Potti, Deyyala, Sowmyadevi, Kolla, Vivek 等 2026 置信度 0.66 Multimodal deep learning, respiratory disease detection, tuberculosis (TB), pneumonia, chest X-ray analysis, convolutional neural networks (CNN), MobileNet, artificial neural networks (ANN), random forest, late fusion, medical image processing, clinical symptom analysis, healthcare AI, disease prediction system
• AutoRespire: A Multimodal AI-Driven Healthcare System for Tuberculosis and Pneumonia Classification
  This paper presents AutoRespire, a multimodal AI-driven healthcare system designed for the detection of Tuberculosis and Pneumonia. The system integrates chest X-ray image analysis with clinical symptom data to improve diagnostic accuracy. Advanced machine lea…
  datacite M, M.Siva Krishna, Priyanka, Potti, Deyyala, Sowmyadevi, Kolla, Vivek 等 2026 置信度 0.66 Multimodal deep learning, respiratory disease detection, tuberculosis (TB), pneumonia, chest X-ray analysis, convolutional neural networks (CNN), MobileNet, artificial neural networks (ANN), random forest, late fusion, medical image processing, clinical symptom analysis, healthcare AI, disease prediction system
• EARLY DIAGNOSIS OF POPULATION MORBIDITY USING ARTIFICIAL INTELLIGENCE: SOCIAL SIGNIFICANCE AND CLINICAL VALIDITY
  This article presents the development and expert evaluation of an AI agent ‒ an intelligent platform for early diagnosis of obstetric and gynaecological conditions. The agent operates in a conversational format (text messaging or voice call) and is designed to…
  datacite KUANDYK TOREKELDI TILEUBERDIULY, MUSAKHANOVA AKMARAL KALMAKHANBEKOVNA 2026 置信度 0.66 artificial intelligence, early diagnosis, obstetrics and gynaecology, AI agent, clinical decision-making, decision support system, conversational agent, machine learning, natural language processing.
• EARLY DIAGNOSIS OF POPULATION MORBIDITY USING ARTIFICIAL INTELLIGENCE: SOCIAL SIGNIFICANCE AND CLINICAL VALIDITY
  This article presents the development and expert evaluation of an AI agent ‒ an intelligent platform for early diagnosis of obstetric and gynaecological conditions. The agent operates in a conversational format (text messaging or voice call) and is designed to…
  datacite KUANDYK TOREKELDI TILEUBERDIULY, MUSAKHANOVA AKMARAL KALMAKHANBEKOVNA 2026 置信度 0.66 artificial intelligence, early diagnosis, obstetrics and gynaecology, AI agent, clinical decision-making, decision support system, conversational agent, machine learning, natural language processing.
• SOLID LIPID NANOPARTICLES IN CONTROLLED DRUG DELIVERY
  Solid Lipid Nanoparticles (SLNs) have attracted growing interest in pharmaceutical research over the past three decades. Their capacity to carry both lipophilic and moderately hydrophilic drugs, shield sensitive molecules during circulation, and navigate biolo…
  datacite 1Uzair Aziz Mir, 2*Mr. Neeku Singh 2026 置信度 0.66
• SOLID LIPID NANOPARTICLES IN CONTROLLED DRUG DELIVERY
  Solid Lipid Nanoparticles (SLNs) have attracted growing interest in pharmaceutical research over the past three decades. Their capacity to carry both lipophilic and moderately hydrophilic drugs, shield sensitive molecules during circulation, and navigate biolo…
  datacite 1Uzair Aziz Mir, 2*Mr. Neeku Singh 2026 置信度 0.66
• Performance Inflation and Reporting Biases in Respiratory Artificial Intelligence: A Data-Driven Landscape Analysis
  This dataset supports the manuscript "Performance Inflation and Reporting Biases in Respiratory Artificial Intelligence: A Data-Driven Landscape Analysis." It contains metadata extracted from 446 peer-reviewed studies detailing the application of artificial in…
  datacite Jubair, Hassan 2026 置信度 0.66
• Performance Inflation and Reporting Biases in Respiratory Artificial Intelligence: A Data-Driven Landscape Analysis
  This dataset supports the manuscript "Performance Inflation and Reporting Biases in Respiratory Artificial Intelligence: A Data-Driven Landscape Analysis." It contains metadata extracted from 446 peer-reviewed studies detailing the application of artificial in…
  datacite Jubair, Hassan 2026 置信度 0.66
• Artificial intelligence in drug discovery: reshaping the future of therapeutics
  Traditional drug discovery methods involve target identification, hit to lead optimization, preclinical and clinical trials that are often time consuming, expensive with high failure rates. Artificial intelligence (AI) has emerged as an innovative and transfor…
  datacite Harithalakshmi Jandhyam 2026 置信度 0.66 Artificial intelligenceDrug discoveryTherapeutics
• Artificial intelligence in drug discovery: reshaping the future of therapeutics
  Traditional drug discovery methods involve target identification, hit to lead optimization, preclinical and clinical trials that are often time consuming, expensive with high failure rates. Artificial intelligence (AI) has emerged as an innovative and transfor…
  datacite Harithalakshmi Jandhyam 2026 置信度 0.66 Artificial intelligenceDrug discoveryTherapeutics
• An improved framework for detecting Thyroid disease and Thyroid Cancer using Filter Based Feature Selection
  Thyroid issues and thyroid cancer are among the most frequent endocrine disorders that necessitate the earliest and most precise diagnosis to avert severe health issues. The methods usually applied in diagnostics are often protracted and can be hampered by lim…
  datacite Chetan Padole, Rashmi Lambhate, Chetan Rathod, Ayush Nandurkar 等 2026 置信度 0.66
• An improved framework for detecting Thyroid disease and Thyroid Cancer using Filter Based Feature Selection
  Thyroid issues and thyroid cancer are among the most frequent endocrine disorders that necessitate the earliest and most precise diagnosis to avert severe health issues. The methods usually applied in diagnostics are often protracted and can be hampered by lim…
  datacite Chetan Padole, Rashmi Lambhate, Chetan Rathod, Ayush Nandurkar 等 2026 置信度 0.66
• COSFAMM Baseline Dataset: Psychosocial and Clinical Predictors of Depression in Older Mexican Adults (2014–2019)
  This dataset accompanies the article “Depressive Symptom Predictors in Older Mexican Adults: Interaction Structures and Non‑Linear Effects from Machine Learning Explainability.” It contains baseline data from 1,252 adults aged ≥ 60 years participating in the C…
  datacite Efrén, Murillo-Zamora 2026 置信度 0.66