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• Interpretable Deep Learning Radiomics for Differentiating Pleomorphic Adenoma and Warthin Tumor.
  Background/aim Preoperative differentiation between pleomorphic adenoma (PA) and Warthin tumor (WT) is essential for optimizing surgical strategies. This study aimed to develop and validate an interpretable machine learning framework integrating clinical data,…
  europepmc 2026 置信度 0.80
• A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).
  Background The prevalence of post-traumatic stress disorder (PTSD) among South Korean firefighters is likely to be under-reported because of stigma and defensive attitudes. We aimed to develop a machine learning-based classification model, named Firefighter PT…
  europepmc 2026 置信度 0.80
• Construction and validation of a machine learning-based model for predicting pneumonia risk in patients with hemorrhagic stroke.
  Objective This study aimed to develop and validate a distinct, stable, and interpretable predictive model using machine learning techniques to identify individuals at high risk of pneumonia early after admission. The goal was to provide a potential quantitativ…
  europepmc 2026 置信度 0.80
• Machine learning-based prediction of bronchiolitis in children under two years: a multicenter study within a single metropolitan area.
  Background Bronchiolitis is a leading cause of hospitalization in infants, yet early and accurate risk prediction remains a clinical challenge. Traditional models often lack the complexity to capture nonlinear interactions or the transparency required for clin…
  europepmc 2026 置信度 0.80
• Advancing In-Context Learning for Efficient and Stable Medical Report Generation.
  Vision-language models (VLMs) have shown strong generalization across multimodal tasks, but adapting them to medical report generation (MRG) often demands extensive paired image-text data that are limited due to data privacy and annotation cost. In-context lea…
  europepmc 2026 置信度 0.80
• Development and validation of a machine learning model for colorectal cancer status classification using NHANES data: A cross-sectional study.
  Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide. Tools based on routinely collected variables may help identify prevalent CRC status and support clinical evaluation. Traditional approaches often rely on limited pr…
  europepmc 2026 置信度 0.80
• Development and External Validation of an Explainable Machine-Learning Model for Predicting Postoperative Pulmonary Complications in Older Adults Undergoing Degenerative Spine Surgery.
  Background : Postoperative pulmonary complications (PPCs) remain common in older adults undergoing degenerative spine surgery and are associated with adverse outcomes. We aimed to develop and externally validate an explainable machine-learning model using rout…
  europepmc 2026 置信度 0.80
• Development and validation of a machine learning model for sperm DNA fragmentation rate in infertile men: a multicenter retrospective study.
  Background The sperm DNA fragmentation index (DFI) provides important reference for evaluating male fertility and assisted reproductive outcomes, and its degree of damage is influenced by multiple factors. Our study aims to develop a machine learning-based pre…
  europepmc 2026 置信度 0.80
• Integrated Intratumoral and Peritumoral Ultrasound Radiomics Models for Breast Nodule Diagnosis Using Machine Learning.
  Aims/background The differentiation of benign and malignant breast nodules, particularly those categorized as Breast Imaging Reporting and Data System (BI-RADS) 3-4, remains a clinical challenge due to the subjectivity and operator dependence of conventional u…
  europepmc 2026 置信度 0.80
• Perioperative prediction of adnexal malignancy by an interpretable machine learning model for guiding ovarian preservation in premenopausal endometrial cancer.
  Background Ovarian preservation in premenopausal patients with endometrial cancer remains challenging due to the potential presence of concurrent adnexal malignancy. To support surgical decision-making, we developed an interpretable machine learning model for …
  europepmc 2026 置信度 0.80
• Analysis of health anxiety trajectories in rabies exposure patients based on machine learning.
  Objective To identify the dynamic evolution trajectory of health anxiety in patients with initial rabies exposure and to conduct predictive analysis of health anxiety trajectories using machine learning algorithms. Methods A total of 548 rabies exposure patien…
  europepmc 2026 置信度 0.80
• Prediction and mechanistic insights into drug-induced reproductive toxicity through integrated machine learning, FAERS-based signal comparison, and network toxicology analyses.
  Drug-induced reproductive toxicity is a critical concern in drug safety evaluation, whereas conventional assessment methods are often constrained by high costs and long experimental cycles. In this study, a machine learning-based predictive model for reproduct…
  europepmc 2026 置信度 0.80
• Using Machine Learning to Predict Breast Cancer-Related Lymphedema Following Axillary Lymph Node Dissection.
  Background Breast cancer-related lymphedema (BCRL) is a common and debilitating sequela of axillary lymph node dissection (ALND). Although machine learning (ML)-based prediction models have been proposed, few focus exclusively on patients undergoing ALND, and …
  europepmc 2026 置信度 0.80
• Machine Learning Applications for Opioid Use Management in Chronic Cancer Pain: A Systematic Scoping Review.
  Chronic pain remains a critical clinical issue worldwide, with adverse effects on the quality of life of oncology patients. Meanwhile, the overuse of opioids to treat or alleviate chronic cancer pain has contributed to a global opioid crisis. The increasing ac…
  europepmc 2026 置信度 0.80
• Global trends and hotspots of machine learning in the diagnosis of prostate cancer: a bibliometric analysis from 1997 to 2024
  Purpose Prostate cancer (PCa) diagnosis has advanced with the integration of machine learning (ML). This study analyzes global trends in ML-based PCa diagnosis research using bibliometric methods. Methods A systematic search was conducted in the Web of Science…
  europepmc 2026 置信度 0.80
• Development and validation of an interpretable machine learning model for predicting systemic inflammatory response syndrome after percutaneous nephrolithotomy: A multicenter study.
  Background Systemic inflammatory response syndrome (SIRS) is a common and potentially reversible early infectious complication after percutaneous nephrolithotomy (PCNL). Accurate perioperative risk stratification may enable timely intervention; however, existi…
  europepmc 2026 置信度 0.80
• Machine learning models in post-stroke aphasia: a scoping review.
  Objective To systematically review the literature on the application of machine learning models in post-stroke aphasia, and to provide a reference for the construction and clinical application of related models. Methods Based on scoping review methodology, we …
  europepmc 2026 置信度 0.80
• Predicting Outcomes of Traumatic Brain Injury Using Machine Learning Models Among Patients at Kilimanjaro Christian Medical Centre, Tanzania: A Registry-Based Cohort Study.
  Background Traumatic brain injury (TBI) remains a major global health burden, disproportionately affecting low- and middle-income countries (LMICs) where access to neurocritical care is limited. Accurate and context-appropriate prognostic models are crucial to…
  europepmc 2026 置信度 0.80
• A Web-Based Machine Learning Calculator for Predicting Preoperative Deep Vein Thrombosis in Elderly Hip Fractures Patients.
  Purpose Hip fractures are often associated with deep vein thrombosis (DVT). This study aimed to develop machine learning models to predict preoperative DVT risk using basic clinical data from elderly patients with hip fractures. Methods Clinical data were retr…
  europepmc 2026 置信度 0.80
• Development and temporal validation of a machine learning model to estimate total MoCA scores from telephone-based items in patients with mild cognitive impairment.
  Background Periodic cognitive monitoring for mild cognitive impairment (MCI) is crucial. However, standard assessments like the Montreal Cognitive Assessment (MoCA) require in-person administration due to visual-based items, creating significant accessibility …
  europepmc 2026 置信度 0.80
• EEG-Based Seizure Prediction Approaches Within Clinically Relevant Pre-seizure Windows Using Scalp EEG Datasets: A Systematic Review.
  This systematic review synthesized evidence on EEG-based seizure prediction within clinically relevant pre-seizure windows, focusing on deep learning and machine learning models evaluated on public datasets. The review aimed to summarize predictive performance…
  europepmc 2026 置信度 0.80
• Development and validation of a routine blood test-based model to predict in-hospital postoperative pulmonary infection in older patients with hip fracture.
  Background Postoperative pulmonary infection (PPI) is a common and serious complication in older adults undergoing hip fracture surgery, leading to prolonged hospitalization, increased costs, and increased mortality. However, simple and reliable preoperative p…
  europepmc 2026 置信度 0.80
• Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department.
  Background Pulmonary thromboembolism (PTE) is a life‑threatening condition that requires prompt and accurate evaluation in the emergency department (ED). Standardized clinical scoring systems, including the Wells and revised Geneva scores, form the cornerstone…
  europepmc 2026 置信度 0.80
• Machine learning-based integrative analysis identifies CXCL13-driven tertiary lymphoid structures as favorable immune and prognostic features in osteosarcoma.
  Background Osteosarcoma, an aggressive bone malignancy with limited response to immunotherapy, remains a major clinical challenge. Tertiary lymphoid structures (TLS), as organized ectopic lymphoid aggregates, play a pivotal role in modulating antitumor immune …
  europepmc 2026 置信度 0.80
• Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health.
  Objective Patients hospitalized for diabetes-related conditions face elevated risks of emergency department (ED) visits post-discharge, driven by both clinical factors and social determinants of health (SDoH). This study aimed to develop and validate predictiv…
  europepmc 2026 置信度 0.80
• Machine Learning-Based Predictive Model for Grade 3 Primary Graft Dysfunction Following Lung Transplantation: A Retrospective Cohort Study.
  Background This study aimed to identify key predictors for Grade 3 Primary Graft Dysfunction (PGD) after lung transplantation. Machine learning (ML) algorithm models were constructed for early clinical identification of high-risk PGD patients based on these pr…
  europepmc 2026 置信度 0.80
• Machine Learning Prediction of Pediatric In-Hospital Survival Before Extracorporeal Membrane Oxygenation Cannulation.
  Identifying suitable candidates for extracorporeal membrane oxygenation (ECMO) is still challenging. Our aim is to leverage machine learning (ML) to predict survival and identify critical variables influencing outcomes in pediatric patients requiring venovenou…
  europepmc 2026 置信度 0.80
• Development and validation of machine learning diagnostic models integrating clinical, CT, and laboratory features to differentiate lung cancer from pulmonary tuberculosis in patients with solitary pulmonary nodules: a single-center retrospective study.
  Background Differentiating lung cancer from pulmonary tuberculosis in patients with solitary pulmonary nodules (SPNs) remains clinically challenging, particularly in tuberculosis-endemic settings, because these two conditions may show overlapping computed tomo…
  europepmc 2026 置信度 0.80
• Machine learning to identify novel bleeding and residual thromboembolic risks in patients on anticoagulation.
  Balancing thromboembolic prevention against bleeding risk remains a key challenge during oral anticoagulant (OAC) therapy. CHA₂DS₂-VASc cannot predict residual thromboembolic risk, and HAS-BLED has insufficient predictive ability for direct oral anticoagulants…
  europepmc 2026 置信度 0.80
• Development and validation of a deep neural network for predicting coronary heart disease in hypertensive patients using 24-hour ambulatory blood pressure monitoring: a retrospective study.
  Background Coronary heart disease (CHD) remains a leading cause of morbidity and mortality worldwide. Early identification of high-risk hypertensive patients is crucial for preventing cardiovascular events. While traditional risk scores rely on static clinical…
  europepmc 2026 置信度 0.80
• Identification and Validation of an Explainable Predictive Model For Heart Failure in Patients With Hypertension.
  Background Heart failure (HF) is a heterogeneous syndrome affecting over 60 million individuals globally. Patients with hypertension are particularly susceptible to developing HF. Therefore, timely identification and predictive assessment of HF risk have signi…
  europepmc 2026 置信度 0.80
• Integrated multi-omics and functional analyses reveal a GDCA-TGR5-mediated immunometabolic signature in anorexia nervosa.
  Background Anorexia nervosa (AN) is frequently accompanied by metabolic disturbances and immune dysregulation, yet its peripheral molecular mechanisms and therapeutic targets remain poorly understood. This study integrates multi-omics analyses with experimenta…
  europepmc 2026 置信度 0.80
• AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application.
  Machine learning models hold promise to revolutionize cardiovascular disease (CVD) prediction in patients with type 2 diabetes, with algorithms such as neural networks demonstrating superior discriminative performance in internal validations. However, a system…
  europepmc 2026 置信度 0.80
• Performance of predictive AI-based clinical decision support systems across clinical domains: A systematic review and meta-analysis.
  Despite advances in deep learning and transformer architectures, prior reviews have focused narrowly on traditional clinical decision support systems (CDSS) or single medical domains, leaving significant gaps in understanding contemporary AI-driven predictive …
  europepmc 2026 置信度 0.80
• Surgical outcomes and risk factors identification for rhegmatogenous retinal detachment repair by pneumatic retinopexy using pure air.
  Objective This study aimed to evaluate the clinical outcomes and identify risk factors for failure of pneumatic retinopexy (PR) using pure air. Methods A retrospective analysis was conducted on 199 eyes from 199 patients with uncomplicated RRD who underwent PR…
  europepmc 2026 置信度 0.80
• Developing and Validating a Machine Learning Model to Predict Brain Injury in Preterm Infants Using Multisource Data from the Early Postnatal Period.
  Background Moderate-to-severe preterm brain injury (PBI), including intraventricular hemorrhage (IVH) and periventricular leukomalacia (PVL), remains an important cause of adverse neurodevelopmental outcomes in preterm infants. Early risk stratification using …
  europepmc 2026 置信度 0.80
• Classification of malignant/benign groups in lung cancer by machine learning and investigation of feature significance of parameters.
  Objectives This study aimed to apply machine learning (ML) models to enhance lung cancer (LC) classification, distinguishing malignant from benign tumors, using data from 73 patients. Methods The dataset included PET/CT biomarkers and demographic factors, with…
  europepmc 2026 置信度 0.80
• Enhanced Survival Prediction in Head and Neck Cancer Using Convolutional Block Attention and Multimodal Data Fusion
  Accurate survival prediction in head and neck cancer (HNC) is essential for guiding clinical decision-making and optimizing treatment strategies. Traditional models, such as Cox proportional hazards, have been widely used but are limited in their ability to ha…
  datacite Farooq, Aiman, Sharma, Utkarsh, Mishra, Deepak 2024 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• MeSH2Wikidata: A set of tools for the interaction between MeSH keywords, OBO Foundry, and Wikidata for enriching biomedical knowledge
  The work consists of tools for the interaction between Wikidata and OBO Foundry and source codes for the use of MeSH keywords of PubMed publications for the enrichment of biomedical knowledge in Wikidata. This work is funded by the Adapting Wikidata to support…
  datacite Turki, Houcemeddine, Chebil, Khalil, Dossou, Bonaventure, Emezue, Chris 等 2023 置信度 0.66 Knowledge and information managementInformation modelling, management and ontologiesInformation systems organisation and managementInformation retrieval and web searchData mining and knowledge discovery
• Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods
  Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical utility, safety, and fairness. This can affect known patient groups - such as t…
  datacite Olesen, Vincent, Weng, Nina, Feragen, Aasa, Petersen, Eike 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Computer Vision and Pattern Recognition (cs.CV)Computers and Society (cs.CY)FOS: Computer and information sciences
• Grand Challenge: Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024
  The Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Grand Challenge is a 2024 MICCAI Grand Challenge. PI Fuller presented the keynote virtually and in-person on 2024-10-17T0900, describing the relevant issues in biomedical image seg…
  datacite Fuller, Clifton D. 2024 置信度 0.66 Radiology and organ imagingBiomedical imagingComputational imagingArtificial intelligence not elsewhere classifiedMachine learning not elsewhere classified
• The Phantom EEG Dataset
  When you use this dataset, please cite this paper. More information about this dataset could also be found in this paper. Xu, X., Wang, B., Xiao, B., Niu, Y., Wang, Y., Wu, X., & Chen, J. (2024). Beware of Overestimated Decoding Performance Arising from Tempor…
  datacite Anonymous 2024 置信度 0.66
• The Phantom EEG Dataset
  When you use this dataset, please cite this paper. More information about this dataset could also be found in this paper. Xu, X., Wang, B., Xiao, B., Niu, Y., Wang, Y., Wu, X., & Chen, J. (2024). Beware of Overestimated Decoding Performance Arising from Tempor…
  datacite Anonymous 2024 置信度 0.66
• The Phantom EEG Dataset
  New version on https://zenodo.org/records/13341214. When you use this dataset, please cite this paper. More information about this dataset could also be found in this paper. Xu, X., Wang, B., Xiao, B., Niu, Y., Wang, Y., Wu, X., & Chen, J. (2024). Beware of Ov…
  datacite Anonymous 2024 置信度 0.66
• CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT
  The widespread adoption of Electronic Health Records (EHR) has significantly increased the amount of available healthcare data. This has allowed models inspired by Natural Language Processing (NLP) and Computer Vision, which scale exceptionally well, to be use…
  datacite Odgaard, Mikkel, Klein, Kiril Vadimovic, Thysen, Sanne Møller, Jimenez-Solem, Espen 等 2024 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Journal of Student-Scientists' Research, Vol. 6 (2024)
  Abstracts submitted by the Aspiring Scientists Summer Internship Program cohort of 2024. Each describes an 8-week project conducted under faculty mentorship at George Mason University that was publically presented at our in-person or remote research symposiums…
  datacite Journal Of Student-Scientists' Research 2024 置信度 0.66
• Advancing Vascular Surgery: The Role Of Artificial Intelligence And Machine Learning In Managing Carotid Stenosis
  Introduction: Cardiovascular diseases affect 17.7 million people annually, worldwide. Carotid degenerative disease, commonly described as atherosclerotic plaque accumulation, significantly contributes to this, posing a risk for cerebrovascular events and ische…
  datacite Pias, Ana Daniela, Pereira-Macedo, Juliana, Marreiros, Ana, António, Nuno 等 2024 置信度 0.66
• Machine learning applications in studying mental health among immigrants and racial and ethnic minorities: an exploratory scoping review
  Abstract Background The use of machine learning (ML) in mental health (MH) research is increasing, especially as new, more complex data types become available to analyze. By examining the published literature, this review aims to explore the current applicatio…
  datacite Park, Khushbu Khatri, Saleem, Mohammad, Al-Garadi, Mohammed Ali, Ahmed, Abdulaziz 2024 置信度 0.66 BiotechnologyBiological Sciences not elsewhere classifiedScience Policy
• Machine learning applications in studying mental health among immigrants and racial and ethnic minorities: an exploratory scoping review
  Abstract Background The use of machine learning (ML) in mental health (MH) research is increasing, especially as new, more complex data types become available to analyze. By examining the published literature, this review aims to explore the current applicatio…
  datacite Park, Khushbu Khatri, Saleem, Mohammad, Al-Garadi, Mohammed Ali, Ahmed, Abdulaziz 2024 置信度 0.66 BiotechnologyBiological Sciences not elsewhere classifiedScience Policy
• Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones
  Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a…
  datacite Ianevski, Aleksandr, Nader, Kristen 2024 置信度 0.66
• Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones
  Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a…
  datacite Ianevski, Aleksandr, Nader, Kristen 2024 置信度 0.66
• Collaborative learning of common latent representations in routinely collected multivariate ICU physiological signals
  In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping. In contrast to previous research focused on electronic health records (EHR), here we propose an ML approa…
  datacite Haule, Hollan, Piper, Ian, Jones, Patricia, Lo, Tsz-Yan Milly 等 2024 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• The Impact of Technology and Digital Health on Cardiology: A Review of the Present to Reach the Future
  The integration of digital health technologies is revolutionizing the field of cardiology, particularly in the diagnosis, treatment, and management of cardiovascular diseases (CVDs). The rapid advancements in wearable devices, artificial intelligence (AI), and…
  datacite Couto Da Rocha, Sofia, Ramos, Alberto 2024 置信度 0.66
• <b>Datos_individuos</b>
  What is Cholesterol Concentration and Its Relationship with PPG Signals? Cholesterol concentration refers to the total amount of cholesterol present in the blood, measured in milligrams per deciliter (mg/dL). While cholesterol is a vital fatty substance necess…
  datacite ortiz, Juan david 2024 置信度 0.66 Clinical chemistry (incl. diagnostics)
• <b>Datos_individuos</b>
  What is Cholesterol Concentration and Its Relationship with PPG Signals? Cholesterol concentration refers to the total amount of cholesterol present in the blood, measured in milligrams per deciliter (mg/dL). While cholesterol is a vital fatty substance necess…
  datacite ortiz, Juan david 2024 置信度 0.66 Clinical chemistry (incl. diagnostics)
• Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling
  The paper proposes a novel approach of survival transformers and extreme gradient boosting models in predicting cognitive deterioration in individuals with mild cognitive impairment (MCI) using metabolomics data in the ADNI cohort. By leveraging advanced machi…
  datacite Musto, Henry, Stamate, Daniel, Logofatu, Doina, Stahl, Daniel 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Neural and Evolutionary Computing (cs.NE)FOS: Computer and information sciencesFOS: Computer and information sciences
• Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients
  Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia and is associated with increased morbidity and mortality. The effectiveness of current clinical interventions for AF is often limited by an incomplete understanding of the atrial anatomi…
  datacite Beveridge, Lucas, Zhang, Le 2024 置信度 0.66 Image and Video Processing (eess.IV)Computer Vision and Pattern Recognition (cs.CV)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineeringFOS: Computer and information sciences
• Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces
  Brain-machine interfaces (BMIs) are expanding beyond clinical settings thanks to advances in hardware and algorithms. However, they still face challenges in user-friendliness and signal variability. Classification models need periodic adaptation for real-life …
  datacite Mei, Lan, Cioflan, Cristian, Ingolfsson, Thorir Mar, Kartsch, Victor 等 2024 置信度 0.66 Signal Processing (eess.SP)Systems and Control (eess.SY)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineering
• Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments
  Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. This gap re…
  datacite Vermani, Ayesha, Dowling, Matthew, Jeon, Hyungju, Jordan, Ian 等 2024 置信度 0.66 Neurons and Cognition (q-bio.NC)FOS: Biological sciencesFOS: Biological sciences
• When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
  The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical data pivo…
  datacite Gao, Yanjun, Myers, Skatje, Chen, Shan, Dligach, Dmitriy 等 2024 置信度 0.66 Computation and Language (cs.CL)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Evaluating the Prognostic Accuracy of Two Severity Scoring Systems in Acute Kidney Failure
  Background: Acute kidney failure (AKF), a rapid loss of kidney function, poses significant morbidity and mortality risks, particularly in hospitalized and critically ill patients. Accurate prognostic tools are essential for effective clinical management and im…
  datacite Shashank Shekhar, Preeti Sinha, Wahengbam Purnakishor Singh 2024 置信度 0.66 Acute Kidney Failure, Prognostic Accuracy, Severity Scoring Systems, Clinical Outcomes, ROC Curve Analysis
• Evaluating the Prognostic Accuracy of Two Severity Scoring Systems in Acute Kidney Failure
  Background: Acute kidney failure (AKF), a rapid loss of kidney function, poses significant morbidity and mortality risks, particularly in hospitalized and critically ill patients. Accurate prognostic tools are essential for effective clinical management and im…
  datacite Shashank Shekhar, Preeti Sinha, Wahengbam Purnakishor Singh 2024 置信度 0.66 Acute Kidney Failure, Prognostic Accuracy, Severity Scoring Systems, Clinical Outcomes, ROC Curve Analysis
• Webinars - HSI Awardee Grantsmanship
  The HSI National STEM Resource Hub is preserving its website and compiled resources for future use and reference. This entry consists of the following webinars. Webinar Summer Grantsmanship Resources. Webinar Link: https://youtu.be/YwW-lbUA4xw Dr. Ellen Carpen…
  datacite HSI National STEM Resource Hub, Serrano, Elba, Morales, Gabriela, Peterman, Jarod 等 2024 置信度 0.66
• Webinars - HSI Awardee Grantsmanship
  The HSI National STEM Resource Hub is preserving its website and compiled resources for future use and reference. This entry consists of the following webinars. Webinar Summer Grantsmanship Resources. Webinar Link: https://youtu.be/YwW-lbUA4xw Dr. Ellen Carpen…
  datacite HSI National STEM Resource Hub, Serrano, Elba, Morales, Gabriela, Peterman, Jarod 等 2024 置信度 0.66
• Prognostic Significance of Tumor Budding in Bladder Cancer: A Call for Molecular Insights
  Dear Editor, We wish to share our insights on the critical prognostic significance of tumor budding (TB) in bladder cancer (BC) and underscore the urgent necessity for further molecular-level investigations in this domain, prompted by recent studies. Tumor bud…
  datacite Doğan, Tunay 2024 置信度 0.66
• Prognostic Significance of Tumor Budding in Bladder Cancer: A Call for Molecular Insights
  Dear Editor, We wish to share our insights on the critical prognostic significance of tumor budding (TB) in bladder cancer (BC) and underscore the urgent necessity for further molecular-level investigations in this domain, prompted by recent studies. Tumor bud…
  datacite Doğan, Tunay 2024 置信度 0.66
• Self-supervised Speech Models for Word-Level Stuttered Speech Detection
  Clinical diagnosis of stuttering requires an assessment by a licensed speech-language pathologist. However, this process is time-consuming and requires clinicians with training and experience in stuttering and fluency disorders. Unfortunately, only a small per…
  datacite Shih, Yi-Jen, Gkalitsiou, Zoi, Dimakis, Alexandros G., Harwath, David 2024 置信度 0.66 Audio and Speech Processing (eess.AS)Artificial Intelligence (cs.AI)Computation and Language (cs.CL)Sound (cs.SD)FOS: Electrical engineering, electronic engineering, information engineering
• Questioning AI: Promoting Decision-Making Autonomy Through Reflection
  Decision-making is increasingly supported by machine recommendations. In healthcare, for example, a clinical decision support system is used by the physician to find a treatment option for a patient. In doing so, people can rely too much on these systems, whic…
  datacite Fischer, Simon WS 2024 置信度 0.66 Human-Computer Interaction (cs.HC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Multi-feature Compensatory Motion Analysis for Reaching Motions Over a Discretely Sampled Workspace
  The absence of functional arm joints, such as the wrist, in upper extremity prostheses leads to compensatory motions in the users' daily activities. Compensatory motions have been previously studied for varying task protocols and evaluation metrics. However, t…
  datacite Yang, Qihan, Gloumakov, Yuri, Spiers, Adam J. 2024 置信度 0.66 Robotics (cs.RO)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models
  The estimated Glomerular Filtration Rate (eGFR) is an essential indicator of kidney function in clinical practice. Although traditional equations and Machine Learning (ML) models using clinical and laboratory data can estimate eGFR, accurately predicting futur…
  datacite Li, Chih-Yuan, Wu, Jun-Ting, Hsu, Chan, Lin, Ming-Yen 等 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia
  We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model integrates three components: an edge-based learning module that encodes functiona…
  datacite Chen, Zijian, Varkanitsa, Maria, Ishwar, Prakash, Konrad, Janusz 等 2024 置信度 0.66 Machine Learning (cs.LG)Signal Processing (eess.SP)Neurons and Cognition (q-bio.NC)FOS: Computer and information sciencesFOS: Computer and information sciences
• An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion
  Fairness of machine learning models in healthcare has drawn increasing attention from clinicians, researchers, and even at the highest level of government. On the other hand, the importance of developing and deploying interpretable or explainable models has be…
  datacite Lucas, Mary M., Wang, Xiaoyang, Chang, Chia-Hsuan, Yang, Christopher C. 等 2024 置信度 0.66 Machine Learning (cs.LG)Computers and Society (cs.CY)FOS: Computer and information sciencesFOS: Computer and information sciences
• Research on the Load_breast_cancer data set under Multiple Machine Learning Algorithms
  breast cancer ranks first in female malignant tumors. Early detection and diagnosis is the key to treatment. This paper uses the open-source load_break_cancer breast cancer data set, mainly uses random forest, support vector machine, logical regression, Gauss …
  crossref Meirui Song, Shixiao Wu, Rujuan Huang 2024-09-20T16:25:21Z 置信度 0.70
• A Survey on Skin Lesion Detection and Classification using Machine Learning
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