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BMC Medical Informatics and Decision Making
Scopus
ISSN 1472-6947 · BMC · 3.831 artikel di indeks
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Artikel Jurnal
Development and validation of a risk nomogram for predicting recurrence in patients with non-valvular atrial fibrillation after radiofrequency catheter ablation
Abstract Background Limited evidence exists regarding predictors of recurrence in patients with non-valvular atrial fibrillation (NVAF) following radiofrequency catheter ablation (RFCA). This study aimed to develop and validate a risk model for post-ablation recurrence in these patients. Methods…
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Artikel Jurnal
A machine learning model for the early prediction of Gram-negative bloodstream infection in ICU patients
Abstract Background Gram-negative bloodstream infection (GN-BSI) can induce fatal septic shock. Moreover, the increasingly severe problem of antimicrobial resistance has led to high clinical mortality, particularly among intensive care unit (ICU) patients. The early identification of pathogens and…
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Artikel Jurnal
Timing matters: a machine learning–driven comparison of community and hospital-onset sepsis
Abstract Background Sepsis is a leading cause of morbidity and mortality in hospitalized patients. While most sepsis research has focused on community-onset sepsis (COS), hospital-onset sepsis (HOS) is increasingly recognized as a distinct clinical entity with worse outcomes. However, a few studies…
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Artikel Jurnal
Machine learning-based screening of sarcopenia in elderly women using non-invasive physiological signals
Abstract Background Sarcopenia is the age related loss of skeletal muscle mass and strength, which increases frailty, fall risk, and healthcare costs. Conventional imaging based diagnostics are costly and impractical in routine clinical or community settings. We investigated whether noninvasive…
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Artikel Jurnal
Intention to use smartphone medical applications among patients with chronic disease in southwest Ethiopia: an extended UTAUT2 model approach
Abstract Background Chronic diseases are a major global health burden, particularly in low and middle-income countries like Ethiopia, where they significantly contribute to mortality. This underscores a critical need for effective prevention strategies. Smartphone medical applications (apps)…
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Artikel Jurnal
Dual-path deep learning framework for accurate and interpretable brain tumor diagnosis
Abstract Existing frameworks for brain tumor diagnosis often focus on standalone classification or retrieval tasks, limiting clinical interpretability and failing to leverage complementary diagnostic insights. To address this, we propose a novel dual-path deep learning framework that…
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Artikel Jurnal
Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy
Abstract Background Sepsis-Induced coagulopathy (SIC) is not only a common complication in the development process of sepsis but also related to poor prognosis of sepsis. We aimed to establish a machine learning (ML) model to predict the 28-day mortality risk of patients with SIC. Methods We…
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Artikel Jurnal
Textbook-level medical knowledge in large language models: comparative evaluation using Japanese National Medical Examination
Abstract Background The accuracy of the latest reasoning-enhanced large language models on national medical licensing examinations remains unknown, which is crucial for determining how close they are to serving as effective knowledge sources for medical education. This study aimed to evaluate the…
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Artikel Jurnal
Factors associated with clinical coders’ intention to use the international classification of diseases 11th revision (ICD-11): a cross-sectional study in Iran
Abstract Background Given the pivotal role of clinical coders’ intention to use (ITU) in the adoption of the International Classification of Diseases, 11th Revision (ICD-11), this study aimed to examine their ITU concerning ICD-11 in teaching hospitals in Iran. Methods A descriptive and…
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Artikel Jurnal
Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction
Purpose Background Sepsis is a leading cause of mortality in intensive care units (ICUs), and its rapid progression poses significant challenges for early detection. Traditional scoring systems, such as SOFA and APACHE II, provide clinical benchmarks but often fail to capture subtle early signs of…
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Artikel Jurnal
Benchmark evaluation of deepseek AI models in antibacterial clinical decision-making for infectious diseases
Abstract Background Antimicrobial resistance (AMR) poses a global threat to public health, though AI models have shown transformative potential in combating AMR. China’s DeepSeek, a novel open-source, low-cost, and locally deployable AI model, is increasingly integrated into clinical workflows for…
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Artikel Jurnal
Sharing EHR data of patients with rare diseases for research - The role of quality assessments in a national federated research data infrastructure
Abstract Background To obtain sufficient data for clinical research on Rare Diseases (RDs), the project “Collaboration on Rare Diseases” (CORD-MI) has connected 24 University Medical Centers (UMCs) in Germany. As part of the national Medical Informatics Initiative (MII), which aims at making the…
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Artikel Jurnal
Comparison of human–AI agreement in ASA scoring by gender and duration of clinical experience: a real-world study
Abstract Background Accurate preoperative risk identification is critical for patient safety and postoperative outcomes. Anaesthesiologists make decisions on the basis of ASA classification and additional parameters. Artificial intelligence (AI)-based decision support may offer more objective…
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Artikel Jurnal
Evaluating the external validity of an artificial intelligence-based mobile support app for caregiving relatives by an online expert survey
Abstract Background Mobile Care Backup is a support app for family caregivers that provides textual information on topics personalized to their specific care situation. Personalization is performed by an artificial intelligence-based expert system. Here, we present the evaluation of the expert…
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Artikel Jurnal
Interpretable machine learning based decision-making system for lung adenocarcinoma metastasis: a population-based study with exploration of multi-classification models
Abstract Background Accurate metastasis prediction in lung adenocarcinoma is essential for effective treatment plans and improved prognosis. Current methods face challenges in accuracy and clinical application. Methods We developed a binary classification model (XGBoost) and a multiclassification…
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Artikel Jurnal
Predicting the risk of activities of daily living dysfunction in middle-aged and older adults with comorbid hypertension and diabetes: a national population-based survey analysis
Abstract Objective To develop and validate an interpretable model for predicting activities of daily living (ADL) dysfunction in middle-aged and older adults with comorbid hypertension and diabetes. Methods This is a cross-sectional study. Data were derived from wave 4 of the China Health and…
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Artikel Jurnal
Classification of pulmonary diseases using machine learning and deep learning models on GLI-2012 standardized spirometry features
Abstract Background Artificial intelligence applications play an increasingly important role in medical diagnosis processes, nowadays. Spirometry, which is widely used among lung function tests, is an important method in the early diagnosis of pulmonary diseases and monitoring. In this study, the…
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Artikel Jurnal
Evaluation of large language models in nutrition risk screening: a comparative analysis across 8 LLMs based on real-world EHR datasets
Abstract Background Nutrition risk screening (NRS) is a critical step in the early identification of malnutrition among hospitalized patients. Traditional methods, which rely on manual assessments using tools such as Nutrition Risk Screening 2002 (NRS-2002) based on electronic health records…
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Artikel Jurnal
Development and validation of random-forest based federated ensemble learning algorithms for delirium prediction using electronic medical records from eleven hospitals in Austria: a retrospective study
Abstract Background Machine learning models have shown great potential in preventive medicine but require large datasets, which is a challenge due to strict privacy regulations in the healthcare sector. Federated learning is an approach that enables collaboration between institutions while…
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