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Applied Medical Informatics
ISSN 2067-7855 · Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca · 643 artikel di indeks
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System Architecture and Prospective Evaluation Protocol of AskDoc: A Secure Multimodal mHealth Triage Assistant — Pilot Safety and Feasibility Study
Aim: This study is explicitly designed as a feasibility and safety pilot, not a powered efficacy trial. Its dual purpose is: (i) to establish whether an AI-augmented, rule-constrained triage strategy can reduce clinically unsafe under-triage compared with a rule-based baseline while maintaining…
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Assessing Patient Safety Culture: Validation of the Veterans Health Administration Patient Safety Culture Survey among Romanian Nurses
Patient safety culture is essential for improving healthcare quality and reducing medical errors. In Romania, systematic assessment of this culture remains limited, particularly among nurses, who play a key role in preventing incidents. This study aimed to culturally adapt and psychometrically…
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Enhancing the Accuracy of Large Language Models in Medical Coding through Retrieval-Based Approaches
Purpose: Medical coding, essential for healthcare administration and research, requires significant expertise and resources. While Large Language Models (LLMs) showed promise in automating this task, recent studies highlighted their limitations, with even advanced models like GPT-4 achieving only…
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A Simple Interactive Prototype for Monitoring Chemotherapy-Induced Peripheral Neuropathy with CTCAE Grading and Supportive Natural Compound Suggestions
Chemotherapy-induced peripheral neuropathy (CIPN) represents a frequent dose-limiting toxicity in oncology patients treated with neurotoxic agents such as taxanes and platinum compounds. Natural compounds with neuroprotective properties demonstrate supportive potential, particularly in preclinical…
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Performance Comparison of a Hybrid Convolutional Neural Network-Long Short-Term Memory and CNN Model for Malaria Diagnosis using Microscopic Blood Smears
Aim: This study evaluates and compares the performance of a lightweight Convolutional Neural Network (CNN) and a hybrid CNN–Long Short-Term Memory (CNN–LSTM) model for classifying malaria-infected and uninfected microscopic blood smear images, with emphasis on performance–efficiency trade-offs…
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Editorial
The present supplement of Applied Medical Informatics is dedicated to the 2nd edition of the jRoMedINF Conference 2026, a hybrid conference hosted by the “Carol Davila” University of Medicine and Pharmacy of Bucharest, Romania, and centered on the theme Medical Data Lifecycle: From Collection to…
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Automating Adverse Event Extraction from EMA Summary of Product Characteristics
Systematically extracting adverse event (AE) data from Summary of Product Characteristics (SmPC) documents is often necessary in exploring drug safety with computational means (e.g. for building QSAR models). The European Medicines Agency (EMA) makes available the SmPCs in a pdf format, which…
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Optimizing the ECG Data Lifecycle for High-Fidelity Clinical Decision Support
The integrity of the medical data lifecycle is fundamentally contingent upon the signal fidelity established during the initial acquisition phase. In wearable cardiology, the transition from controlled clinical environments to ambulatory monitoring introduces significant signal degradation…
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The Opinion of the General Romanian Population on Introducing an AI-powered Medical Data Collector
Introduction: Due to the continuous development of AI, many fields have sought to use AI-powered tools or applications to reduce time and costs and increase efficiency. This study raises the scenario of introducing an AI-powered data collector in medical triage areas and how the general Romanian…
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AMI: A Chatbot for Emotional Support in Romanian
Background: Assistance for mental health-related issues remains largely inaccessible for many individuals. Factors such as economic stability, place of residence and even societal expectations play a crucial role in whether a person seeks or receives professional help. According to the "Don't let…
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Classification of Sleep Stages from EEG Data
Background: Manual sleep staging from electroencephalography (EEG) is time-consuming and affected by inter-scorer variability. Automated sleep stage classification can support clinical workflows if it generalizes across subjects, remains computationally feasible, and provides reliable probability…
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Kardio Diagnostix Implementation of a Continuous Medical Data Lifecycle
Kardio Diagnostix was founded with the goal of reducing the burden on health systems while increasing the quality of care. We have developed a a novel deep learning architecture integrating a physics-informed Harmonic-Percussive-Residual (HPR) signal decomposition with a stacked Long Short-Term…
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Automated Detection of Coronary Artery Disease from Multiparametric Cardiac Magnetic Resonance Imaging Using Deep Learning
Coronary artery disease (CAD) remains the most prevalent cardiovascular disorder and a leading cause of global morbidity and mortality. It is primarily driven by atherosclerotic plaque accumulation within the coronary arteries, resulting in impaired myocardial perfusion and potentially leading to…
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Physics-Informed Neural Networks for Robust Multiple Sclerosis Lesion Detection: Integrating Bloch-Based Pre-Training
Background: While the diagnosis of Multiple Sclerosis (MS) requires a combination of clinical history, physical examinations, and spinal tap results, Magnetic Resonance Imaging (MRI) plays a central and indispensable role in this assessment. Conventional MRI (cMRI) provides qualitative intensities…
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A Frequency-Based Electroencephalography Approach to Real-Time Attention Detection
The aim of this study was to develop and validate a frequency-based algorithm for estimating attention from electroencephalography (EEG) signals. Attention is a complex multi-dimensional cognitive process that is critical in task performance and learning. There are many approaches out there that…
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Automated Breast Lesion Segmentation in Ultrasound Images Using Attention U-Net Architectures
Background: In Romania, breast cancer remains the leading type of cancer and third cause of cancer-related deaths among women, according to the National Institute of Public Health (NIPH 2024). Consequently, breast cancer continues to be a major challenge in the Romanian healthcare system, making…
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Real-Time Sleep Stage Monitoring Using Neural Networks For Optimal Wake-Up Timing
Background: Human sleep architecture is cyclic, oscillating between Non-Rapid Eye Movement (NREM) stages and Rapid Eye Movement (REM). NREM sleep progresses from light sleep (Stages N1 and N2) to deep, slow-wave sleep (Stage N3). Awakening during deep N3 or intense REM sleep often results in sleep…
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Artificial Intelligence for Polysomnographic Analysis: Clinical Validation, Technical Architecture and Real-World Implementation of Somnolyzer
Health data form the foundation of modern medicine, yet their clinical value is determined by how effectively they are managed throughout the entire data lifecycle. From the collection of biomedical and physiological data to their transformation into actionable clinical insights, each stage…
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Biomarker-Led Predictive Modelling and Risk Classification in Personalized Medicine
Background: Identifying predictive and prognostic factors is becoming pivotal in medical research, especially as scientific discoveries have led to an in-depth understanding of diseases and genetics, resulting in personalized therapy. As we gain a better understanding of aspects involved in disease…
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Traceable and Reproducible 3D Magnetic Field Mapping for Data-Driven Magnetic Drug Targeting
Background: Magnetic Drug Targeting (MDT) aims to localise drugs while minimising systemic toxicity in vascular disease, localised tumours, and site-specific inflammatory conditions. MDT safety and efficacy depend on accurate three-dimensional magnetic-field distributions and gradients governing…