bluuebunny/crossref_metadata_2025 huggingface dataset:
Unveiling the Potential of ChatGPT and YOLOv7 for Evaluating Children’s Emotions Using Their Artistic Expressions
Authors: Shah Uzair , Khan Sulaiman , Alzubaidi Mahmood , Agus Marco , Househ Mowafa
Published: August 2024

Recent advancements in large language models (LLMs) have sparked considerable interest in their potential applications across various healthcare domains. One promising prospect is leveraging these generative models to accurately predict children’s emotions by combining computer vision and natural language processing techniques. However, understanding children’s emotional states based on their artistic expressions is equally crucial. To address this challenge, this paper presents a pipelined architecture comprising YOLOv7 and the powerful GPT-3.5 Turbo language model, where YOLOv7 is employed for object detection using art therapy imaging annotations, while GPT-3.5 interprets the sketches. After rigorously evaluating the proposed framework through a series of comprehensive experiments, we observed that our model achieved high confidence scores for both object detection and emotion interpretation. The robust performance of the proposed framework not only aids in explaining children’s art but also provides valuable insights for parents and therapists. This capability enables them to better understand children’s emotional states based on their artistic expressions, ultimately facilitating improved support and care.
Performance Evaluation of Commonly Used Portable Hemoglobin Sensors in Comparison to Clinical Test Results
Authors: Islam Rafiqul , To Saori , Izukura Rieko , Sato Yoko , Nishikitani Mariko , Kikuchi Kimiyo , Yokota Fumihiko , Ikeda Subaru , Jahan Nusrat , Ahmed Ashir , Miyazaki Masashi , Nakashima Naoki
Published: August 2024

Telehealth systems in underserved countries leverage various low-cost portable medical sensors to transmit patients’ vital information to remote doctors, facilitating timely diagnoses and interventions. However, the potential risks associated with inaccurate data pose considerable threats to the health of individuals. This study focuses on identifying high-quality portable hemoglobin sensors, employing the Japanese clinical pathology laboratory as a gold standard. Out of the eight sensors evaluated in this study, four were found to be highly erroneous.
Revolutionizing Physical Activity: How Private Sector Physicians Harness Mobile Apps for Enhanced Health Outcomes
Authors: Berrami Hind , Boubekri Zineb , Mouzoune Fatima Zahra , Niyonsaba Adelin , Bouriss Fatima Ezzahra , Jallal Manar , Serhier Zineb , Bennani Othmani Mohammed
Published: August 2024

Introduction: The vital importance of engaging in physical activity for the maintenance and improvement of health is undeniable. The advent of digital technology has significantly enhanced the encouragement of physical activity (PA) through numerous interventions. Our study’s goal is to examine the usage and prescription practices of mobile applications and fitness trackers by doctors operating in the private sector. Methods: cross-sectional study, which targeted doctors in the private sector, whether general practitioners or specialists was carried out in January and February 2024, A paper questionnaire was distributed to the doctors’ offices. Results: the mean age was 44.3±11.7 years, 77% of doctors are physically active, the prevalence of use of mobile applications and fitness trackers among private sector physicians for improving and monitoring their physical activity was 32% with a CI ranging from [26-40] % and only 15% recommend mobile apps to patients. This reluctance was attributed to the applications’ lack of customization and the challenges patients faced in using them. Conclusion: In our research, we found that doctors rarely utilized mobile applications to enhance their practice of physical activity and were hesitant to recommend them to their patients.
Implementation and Scale-up of Telemonitoring in the Netherlands
Authors: Gijsbers Harm , Dusseljee-Peute Linda , van de Belt Tom , Schijven Marlies
Published: August 2024

The healthcare system requires reforms for equitable and sustainable care. Telemonitoring offers a solution, yet its implementation lacks research. In the Netherlands, University Medical Centers (UMCs) lead healthcare transformation through the Citrien-2 network, scaling up three telemonitoring programs from 2020 to 2022. An uncontrolled before-after study design was used to evaluate upscaling. Primary outcomes focused on telemonitoring normalization using the Normalisation Process Theory (NoMAD). Secondary outcomes included patient uptake and qualitative analyses. Results show implementation across projects and UMCs, with overall increased patient uptake. Healthcare professionals perceive telemonitoring positively, but challenges persist in integrating it into workflows. Continuous assessment is crucial for addressing implementation complexities and maximizing telemonitoring’s impact on healthcare delivery.
A Pipeline for Real-Time Synchronization of Relevant Data in Pediatric Emergencies
Authors: Schmucker Michael , Reiswich Andreas , Haag Martin
Published: August 2024

Pediatric emergencies are rare events that require precise medical intervention. The dosing of medication is particularly tricky. The challenges require technical aids for automatic dosage calculation without restricting the emergency physicians’ ability to act. This short paper describes the development process of an automatic data processing pipeline that synchronizes multiple sensor data to perform the necessary calculations. In a final evaluation process, two transmission protocols (HTTP and MQTT)), are compared to determine which is more suitable for this application. The results of the evaluation clearly showed that MQTT is the preferred choice for the data processing pipeline due to its significantly lower latency.
An eHealth Coaching Solution to Improve Transitional Care of Seniors with Heart Failure: Long-Field Trial
Authors: Gosetto Laëtitia , Guebey Julie , Lovis Christian , Anghel Ionut , Cioara Tudor
Published: August 2024

Heart failure is the leading reason for seniors being admitted to hospitals. Over half of the elderly individuals diagnosed with heart failure find themselves readmitted to hospitals within a span of six months. This recurrence is associated with inadequate adherence to medical treatment and recommendations, underscoring the necessity for support systems that aid seniors in better adhering to post-hospitalization instructions. The objective of this study is to evaluate the usability, usefulness and added value of the core functionalities within the H2HCare Ambient Assisted Living developed system that was evaluated with 11 participants over a long field trial of three months. Our assessment encompassed the examination of their Quality of Life as well as the usability and efficacy of the system. Overall, participants reported finding the system user-friendly, beneficial, and conducive to enhanced disease management. Improvements include tailoring the alarm system to patient standards and using a questionnaire to assess situation urgency.
Enhancing Medical Device Management with IoT Powerstrip
Authors: Oyama Shintaro , Otsuka Takanobu , Ideno Yoshinori , Shima Kosuke , Miyagi Hideki
Published: August 2024

Managing medical devices efficiently was a challenge, especially with manual methods proving error-prone amid staff shortages. To overcome these issues, we developed a novel IoT powerstrip device for tracking usage and location. This helps monitor devices in real-time, empowering more efficient management and utilization. We introduced it in four hospitals, connecting 192 medical devices to the strips and quantitatively confirmed its effectiveness.
DR.BEAT: Rule-Based Algorithm for SCG Analysis Without ECG Reference
Authors: Pickert Marie Cathrine , Tharra Tabea , Kulau Ulf , Richter Christoph , Zettner Philip , Marschollek Michael , Wolf Klaus-Hendrik
Published: August 2024

The DR.BEAT project aims to develop an accelerometer-based, wearable sensor system for measuring ballistocardiographic (BCG) signals, coupled with signal processing and visualization, to support cardiac health monitoring. A rule-based heartbeat detection was developed to enable the derivation of health parameters independent of an existing reference. This paper outlines the algorithm’s methodology and provides an initial evaluation of its performance based on seismocardiographic (SCG) measurements obtained from an initial study involving twelve heart-healthy adults. On average, 87.6% of the heartbeats over all measurements, 97.6% of the heartbeats at rest and 71.9% of the heartbeats during physical stress could be detected.
User Engagement with the User Manuals of Smart Medical Wearable Technologies and Relevant Questions of User Behavior
Authors: Simon Aniko , Xinyu Qiu , Maksimovic Ana , Talovics Melinda , Baranyai-Santa Eszter , Berze David , Odor Viktoria , Kara Peter A.
Published: August 2024

In this paper, engagement with smart medical wearables and with their user manuals, as well as related user behavior are studied. A research questionnaire containing 15 single-choice questions was completed by 1381 test participants to address relevant topics of the investigated area, including trust in measured medical data, device calibration, technical terminologies and function discovery in the documentation, information sources beyond the documentation, and wearing such devices to bed. The questionnaire particularly focused on device functionalities and characteristics that initially led to the purchase of the smart medical wearable.
Empowering Ageing with Ingenuity: A Scoping Review on the Methods and Technologies Used into Elderly Health Monitoring
Authors: Moraitopoulos Alexandros , Billis Antonis , Bamidis Panagiotis
Published: August 2024

With the rise in global life expectancy, ensuring healthier aging experiences for the older population becomes paramount. This scoping review delves into the technologies employed in the remote health monitoring of the elderly over the past 15 years. Exploring the concept of “Healthy Ageing” as proposed by the World Health Organization, this paper attempts to highlight the techniques and technologies used in health monitoring of the elderly population. The integration of wearable sensors in health monitoring presents promising avenues for enhancing healthcare delivery to older adults. However, challenges such as limited digital literacy and privacy concerns persist, necessitating innovative solutions for unobtrusive monitoring. This paper discusses the potential of passive and ambient sensors to address these challenges, offering insights into enhancing the well-being of the older population while preserving their independence and privacy.
Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies
Authors: Theodoropoulos Christos , Mulligan Natasha , Bettencourt-Silva Joao
Published: August 2024

Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standardization or sparseness of the data. We previously introduced a person-centric ontology to organize information about individual patients, and a representation learning framework to extract person-centric knowledge graphs (PKGs) and to train Graph Neural Networks (GNNs). In this paper, we propose a systematic approach to examine the results of GNN models trained with both structured and unstructured information from the MIMIC-III dataset. Through ablation studies on different clinical, demographic, and social data, we show the robustness of this approach in identifying predictive features in PKGs for the task of readmission prediction.
Enabling AI in Radiology: Evaluation of an AI Deployment Process
Authors: Silsand Line , Kannelønning Mari , Severinsen Gro-Hilde , Ellingsen Gunnar
Published: August 2024

Artificial intelligence (AI) is expected to transform healthcare systems and make them more sustainable. Despite the increased availability of AI tools for disease detection, evidence of their impact on healthcare organisations and patient care remains limited. Drawing on previous research underscoring the need for comprehensive evaluations of real-world AI deployments, this paper explores the challenges and opportunities encountered while procuring and implementing AI solutions for radiology. The paper aims to contribute to a better understanding of the complexities surrounding AI deployments in real-world clinical settings through a process evaluation study.
Multimodal Model for Predicting Fetal Acidosis in Delivery Room
Authors: Choi Byungjin , Park Chang Eun , Park Jong Chan , Chang Hye Jin , Hwang Kyung Joo , Yum Sun Hyung , Jo Eunae , Kim Miran
Published: August 2024

In the delivery room, fetal well-being is evaluated through laboratory tests, biosignals like cardiotocography, and imaging techniques such as fetal echocardiography. We have developed a multimodal machine learning model that integrates medical records, biosignals, and imaging data to predict fetal acidosis, using a dataset from a tertiary hospital’s delivery room (n=2,266). To achieve this, features were extracted from unstructured data sources, including biosignals and imaging, and then merged with structured data from medical records. The concatenated vectors formed the basis for training a classifier to predict post-delivery fetal acidosis. Our model achieved an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.752 on the test dataset, demonstrating the potential of multimodal models in predicting various fetal outcomes.
Secure Extraction of Personal Information from EHR by Federated Machine Learning
Authors: El Azzouzi Mohamed , Bellafqira Reda , Coatrieux Gouenou , Cuggia Marc , Bouzille Guillaume
Published: August 2024

Secure extraction of Personally Identifiable Information (PII) from Electronic Health Records (EHRs) presents significant privacy and security challenges. This study explores the application of Federated Learning (FL) to overcome these challenges within the context of French EHRs. By utilizing a multilingual BERT model in an FL simulation involving 20 hospitals, each represented by a unique medical department or pole, we compared the performance of two setups: individual models, where each hospital uses only its own training and validation data without engaging in the FL process, and federated models, where multiple hospitals collaborate to train a global FL model. Our findings demonstrate that FL models not only preserve data confidentiality but also outperform the individual models. In fact, the Global FL model achieved an F1 score of 75,7%, slightly comparable to that of the Centralized approach at 78,5%. This research underscores the potential of FL in extracting PIIs from EHRs, encouraging its broader adoption in health data analysis.
In Silico Screening of Phytocompounds from West African Traditional Medicine and Molecular Docking Targeting Dengue Virus Protein NS2B/NS3
Authors: Kantagba Yves M.K. , Barro Seydou Golo , Nikiema Serge L.W. , Staccini Pascal
Published: August 2024

Traditional medicine offers a wide range of application for in silico study techniques. This drug research and development strategy is embryonic in the West African context, particularly in Burkina Faso, which is increasingly faced with emerging diseases such as dengue fever. Circulation of the 4 serotypes of this virus has been documented in the country. This study aims to evaluate the therapeutic potential of phytocompounds contained in the West African pharmacopoeia against dengue virus NS2B/NS3 protein, using computational methods integrating several software packages and databases. Based on a literature review, we identified 191 molecules from 30 plants known for their antiviral effects. Five met the inclusion criteria for molecular docking: patulin from calotropis procera, resiniferonol from Euphorbia poissonii, Securinol A from Flueggea virosa, Shikimic acid and Methyl gallate from Terminalia macroptera. The best binding scores were observed between resiniferonol and the serotypes 1, 2 and 4 NS2B/NS3 protease, with binding energies of -7.4 Kcal/mol, -6.8 Kcal/mol and -7.3 Kcal/mol respectively; while the NS2B/NS3 protease of serotype 3 had the best affinity for securinol A (-7 Kcal/mol). This study points the way to further research in computer aided drug design field and calls for multidisciplinary collaboration to promote West African medicinal plants against health challenges.
Continuous, Learned Imputation of Missing Values in Parkinson’s Disease
Authors: Gundler Christopher , Pötter-Nerger Monika
Published: August 2024

Parkinson’s disease management requires accurate clinical scores but suffers from missing data. Leveraging self-supervised learning, we demonstrate superior generalization capabilities across populations compared to other well-established imputation techniques (MIWAE, MissForest, MICE). With the ability to employ the method already during the data collection and not afterward, the technology allows more robust data collection in clinical reality.
Comparing Patient Perception and Physician’s Records: Generative AI Performance Evaluation
Authors: Thawinwisan Nattawipa , Liu Chang , Kishimoto Kazumasa , Yamamoto Goshiro , Mori Yukiko , Kuroda Tomohiro
Published: August 2024

Patient perception involves a patient’s thoughts and beliefs regarding their health status. It is also associated with medical compliance and outcomes. However, discrepancies often arise between patient perception and physicians’ documentation within the medical records, resulting in misunderstanding and suboptimal doctor-patient communication. In this study, we assessed the efficacy of generative artificial intelligence (AI) in comparing the content of patient perception as recorded in patient questionnaires and physicians’ records of the Department of Breast Surgery. We evaluated the precision and recall of the generative AI by comparison with human-created ground truth. Our results demonstrated the high performance of the generative AI in comprehending and contrasting symptoms and the entire content recorded differently by patients and physicians, with F1 scores ranging from 0.77 to 0.97. These results highlight the potential contribution of a generative AI to deeper mutual comprehension in healthcare scenarios.
Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach
Authors: Haraldsson Tobias , Marzano Luca , Krishna Harsha , Lethval Sven , Falk Nina , Bodeby Patrik , Raghothama Jayanth , Meijer Sebastiaan , Darwich Adam S.
Published: August 2024

Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging ED real-world production data provides a unique opportunity to comprehend this multifaceted problem holistically. This paper introduces a novel approach to analyse healthcare production data, treating the length of stay of patients, and the follow up decision regarding discharge or admission to the hospital as a time-to-event analysis problem. Our methodology employs traditional survival estimators and machine learning models, and Shapley additive explanations values to interpret the model outcomes. The most relevant features influencing length of stay were whether the patient received a scan at the ED, emergency room urgent visit, age, triage level, and the medical alarm unit category. The clinical insights derived from the explanation of the models holds promise for increase understanding of the overcrowding from the data. Our work demonstrates that a time-to-event approach to the over- crowding serves as a valuable initial to uncover crucial insights for further investigation and policy design.
Improving the Quality of Unstructured Cancer Data Using Large Language Models: A German Oncological Case Study
Authors: Mou Yongli , Lehmkuhl Jonathan , Sauerbrunn Nicolas , Köchel Anja , Panse Jens , Truh Daniel , Sowe Sulayman , Brümmendorf Tim , Decker Stefan
Published: August 2024

With cancer being a leading cause of death globally, epidemiological and clinical cancer registration is paramount for enhancing oncological care and facilitating scientific research. However, the heterogeneous landscape of medical data presents significant challenges to the current manual process of tumor documentation. This paper explores the potential of Large Language Models (LLMs) for transforming unstructured medical reports into the structured format mandated by the German Basic Oncology Dataset. Our findings indicate that integrating LLMs into existing hospital data management systems or cancer registries can significantly enhance the quality and completeness of cancer data collection - a vital component for diagnosing and treating cancer and improving the effectiveness and benefits of therapies. This work contributes to the broader discussion on the potential of artificial intelligence or LLMs to revolutionize medical data processing and reporting in general and cancer care in particular.
A Vision on User-Centered Implementation and Evaluation of Explainable AI for Predicting Hospital-Onset Bacteremia
Authors: Hoogestraat Anna Thalea , Wulff Antje
Published: August 2024

In recent years, artificial intelligence (AI) has gained momentum in many fields of daily live. In healthcare, AI can be used for diagnosing or predicting illnesses. However, explainable AI (XAI) is needed to ensure that users understand how the algorithm arrives at a decision. In our research project, machine learning methods are used for individual risk prediction of hospital-onset bacteremia (HOB). This paper presents a vision on a step-wise process for implementation and evaluation of user-centered XAI for risk prediction of HOB. An initial requirement analysis revealed first insights on the users’ needs of explainability to use and trust such risk prediction applications. The findings were then used to propose step-wise process towards a user-centered evaluation.
Merging Biomedical Ontologies with BioSTransformers
Authors: Menad Safaa , Abdeddaïm Saïd , Soualmia Lina F.
Published: August 2024

Ontologies play a key role in representing and structuring domain knowledge. In the biomedical domain, the need for this type of representation is crucial for structuring, coding, and retrieving data. However, available ontologies do not encompass all the relevant concepts and relationships. In this paper, we propose the framework SiMHOMer (Siamese Models for Health Ontologies Merging) to semantically merge and integrate the most relevant ontologies in the healthcare domain, with a first focus on diseases, symptoms, drugs, and adverse events. We propose to rely on the siamese neural models we developed and trained on biomedical data, BioSTransformers, to identify new relevant relations between concepts and to create new semantic relations, the objective being to build a new merging ontology that could be used in applications. To validate the proposed approach and the new relations, we relied on the UMLS Metathesaurus and the Semantic Network. Our first results show promising improvements for future research.
A Comparative Analysis of Federated and Centralized Learning for SpO2 Prediction in Five Critical Care Databases
Authors: Schwinn Johanna , Sheikhalishahi Seyedmostafa , Morhart Matthaeus , Kaspar Mathias , Hinske Ludwig Christian
Published: August 2024

This study explores the potential of federated learning (FL) to develop a predictive model of hypoxemia in intensive care unit (ICU) patients. Centralized learning (CL) and local learning (LL) approaches have been limited by the localized nature of data, which restricts CL approaches to the available data due to data privacy regulations. A CL approach that combines data from different institutions, could offer superior performance compared to a single-institution approach. However, the use of this method raises ethical and regulatory concerns. In this context, FL presents a promising middle ground, enabling collaborative model training on geographically dispersed ICU data without compromising patient confidentiality. This study is the first to use all five public ICU databases combined. The findings demonstrate that FL achieved comparable or even slightly improved performance compared to local or centralized learning approaches.
Comparison of Regression Methods to Predict the First Spike Latency in Response to an External Stimulus in Intracellular Recordings for Cerebellar Cells
Authors: Sharif Samane , Ghouchan Nezhad Noor Nia Raheleh , Abbassian Hassan , Eslami Saeid
Published: August 2024

The significance of intracellular recording in neurophysiology is emphasized in this article, with considering the functions of neurons, particularly the role of first spike latency in response to external stimuli. The study employs advanced machine learning techniques to predict first spike latency from whole cell patch recording data. Experiments were conducted on Control (Salin) and Experiment (Harmaline) groups, generating a dataset for developing predictive models. Because the dataset has a limited number of samples, we utilized models that are effective with small datasets. Among different groups of regression models (linear, ensemble, and tree models), the ensemble models, specifically the LGB method, can achieve better performance. The results demonstrate accurate prediction of first spike latency, with an average mean squared error of 0.0002 and mean absolute error of 0.01 in 10-fold cross-validation. The research suggests the potential of machine learning in forecasting the first spike latency, allowing reliable estimation without the need for extensive animal testing. This intelligent predictive system facilitates efficient analysis of first spike latency changes in both healthy and unhealthy brain cells, streamlining experimentation and providing more detailed insights into the captured signals.
A Road Map of Prompt Engineering for ChatGPT in Healthcare: A Perspective Study
Authors: Abhari Shahabeddin , Fatahi Somayeh , Saragadam Ashish , Chumachenko Dmytro , Pelegrini Morita Plinio
Published: August 2024

Generative AI models, such as ChatGPT, have significantly impacted healthcare through the strategic use of prompts to enhance precision, relevance, and ethical standards. This perspective explores the application of prompt engineering to tailor outputs specifically for healthcare stakeholders: patients, providers, policymakers, and researchers. A nine-stage process for prompt engineering in healthcare is proposed, encompassing identifying applications, understanding stakeholder needs, designing tailored prompts, iterative testing and refinement, ethical considerations, collaborative feedback, documentation, training, and continuous updates. A literature review focused on “Generative AI” or “ChatGPT,” prompts, and healthcare informed this study, identifying key prompts through qualitative analysis and expert input. This systematic approach ensures that AI-generated prompts align with stakeholder requirements, offering valuable insights into symptoms, treatments, and prevention, thereby supporting informed decision-making among patients.
Using Formal Knowledge to Support Episodic Evidence-Based Nursing Care
Authors: Jaffe Shai , Ben Shahar Bruria , Shahar Yuval , Goldstein Ayelet , Shalom Erez , Selivanova Maya , Rimon Ephraim , Cohen Odeya
Published: August 2024

Applying evidence-based medicine prevents medical errors highlighting the need for applying Clinical Guidelines (CGs) to improve patient care by nurses. However, nurses often face challenges in utilizing CGs due to patient-specific needs. Developing a Clinical Decision Support System (CDSS) can provide real-time context-sensitive CG-based recommendations. Therefore, there is a need to acquire and represent CGs in a machine-applicable manner. Also, there is a need to be able to provide recommendations episodically, only when requested, and not continuously, and to assess previous partial performance of evidence-based actions on a continuous scale. This study evaluated the feasibility of acquiring and representing major nursing CGs, in a machine-applicable manner for episodic use. Using data from an Israeli geriatric center, the results suggest that an episodic CDSS effectively supports the application of formalized nursing knowledge.
Improving Interpretability of Leucocyte Classification with Multimodal Network
Authors: Chossegros Manon , Tannier Xavier , Stockholm Daniel
Published: August 2024

White blood cell classification plays a key role in the diagnosis of hematologic diseases. Models can perform classification either from images or based on morphological features. Image-based classification generally yields higher performance, but feature-based classification is more interpretable for clinicians. In this study, we employed a Multimodal neural network to classify white blood cells, utilizing a combination of images and morphological features. We compared this approach with image-only and feature-only training. While the highest performance was achieved with image-only training, the Multimodal model provided enhanced interpretability by the computation of SHAP values, and revealed crucial morphological features for biological characterization of the cells.
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
Authors: Müller Dominik , Meyer Philip , Rentschler Lukas , Manz Robin , Hieber Daniel , Bäcker Jonas , Cramer Samantha , Wengenmayr Christoph , Märkl Bruno , Huss Ralf , Kramer Frank , Soto-Rey Iñaki , Raffler Johannes
Published: August 2024

Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in prostate carcinoma focusing on comparing traditional and recent architectures. A standardized image classification pipeline, based on the AUCMEDI framework, facilitated robust evaluation using an in-house dataset consisting of 34,264 annotated tissue tiles. The results indicated varying sensitivity across architectures, with ConvNeXt demonstrating the strongest performance. Notably, newer architectures achieved superior performance, even though with challenges in differentiating closely related Gleason grades. The ConvNeXt model was capable of learning a balance between complexity and generalizability. Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer.
Quality Assessment for Secondary Use of Imaging Trials
Authors: Seyderhelm Friederike , Balzer Felix , Bejaoui Alaa , Bosserdt Maria , Bowden James , Dewey Marc , Föllmer Bernhard , Tzschätzsch Heiko , Zerbe Norman , Krefting Dagmar
Published: August 2024

Secondary use of health data has become an emerging topic in medical informatics. Many initiatives focus on clinical routine data, but clinical trial data has complementary strengths regarding highly structured documentation and mandatory data quality (DQ) reviews during the implementation. Clinical imaging trials investigate new imaging methods and procedures. Recently, DQ frameworks for structured data were proposed for harmonized quality assessments (QA). In this article, we investigate the application of these concepts to imaging trials and how a DQ framework could be defined for secondary use scenarios. We conclude that image quality can be assessed through both pixel data and metadata, and the latter can mostly be handled like structured study documentation in QA. For pixel data, typical quality indicators can be mapped to existing frameworks, but require additional image processing. Specific attention needs to be drawn to complete de-identification of imaging data, both on pixel data and metadata level.
Deep Learning-Based Synthetic Skin Lesion Image Classification
Authors: Abbasi Saadullah Farooq , Bilal Muhammad , Mukherjee Teesta , Churm James , Pournik Omid , Epiphaniou Gregory , Arvanitis Theodoros N.
Published: August 2024

Advances in general-purpose computers have enabled the generation of high-quality synthetic medical images that human eyes cannot differ between real and AI-generated images. To analyse the efficacy of the generated medical images, this study proposed a modified VGG16-based algorithm to recognise AI-generated medical images. Initially, 10,000 synthetic medical skin lesion images were generated using a Generative Adversarial Network (GAN), providing a set of images for comparison to real images. Then, an enhanced VGG16-based algorithm has been developed to classify real images vs AI-generated images. Following hyperparameters tuning and training, the optimal approach can classify the images with 99.82% accuracy. Multiple other evaluations have been used to evaluate the efficacy of the proposed network. The complete dataset used in this study is available online to the research community for future research.
Serious Games in Orofacial Myofunctional Disorder Therapy for Children: An Expert Survey
Authors: Hieber Daniel , Heindl Anna , Karthan Maximilian , Holl Felix , Krüger Tobias , Pryss Rüdiger , Schobel Johannes
Published: August 2024

Orofacial Myofunctional Disorder (OMD) is believed to affect approximately 30-50% of all children. The various causes of OMD often revolve around an incorrect resting position of the tongue and cause symptoms such as difficulty in speech and swallowing. While these symptoms can persist and lead to jaw deformities, such as overjet and open bite, manual therapy has been shown to be effective, especially in children. However, much of the therapy must be done as home exercises by children without the supervision of a therapist. Since these exercises are often not perceived as exciting by the children, half-hearted performance or complete omission of the exercises is common, rendering the therapy less effective or completely useless. To overcome this limitation, we implemented the LudusMyo platform, a serious game platform for OMD therapy. While children are the main target group, the acceptance (and usability) assessment by experts is the first milestone for the successful implementation of an mHealth application for therapy. For this reason, we conducted an expert survey among OMD therapists to gather their input on the LudusMyo prototype. The results of this expert survey are reported in this manuscript.
Contribution of a Tablet-Based Tests to the Diagnosis of Neurocognitive Disorders in Older Adults: A Feasibility Study
Authors: Um Din Nathavy , Maronnat Florian , Le Boterff Quentin , Otmane Samir , Badra Fadi , Lindberg Påvel G. , Belmin Joël
Published: August 2024

Neurocognitive diseases are diagnosed in specialized centers such as memory clinics, where the waiting time can be long. The reference assessment involves a battery of tests carried out by a specialized team. Facilitating screening in primary care using new technologies could make it possible to appropriately direct care pathways towards specialist care. This work aimed to set up a battery of questionnaires, cognitive and manual dexterity tests on a digital tablet to screen people with cognitive impairment. Three groups of people are recruited from a memory consultation: people with major neurocognitive disorders, people with mild neurocognitive disorders and people with no cognitive impairment. Initial results in geriatric settings show that the digital tablet assessment test is feasible and well accepted, but that manual dexterity assessment needs to be adapted to the bodily particularities of the very old.
Reproducibility of Radiomic Features in Glial Brain Tumors
Authors: Danilov Gleb , Shevchenko Alexander , Afandiev Ramin , Batalov Artem , Pogosbekyan Eduard , Zakharova Natalia , Shugai Svetlana , Pronin Igor
Published: August 2024

In our recent research, we have effectively demonstrated the feasibility of classifying magnetic resonance images (MRI) of glial tumors into four histological types utilizing standardized volume of interest (VOI), radiomics and machine learning. This research aims to determine the reproducibility of our approach when the locations of VOI are changed. We were able to demonstrate high reproducibility of ML results when the same feature selection methodology was employed across different VOIs. However, the reproducibility of radiomic features and their sets among various VOIs was not ensured for the sample size (n = 85) studied. The limited reproducibility of radiomic features should be taken into account when evaluating radiomics studies in glial tumors.
How to Tell Whether Patients Engage and Use a Patient Portal – An Analysis of Five Functions
Authors: Hægermark Emil Aale , Nemeth Julia , Faxvaag Arild
Published: August 2024

Background: The road to a more sustainable healthcare system includes creating a digital interface to the healthcare system that patients can use to engage in their health problems while outside the consultation room. The aim of the study was to evaluate trends in the use of functions in a patient portal and assess which functions were most likely to contribute to sustainable healthcare. Study design: Open, uncontrolled retrospective analysis of citizens’ use of the patient portal. Methods: Extraction and statistical analysis of log data. Results: Log analysis revealed that patients engaged with the patient portal to make appointments, fill out and submit questionnaires, send messages to their care provider, inspect their laboratory results, and view notes about themselves. The functions that displayed a significantly increasing trend were the number of appointments made, the number of messages sent, and the number of checked test results. Discussion: While portal engagement can reduce patient dependency on healthcare services, external factors also influence this outcome. Further research is needed to investigate which functions support healthcare sustainability and enhance patient empowerment, possibly through other study designs.
Are You the Outlier? Identifying Targets for Privacy Attacks on Health Datasets
Authors: Halilovic Mehmed , Meurers Thierry , Otte Karen , Prasser Fabian
Published: August 2024

The identification of vulnerable records (targets) is an important step for many privacy attacks on protected health data. We implemented and evaluated three outlier metrics for detecting potential targets. Next, we assessed differences and similarities between the top-k targets suggested by the different methods and studied how susceptible those targets are to membership inference attacks on synthetic data. Our results suggest that there is no one-size-fits-all approach and that target selection methods should be chosen based on the type of attack that is to be performed.
Development of a Maturity Model for Medical Patient Registries – A Community Approach
Authors: Niemeyer Anna ,   TMF WG Medical Registries, Regierer Anne C. , Hartz Tobias , Veit Christof , van den Berg Neeltje , Beckedorf Irina , Schlünder Irene , Neubauer Emily , Hoffmann Wolfgang , Semler Sebastian C. , Röhrig Rainer
Published: August 2024

Objective: Development of a generic model to visualize the potential for use and further development of registries to assess the suitability of the registry for a specific purpose. Methods: Multi-stage community approach. Results: The maturity model has 9 categories with 105 items. The purpose of the registry is mapped via potential usage dimensions. Conclusion: Important for acceptance is the appropriateness of the requirements in relation to the purposes.
Classifier Chains for LOINC Transcoding
Authors: Michel-Picque Théodore , Bringay Sandra , Poncelet Pascal , Patel Namrata , Mayoral Guilhem
Published: August 2024

Purpose: Mapping clinical observations and medical test results into the standardized vocabulary LOINC is a prerequisite for exchanging clinical data between health information systems and ensuring efficient interoperability. Methods: We present a comparison of three approaches for LOINC transcoding applied to French data collected from real-world settings. These approaches include both a state-of-the-art language model approach and a classifier chains approach. Results: Our study demonstrates that we successfully improve the performance of the baselines using the classifier chains approach and compete effectively with state-of-the-art language models. Conclusions: Our approach proves to be efficient, cost-effective despite reproducibility challenges and potential for future optimizations and dataset testing.
Challenges and Lessons Learned in Mapping HL7 v2 Data to openEHR: Insights from UKSH Medical Data Integration Center
Authors: Anywar Michael , Macedo Mário , Pazmino Santiago , Bronsch Tobias , Kinast Benjamin , Kock-Schoppenhauer Ann-Kristin , Schreiweis Björn
Published: August 2024

This paper explores the challenges and lessons learned during the mapping of HL7 v2 messages structured using custom schema to openEHR for the Medical Data Integration Center (MeDIC) of the University Hospital, Schleswig-Holstein (UKSH). Missing timestamps in observations, missing units of measurement, inconsistencies in decimal separators and unexpected datatypes were identified as critical inconsistencies in this process. These anomalies highlight the difficulty of automating the transformation of HL7 v2 data to any standard, particularly openEHR, using off-the-shelf tools. Addressing these anomalies is crucial for enhancing data interoperability, supporting evidence-based research, and optimizing clinical decision-making. Implementing proper data quality measures and governance will unlock the potential of integrated clinical data, empowering clinicians and researchers and fostering a robust healthcare ecosystem.
Development and Implementation of Semantic Information Standards for Oncology
Authors: Ebben Kees C.W.J. , Vermeulen Lonneke R.E. , Swinkels Astrid C.P. , Sloep Matthijs , van der Werf Jurrian
Published: August 2024

Data exchange in oncological healthcare is hindered by insufficient standardization agreements. An Information Standard comprises agreements facilitating accurate communication of care information with the necessary quality and timeliness. We introduce a structured approach to designing, implementing, and maintaining semantic information standards for oncology, supporting information use across medical scenarios. It consists of an element dataset organized into three tiers, ensuring comprehensive documentation and reliable information exchange. These agreements enhance health data interoperability and system functionality, governed by semantic standardization. Together with communication standards, they empower healthcare professionals with extensive medical records and grant patients control over their health data. Consequently, a high-quality semantic information standard supports both providers and patients, and is adequate during development and manageable during maintenance.
Scalar Values in SNOMED CT: A Proposed Extension
Authors: Ehrsam Julien , Gaudet-Blavignac Christophe , Lovis Christian
Published: August 2024

Representing numeric values such as scalars holds great importance for accurately depicting clinical data. While the result value itself will always be represented using an integer, decimal, or other scalar format, it needs to be linked to its corresponding data element. In SNOMED CT, as in most other terminology systems, this is done through an attribute relationship. While some scalar values are already included in this way, they only represent a small fraction of possibilities. Our intention is to expand the scope of scalar representation by validating new attributes using a previously established method. The result is a list of five attributes validated for local representation of scalar values, improving semantic representation and interoperability.
A Missing Link on Semantic Interoperability
Authors: Rossimori Angelo , Girolami Sandro , Freriks Gerard
Published: August 2024

Syntactic interoperability among health ICT systems is well-established, but achieving semantic interoperability requires more than just exchanging codes. We present a pragmatic, safe, and effective approach towards an ambitious goal: enabling any software to process a critical mass of routine clinical information in a replicable manner across various systems and local contexts. We advocate for the use of reliable, parsimonious coding to handle the most replicable aspects of data processing for routine patient information, while reserving the intricate interpretation of individual patient data nuances for skilled professionals, possibly supported by Artificial Intelligence tools. We suggest coping with routine tasks by focusing on a limited set of a few thousand data elements, named the ‘Clinical Documentation Kernel’ (CDK). This approach will provide direct benefits to users and assist in the human interpretation of other patient information. Our preliminary study focuses on the ‘primitives’ and ‘qualifiers’ that bring the highest value to the health ecosystem in various authoritative scenarios in the field of diabetes.
Using Health IT Standards to Facilitate Metadata Exchange Between Research Data Management Systems
Authors: Löbe Matthias , Hu Xiaoming , Klopfenstein Sophie A.I.
Published: August 2024

The National Research Data Infrastructure for Personal Health Data (NFDI4Health) uses Local Data Hubs (LDHs) to manage locally research studies, documents and sensitive personal data to support controlled data sharing. While research data management (RDM) systems facilitate the storage and preparation of data and metadata as well as organizational access, they often lack support for interoperability standards of the application domain. To support the exchange with external registries of research studies, we chose 17 attributes to characterize the most relevant aspects of clinical trials (in the following named “metadata profile”). We implemented the metadata profile in the RDM system FAIRDOM SEEK using core attributes and SEEK’s extended metadata feature and created a mapping conforming to the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) standard version R4. Finally, we implemented a prototype application interface for exports in FHIR-JSON format. We plan to extend the interface to serve central registries and support specific FHIR Implementation Guides from various use cases.
Improving Clinical Documentation of Rare Neuromuscular Diseases: Development of a Standardised Information Model
Authors: Hübner Miriam , Nyoungui Elisabeth , Gazzero Elisabetta , Hassoun Lina , Zschüntzsch Jana , Krefting Dagmar , Schepers Josef , Röttgerd Richard
Published: August 2024

Rare neuromuscular diseases (NMDs) encompass various disorders of the nervous system and skeletal muscles, and present intricate challenges in diagnosis, treatment, and research due to their low prevalence and often diverse multisystemic manifestations. Leveraging collected patient data for secondary use and analysis holds promise for advancing medical understanding in this field. However, a certain level of data quality is a prerequisite for the methods that can be used to analyze data. The heterogeneous nature of NMDs poses a significant obstacle to the creation of standardized documentation, as there are still many challenges to accurate diagnosis and many discrepancies in the diagnostic process between different countries. This paper proposes the development of an information model tailored to NMDs, aiming to augment visibility, address deficiencies in documentation, and facilitate comprehensive analysis and research endeavors. By providing a structured framework, this model seeks to propel advancements in understanding and managing NMD, ultimately benefiting patients and healthcare providers worldwide.