bluuebunny/crossref_metadata_2025 huggingface dataset:
Online Survey Study on the Needs of Former Intensive-Care Patients for the Development of a User-Friendly App
Authors: Molinnus Denise , Kurth Angelique , Mainz Anne , Meister Sven , Marx Gernot , Bickenbach Johannes
Published: August 2024

The aim of this online-survey study is the development and evaluation of a mobile health application specifically designed to meet the needs of individuals who have previously undergone intensive-care treatment. User acceptance and perception play a crucial role in refining and optimizing the app’s features and functionalities. By actively incorporating suggestions and insights from users, the goal is to enhance the overall usability and better cater to the diverse needs of individuals in post-intensive care recovery. This iterative approach ensures that the application remains responsive to the evolving requirements of its target audience. Overall, the emphasis is on creating a user-centric and adaptive tool for former intensive care patients, to develop a user-friendly mobile app.
Elevating Patient Engagement: Implementing EHR-Integrated Medication Reminders for Kidney Transplant Patients
Authors: Oudbier Susan J. , Peute Linda W. , Teeuwisse Patty J.I. , Aarts Juul , Janssen Sharon L. , Nurmohamed S. Azam , Smets Ellen M.A. , Meij Hans J.
Published: August 2024

Digital health can enhance self-management of patients such as usage of medication reminders, thereby improving health outcomes. However, for successful implementation of such interventions, integration with the electronic health record (EHR) is useful. We evaluated the implementation of an integrated patient portal medication reminder tool in kidney transplant patients. Overall, 40.5% of the patients agreed that integrated EHR medication reminders assisted them in taking their medication on time.
Nordic Citizens’ Willingness to Share Digital Health Data
Authors: Eriksen Jeppe , Hjermitslev Camilla Bidstrup , Reponen Jarmo , Hardardottir Gudrun Audur , Tuulikki Vehko , Faxvaag Arild , Nøhr Christian
Published: August 2024

The quality of the digital healthcare systems relies on citizens’ willingness to share their digital health data. This makes citizens’ use, perceptions, and attitudes towards digital healthcare systems pivotal. The study presented here examines Nordic citizens’ willingness to share digital health data with healthcare providers and for research purposes. A cross-sectional study design was applied to obtain answers from citizens in Denmark, Finland, Iceland, Norway, and Sweden. The results are based on answers from 5078 citizens across the five countries. Results based on descriptive statistics indicate that the majority of Nordic citizens are willing to share health data that has clinical relevance with healthcare providers and for research purposes. The odds ratio analysis reveals that citizens’ odds of sharing health data decreases with age and increases with the level of education. Conclusively, this study shows that most Nordic citizens are willing to share their health data, influenced by age and level of education. Awareness of and efforts to support citizens who are unable or unwilling to actively use and engage with the digital healthcare system is recommended.
A Web-Based Questionnaire Builder to Facilitate form Management for the Electronic Data Capture with REDCap
Authors: Auer Florian , Schmid Verena , Kramer Frank
Published: August 2024

REDCap, a popular platform for building surveys for electronic data capture, offers two methods for creating questionnaires: an interactive web interface to modify single questions and an upload method to import entire questionnaires. Both methods present limitations in terms of usability and time needed for different tasks. We propose a browser-based web application to design and manage REDCap questionnaires using a What-You-See-Is-What-You-Get approach. The application provides a user-friendly interface for a comprehensive overview of all imported questionnaires, and three distinct views cater to different aspects of the questionnaire design process. The questionnaires can be imported and exported through the REDCap CSV format and thus integrate seamlessly into its environment. REDCapQB represents a significant advancement in questionnaire design and management, offering researchers a powerful and user-friendly tool for electronic data capture in translational research studies within the REDCap ecosystem.
Privacy-Preserving Search on Medical Data
Authors: Herres Britta , Poschen Carolin , Knorr Konstantin
Published: August 2024

Data sharing spaces for medical data are necessary to facilitate research. To make medical data available for research, a mechanism is preferable that not only provides data a researcher has legal access to, but also contributes to the investigation of their specific research hypothesis. We propose a three-party two-stage search algorithm initiated by a researcher on centrally stored but technically and organizationally separated data. The search seeks to minimize the risk of reidentification of patients and to enable data minimization. In the first stage, we only access data IDs of patients meeting the cohort criteria. In the second stage, the actual data is downloaded if the set of matching patients satisfies the minimum cohort size. Our approach is privacy preserving, as only the researcher is able to connect medical and demographic data, while no single malicious party can get data access. We thereby hope to pave the way for a privacy-aware health data sharing space as currently proposed by the EU.
Challenges Implementing Patient and Public Involvement in a Digital Health Agile Project Which Includes Research, Business and Software Development
Authors: Yrttiaho Tiia , Giunti Guido , Isomursu Minna
Published: August 2024

Patient and public involvement (PPI) is a practice for involving future users in the design, development, and research of health technologies. There is increasing interest and demand for PPI, but little evidence based methodological support for integrating PPI in agile development processes. Multiple sclerosis (MS) is a chronic condition that severely impacts the lives of patients and requires active patient involvement. In this paper, we present the results of a case study, examining the adoption and integration of PPI into the development of a digital therapeutics solution for MS. The results highlight five critical phases that proved to be challenging: selecting patient participants, onboarding of patient participants and employees, framing tasks for patient participants, communication between patient participants and the rest of the development team, and reimbursements. The results are useful in creating evidence-based guidelines and methods for supporting the adoption of PPI.
Converting Entity-Attribute-Value Data Sources to OMOP’s CDM: Lessons Learned
Authors: Katsch Florian , Hussein Rada , Duftschmid Georg
Published: August 2024

Clinical data repositories often use entity-attribute-value (EAV) data models. To be valuable for secondary use, these health data can be transformed to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The present paper describes the lessons learned from such an endeavour based on the concept of registering transformation functions on source data elements. We further provide future work directions for follow-up projects.
Improving Health Information Systems Data Quality in Sub-Saharan Africa
Authors: Sylla Bry , Savadogo Léon , Diallo Gayo
Published: August 2024

Data quality in health information systems (HIS) is essential for informed decision-making in the health sector, particularly in sub-Saharan Africa (SSA) where these systems face many challenges like resource limitations and weak infrastructure. This systematic review assessed the quality of HIS data in the region, focusing on the dimensions, and factors influencing this quality. It highlights the importance of systematic evaluation, ongoing training for data collectors in the analysis and use of data for decision-making, and the adoption of information and communication technologies in the healthcare system to improve data quality. These findings point the way to better use of health data and the need for a more integrated approach to digital health in SSA.
Public Availability of Data Management and Analysis Scripts of Studies Conducted on Open-Access Intensive Care Databases
Authors: Andries Coline , Quindroit Paul , Popoff Benjamin , Oudarrour Ikram , Lamer Antoine
Published: August 2024

Intensive care units (ICUs) provide care for critical patients at high risk of morbidity and mortality, and require continuous monitoring of clinical, biological and, imaging parameters. Collaborative ventures have enabled the emergence of large open access databases for the secondary use of Electronic Health Records (EHRs). The objective of this work was to evaluate the availability of scripts and datasets in publications based on ICU open-access databases. We included 910 original articles based on four ICU open-access databases (Amsterdam University Medical Centers Database, eICU Collaborative Research Database, High time resolution ICU dataset, and Medical Information Mart for Intensive Care). The majority of the studies did not provide their data management scripts (n=839, 92.9%), neither the analysis script (n=843, 93.4%) in the article. Attempts to contact the 845 corresponding authors in question resulted in 89.11% (n=753) of our e-mail requests going unanswered over a two-month period. We received 51 automated messages (55.43%) indicating that emails have not been delivered, while 6 messages (6.52%) redirected to alternative email addresses. Only 20 corresponding authors (18.18%) answered, finally providing the requested materials. Despite scientific journals recommendations to share materials, our study unveils the absence of crucial components for the replication of studies by other research teams.
Insights into the Quality of Mobile Health Apps: Preliminary Results of an Analysis of MARS Scores
Authors: Lef Hendrik , Swoboda Walter , Schobel Johannes , Hieber Daniel , Holl Felix
Published: August 2024

Many mHelath applications have been developed, and the Mobile App Rating Scale (MARS) is a common tool for assessing them. This study aims to provide mean values for MARS scores found in recent literature. We systematically searched for literature in which MARS was used and analyzed them. MARS values for 5,920 applications from 215 studies were compiled. The mean MARS Quality Score is 3.51. The highest average score was achieved in the Functionality category (3.98), followed by Aesthetics (3.52), Information (3.33), Engagement (3.18) and Subjective (2.72). To the best of our knowledge, this is the first study to calculate average values for the five categories of the MARS and the MARS score based on such an extensive collection of data. The study shows that the overall quality of the applications is above the average value of 2.5.
Success Factors for Usability Testing of Digital Health Solutions
Authors: Reichenpfader Daniel , Rivera-Romero Octavio , Denecke Kerstin
Published: August 2024

Background: The design and development of patient-centered digital health solutions requires user involvement, for example through usability testing. Although there are guidelines for conducting usability tests, there is a lack of knowledge about the technical, human, and organizational factors that influence the success of the tests. Objective: To summarize the success factors of usability testing in the context of patient-centered digital health solutions. Method: We considered three case studies and collected experiences related to time management, relevance of results and challenges encountered. Results: Success factors relate to participant privacy and data protection, test environment setup, device and application readiness, user comfort and accessibility, test tools and procedures, and adaptability to user limitations. Conclusions: Small organizational and technical details can have a big impact on the outcome of a usability test. Considering the aspects mentioned in this paper will not only save resources but also the trust of the participating patients.
An In-Depth Exploration of Mapping openEHR and PGHD: A Case Study on Fitbit-Generated Data
Authors: Abedian Somayeh , Taucher Christian , Perscha Severin , Djuris Michael , Hussein Rada , Hanke Sten
Published: August 2024

In recent years, the adoption of wearable gadgets such as Fitbit has revolutionized the way individuals track and monitor their personal activity data. These devices provide valuable in-sights into an individual’s physical activity levels, sleep patterns, and overall health metrics. Integrating this data into healthcare informatics systems can offer significant benefits in terms of personalized healthcare delivery and improved patient outcomes. This paper explores the synergistic integration of Fitbit-generated personal activity data using the openEHR Reference Model in healthcare informatics as a practical case study in patient-generated health data (PGHD) integration based on health informatics standards as a framework for the representation and exchange of Electronic Health Records (EHRs). The synergistic integration of Fitbit-generated personal activity data through openEHR and FHIR standards models also covers the way for advanced analytics and population health management. By linking and analyzing data from various sources, including sensors and wearable devices, healthcare organizations can identify trends, patterns, and insights that can guide population health strategies, preventive care initiatives, and personalized treatment plans, in addition to aiding physicians in follow-up care.
Optimizing Remote Health Monitoring for Digital Platforms: Evaluating the Efficacy of Gamification in Enhancing Data Collection
Authors: Atzeni Michele , Cossu Luca , Vettoretti Martina , Cappon Giacomo
Published: August 2024

In recent years, the integration of game-like elements into non-gaming contexts has shown promise in enhancing user engagement and motivation. This study assesses the impact of gamification elements on data collection efficacy in m-health applications. An ad-hoc mobile application was developed and used in a randomized two-arm pilot study. Participants interacted either with the gamified meal-logging application or with its non-gamified version for ten days. The results from this study emphasize the benefits of incorporating gamification techniques into health applications embedded in digital platforms. While both versions were well-received, reaching high System Usability Scale (SUS) scores (91 and 93.5) and generally positive feedback, the gamified app demonstrated a distinct advantage in promoting user engagement and consistent data logging. This highlights the importance of gamification in health research, suggesting its potential to ensure thorough and consistent data collection, which is essential for producing reliable research outcomes.
Evaluating the Usability of a Telehealth System for People with COPD: A Cross Sectional Study
Authors: Egmose Julie , Laursen Sisse Heiden , Udsen Flemming Witt , Hæsum Lisa Korsbakke Emtekær , Kronborg Thomas , Hejlesen Ole , Hangaard Stine
Published: August 2024

Pulmonary Disease (COPD) exacerbations. However, the effect of telehealth for COPD remains uncertain, which may be due to a lack of attention to usability during the development of telehealth solutions. The aim was to evaluate the usability of a telehealth system for COPD using the Danish Telehealth Usability Questionnaire. A total of 96 people with COPD, who were already using a telehealth system consisting of weekly measurements of physiological parameters and symptom-related questionnaires, were included. The D-TUQ was used to assess the usability of the telehealth system. The overall experience with the usability of the telehealth system was mainly positive, but there was room for improvement.
Muzima Mobile Application for Screening Hypertension and Diabetes: A User Experience of the App Among Community Health Workers in Rwanda
Authors: Mugisha Michael , Ntakirutimana Isaac , Kayiranga Dieudonne , Muhirwa Augustine , Nkurunziza Emmanuel , Sibomana Emmanuel , Uwamahoro Angelique , Igihozo Theophila , Ndoli Amelie , Niyonshuti Emmanuel , Rwubaka Eugene , Tuyishime Isdore , Umutoniwase Emma Marie , Mugisha Emmy
Published: August 2024

Mobile technology has become the leading utility in the social and well-being of people especially in low-resource settings. The use of mobile applications in healthcare promise to improve care and treatment. This study explored the user experience of muzima mobile application among community health workers in Rwanda. We used three data collection methods: observation, Key informant interviews and focus group discussions. We analysed data using thematic content analysis. We found that users were able to complete tasks in the app although some less experienced and older participants struggled to complete the tasks. Users felt that the application helped them to screen and manage patients with diabetes and hypertension in the community which reduced frequent visits to the health centers. Users felt that the application needs improvements in the workflow to facilitate the ease of use. They suggested to digitse other health programs implemented by community health workers. To improve the use and ensure wider implementation, there is a need to consider users’ needs and concerns as discussed in this paper.
Unobtrusive Monitoring of Clinical Deterioration in Smart Homes
Authors: Rostam Niakan Kalhori Sharareh , Tanhapour Mozhgan , Deserno Thomas M.
Published: August 2024

Clinical deterioration (CD) is the physiological decompensation that incurs care escalation, protracted hospital stays, or even death. The early warning score (EWS) calculates the occurrence of CD based on five vital signs. However, there are limited reports regarding EWS monitoring in smart home settings. This study aims to design a CD detection system for health monitoring at home (HM@H) that automatically identifies unstable vital signs and alarms the medical emergency team. We conduct a requirement analysis by interviewing experts. We use unified modeling language (UML) diagrams to define the behavioral and structural aspects of HM@H. We developed a prototype using a SQL-based database and Python to calculate the EWS in the front end. A team of five experts assessed the accuracy and validity of the designed system. The requirement analysis for four main users yielded 30 data elements and 10 functions. Three main components of HM@H are the graphical user interface (GUI), the application programming interface (API), and the server. Results show the possibility of using unobtrusive sensors to collect smart home residents’ vital signs and calculate their EWS scores in real-time. However, further implementation with real data, for frail elderly and hospital-discharged patients is required.
Designing a Social Snus Cessation Mobile Application with an Integrated AI Function
Authors: Citoler Alba Puyuelo , Lee Eunji
Published: August 2024

The application of artificial intelligence (AI) in healthcare is expected to be increased in the coming years. There has been little attention paid on exploring how social aspects and AI can be integrated into mobile applications to support the people who want to quit snus. Our research aims to design a prototype of a social snus cessation mobile application for university students in Sweden, incorporating an AI function. This paper presents a work-in-progress of our research.
Initial Investigations into Physician Acceptance of Medical AI: Examining Trust, Resistance, Perceived Job Insecurity, and Usage Intentions
Authors: Chen Chia-Jung , Liu Chung-Feng
Published: August 2024

This study evaluated physicians’ attitudes towards medical AI across three Taiwanese hospitals, focusing on constructs of trust, resistance, job insecurity, and adoption willingness, with a survey based on the Dual-factor Model yielding 282 responses and a 94% response rate. Results showed positive trust in AI, low resistance and job insecurity concerns, and a high willingness to adopt AI, indicating a favorable view of AI as a supportive tool rather than a replacement. Key adoption factors were identified as regulatory standards, accuracy, workflow integration, and result clarity, providing valuable insights for future AI development in medicine.
Exploring the Intersection of AI and Inclusive Design for People with Disabilities
Authors: El Morr Christo , Singh Damanjot , Sawhney Vaibhav , Fernandes Sabine , El-Lahib Yahya , Gorman Rachel
Published: August 2024

This scoping review examines current research on AI for inclusive design for people with disabilities. We identified both advantages and challenges of AI-based solutions and suggested future research directions. Our search of four online databases for studies from the last five years revealed promising AI applications in education, daily living, home environments, workplaces, and healthcare. However, limitations include limited research, lack of user involvement, potential data bias, and reporting deficiencies. We stress the importance of future research prioritizing user-centered design, inclusive participation, AI bias mitigation, consideration of diverse populations, and ensuring user-friendly performance to fully realize AI’s potential for accessibility and inclusion.
Well-Tempered Medical Prompt Engineering for Explainable Extubation
Authors: Lee Sujung , Cho Won Ik , Park Chansung , Ko Taehoon
Published: August 2024

This study investigated whether the large language model (LLM) utilizes sufficient domain knowledge to reason about critical medical events such as extubation. In detail, we tested whether the LLM accurately comprehends given tabular data and variable importance and whether it can be used in complement to existing ML models such as XGBoost.
MedFrenchmark, a Small Set for Benchmarking Generative LLMs in Medical French
Authors: Quercia Amandine , Zaghir Jamil , Lovis Christian , Gaudet-Blavignac Christophe
Published: August 2024

Generative Large Language Models (LLMs) have become ubiquitous in various fields, including healthcare and medicine. Consequently, there is growing interest in leveraging LLMs for medical applications, leading to the emergence of novel models daily. However, evaluation and benchmarking frameworks for LLMs are scarce, particularly those tailored for medical French. To address this gap, we introduce a minimal benchmark consisting of 114 open questions designed to assess the medical capabilities of LLMs in French. The proposed benchmark encompasses a wide range of medical domains, reflecting real-world clinical scenarios’ complexity. A preliminary validation involved testing seven widely used LLMs with a parameter size of 7 billion. Results revealed significant variability in performance, emphasizing the importance of rigorous evaluation before deploying LLMs in medical settings. In conclusion, we present a novel and valuable resource for rapidly evaluating LLMs in medical French. By promoting greater accountability and standardization, this benchmark has the potential to enhance trustworthiness and utility in harnessing LLMs for medical applications.
On the Fidelity-Privacy Tradeoff of Synthetic Cancer Registry Data
Authors: Röchner Philipp
Published: August 2024

The sharing of personal health data is highly regulated due to privacy and security concerns. An alternative to sharing personal data is to share synthetic data, because ideally it should be impossible to reconstruct real personal data from synthetic data, which is called privacy. At the same time, the structure of the synthetic data should be as similar as possible to the structure of the real data to ensure that conclusions drawn from the synthetic data are also valid for the real data, which is called fidelity. Typically, there is a tradeoff between fidelity and privacy for synthetic health data. We study the fidelity and privacy of cancer data synthesized using generative machine learning approaches. To generate synthetic cancer data, we use variational autoencoders (VAEs), generative adversarial networks (GANs), and denoising diffusion probabilistic models (DDPMs). The tabular cancer registry data studied have nine categorical variables from breast cancer patients. We find that DDPMs generate synthetic cancer data with higher fidelity; that is, the structure of the synthetic data is more similar to the real cancer data than the data generated by VAEs and GANs. At the same time, synthetic cancer data from DDPMs pose a greater privacy risk because the data are more likely to reveal information from real patients than synthetic data from VAEs and GANs.
Quality Assessment of Brain MRI Defacing Using Machine Learning
Authors: Sadeghi Sina , Khodaei Maryam , Hempel Lars , Kirsten Toralf
Published: August 2024

Defacing of brain magnetic resonance imaging (MRI) scans is a crucial process in medical imaging research aimed at preserving patient privacy while maintaining data integrity. However, existing defacing algorithms are prone to errors, potentially compromising patient anonymity. This paper investigates the feasibility and efficacy of automated quality assessment for defaced brain MRIs using machine learning (ML). Our findings demonstrate the promising capability of ML models in accurately distinguishing between properly and inadequately defaced MRI scans.
Physicians’ Attitudes Towards Artificial Intelligence: Results of the PEAK Project
Authors: Negash Sarah , Gundlack Jana , Buch Charlotte , Apfelbacher Timo , Christoph Jan , Schildmann Jan , Frese Thomas , Mikolajczyk Rafael
Published: August 2024

Artificial intelligence (AI) is rapidly reshaping the medical field. This study aimed to investigate the attitudes of physicians towards AI in medical care using focus groups. Most participants seemed to be open to the use of AI in medicine if transparency in AI systems is ensured, regulatory barriers are addressed and personal contact to the patient is maintained. This qualitative study allows insights into how German physicians perceive the use of AI in medical care. Gaining input from physicians is important when designing future applications of AI for the practical use in medical care.
Preliminary Evaluation of Fine-Tuning the OpenDeLD Deidentification Pipeline Across Multi-Center Corpora
Authors: Gupta Shalini , Liu Jiaxing , Wong Zoie Shiu-Yee , Jonnagaddala Jitendra
Published: August 2024

Automatic deidentification of Electronic Health Records (EHR) is a crucial step in secondary usage for biomedical research. This study introduces evaluation of an intricate hybrid deidentification strategy to enhance patient privacy in secondary usage of EHR. Specifically, this study focuses on assessing automatic deidentification using OpenDeID pipeline across diverse corpora for safeguarding sensitive information within EHR datasets by incorporating diverse corpora. Three distinct corpora were utilized: the OpenDeID v2 corpus containing pathology reports from Australian hospitals, the 2014 i2b2/UTHealth deidentification corpus with clinical narratives from the USA, and the 2016 CEGS N-GRID identification corpus comprising psychiatric notes. The OpenDeID pipeline employs a hybrid approach based on deep learning and contextual rules. Pre-processing steps involved harmonizing and addressing encoding and format issues. Precision, Recall, F-measure metrics were used to assess the performance. The evaluation metrics demonstrated the superior performance of the Discharge Summary BioBERT model. Trained on three corpora with a total of 4,038 reports, the best performing model exhibited robust deidentification capabilities when applied to EHR. It achieved impressive micro-averaged F1-scores of 0.9248 and 0.9692 for strict and relaxed settings, respectively. These results offer valuable insights into the model’s efficacy and its potential role in safeguarding patient privacy in secondary usage of EHR.
Comparison of Ensemble Learning Methods for Classification in Cancer Registries
Authors: Schult Nico , Wolters Timo , Hermes Marc , Dählmann Klaas , Hein Andreas
Published: August 2024

Significant developments are currently underway in the field of cancer research, particularly in Germany, regarding cancer registration and the use of medical information systems. The use of such systems contributes significantly to quality assurance and increased efficiency in data evaluation. The growing importance of artificial intelligence (AI) in cancer research is evident as these systems integrate AI for various purposes, i.e. to assist users in data analysis. This paper uses ensemble learning to classify the graphical user interface state of the medical information system CARESS. The results show that all ensemble learning models utilized achieved good performance. In particular, the gradient boosting algorithm performed the best with an accuracy of 97%. The results represent a starting point for further development of ensemble learning in medical data analysis, with the potential for integration into various applications such as recommender systems.
Exploring Prediabetes Pathways Using Explainable AI on Data from Electronic Medical Records
Authors: Console Davide , Lenatti Marta , Simeone Davide , Keshavjee Karim , Guergachi Aziz , Mongelli Maurizio , Paglialonga Alessia
Published: August 2024

This study leverages data from a Canadian database of primary care Electronic Medical Records to develop machine learning models predicting type 2 diabetes mellitus (T2D), prediabetes, or normoglycemia. These models are used as a basis for extracting counterfactual explanations and derive personalized changes in biomarkers to prevent T2D onset, particularly in the still reversible prediabetic state. The models achieve satisfactory performance. Furthermore, feature importance analysis underscores the significance of fasting blood sugar and glycated hemoglobin, while counterfactuals explanations emphasize the centrality of keeping body mass index and cholesterol indicators within or close to the clinically desirable ranges. This research highlights the potential of machine learning and counterfactual explanations in guiding preventive interventions that may help slow down the progression from prediabetes to T2D on an individual basis, eventually fostering a recovery from prediabetes to a normoglycemic state.
Pre-Processing of Categorical Features Within Medical Analysis Systems
Authors: Hermes Marc , Wolters Timo , Schult Nico , Hein Andreas
Published: August 2024

The complexity of the cancer problem domain presents challenges not only to the medical analysis systems tasked with its analysis, but also to the users of such systems. While it is desirable to assist users in operating these medical analysis systems, prior groundwork is required before this can be achieved, such as recognising patterns in the way users create certain analyses within these systems. In this paper, we use machine learning algorithms to analyse user behaviour patterns and attempt to predict the next user interaction within the CARESS medical analysis system. Since an appropriate pre-processing scheme is essential for the performance of these algorithms, we propose the usage of a Natural Language Processing (NLP)- inspired approach to preserve some semantic cohesion of the mostly categorical features of these user interactions. Furthermore, we propose to use a sliding window that contains information about the latest user interactions in combination with Latent Dirichlet Allocation (LDA) to extract a latent topic from these last interactions and use it as additional input to the machine learning models. We compare this pre-processing scheme with other approaches that utilise one-hot encoding and feature hashing. The results of our experiments show that the sliding window LDA scheme is a promising solution, that performs better for our use case than the other evaluated pre-processing schemes. Overall, our results provide an important piece for further research and development in the area of assisting users in operating analysis systems in complex problem domains.
Unlocking the Potential of Free Text in Electronic Health Records with Large Language Models (LLM): Enhancing Patient Safety and Consultation Interactions
Authors: Kumarapeli Pushpa , Haddad Tia , de Lusignan Simon
Published: August 2024

Computer-mediated clinical consultation, involving clinicians, electronic health record (EHR) systems, and patients, yield rich narrative data. Despite advancements in Natural Language Processing (NLP), these narratives remain underutilised. Free text recording in EHRs allows expressivity, complements structured data from clinical coding systems, and facilitates collaborative care. Large language models (LLMs) excel in understanding and generating natural language, enabling complex dialogue processing. Integrating LLM tools into consultations could harness the untapped potential of free text to identify patient safety concerns, support diagnosis and provide content to enhance clinical-patient interactions. Tailoring LLMs for specific consultation tasks through pre-training and fine-tuning is viable. This paper outlines approaches for adopting LLMs in primary care and suggests that using fine-tuned LLMs with prompt engineering could enhance computer-mediated clinical consultation cost-effectively.
Integrating AI-Generated Summaries of Medication Leaflets into a Mobile Application for Medication Management
Authors: Heiler Lisa , Kirchsteiger Katharina , Hartl Simon , Perkics Oliver , Hanke Sten , Bödenler Markus
Published: August 2024

Effective medication management poses significant challenges, particularly when navigating multiple medications with intricate dosages and schedules. This paper presents a prototype mobile application to streamline information retrieval from dense medication leaflets. By utilizing automated information extraction based on large language models, the application seamlessly retrieves pertinent details from the Austrian medicinal product index upon scanning the medication package. This extracted information is organized and displayed within the app, ensuring clarity and accessibility for users. In addition to this core functionality, the application offers a suite of features tailored to facilitate effective medication management. By integrating comprehensive medication information with practical medication management tools, the application empowers users to navigate complex medication regimes with confidence and ease.
Fine-Tuning SSL-Model to Enhance Detection of Cilioretinal Arteries on Colored Fundus Images
Authors: Gobalakrishnan Warnes , Zimmermann Julian , Storck Michael , Yildirim Kemal , Brücher Viktoria Constanze , Eter Nicole , Varghese Julian
Published: August 2024

Cilioretinal arteries are a common congenital anomaly of retinal blood supply. This paper presents a deep learning-based approach for the automated detection of a CRA from color fundus images. Leveraging the Vision Transformer architecture, a pre-trained model from RETFound was fine-tuned to transfer knowledge from a broader dataset to our specific task. An initial dataset of 85 was expanded to 170 images through data augmentation using self-supervised learning-driven techniques. To address the imbalance in the dataset and prevent overfitting, Focal Loss and Early Stopping were implemented. The model’s performance was evaluated using a 70-30 split of the dataset for training and validation. The results showcase the potential of ophthalmic foundation models in enhancing detection of CRAs and reducing the effort required for labeling by retinal experts, as promising results could be achieved with only a small amount of training data through fine-tuning.
Evaluating Synthetic Data Augmentation to Correct for Data Imbalance in Realistic Clinical Prediction Settings
Authors: Wahler Nina , Kaabachi Bayrem , Kulynych Bogdan , Despraz Jérémie , Simon Christian , Raisaro Jean Louis
Published: August 2024

Predictive modeling holds a large potential in clinical decision-making, yet its effectiveness can be hindered by inherent data imbalances in clinical datasets. This study investigates the utility of synthetic data for improving the performance of predictive modeling on realistic small imbalanced clinical datasets. We compared various synthetic data generation methods including Generative Adversarial Networks, Normalizing Flows, and Variational Autoencoders to the standard baselines for correcting for class underrepresentation on four clinical datasets. Although results show improvement in F1 scores in some cases, even over multiple repetitions, we do not obtain statistically significant evidence that synthetic data generation outperforms standard baselines for correcting for class imbalance. This study challenges common beliefs about the efficacy of synthetic data for data augmentation and highlights the importance of evaluating new complex methods against simple baselines.
Computer Vision for Assessing Surgical Movements in Neurosurgery
Authors: Danilov Gleb , Kostyumov Vasiliy , Pilipenko Oleg , Ilyushin Eugeniy , Pitskhelauri David , Zelenova Alexandra , Bykanov Andrey
Published: August 2024

Objective evaluation of microsurgical technique quality is vital for successful training in neurosurgery. This study aimed to assess the accuracy of automatically detecting a neurosurgeon’s proper posture and hand positioning using computer vision. We employed the RTMPose neural network model to identify key anatomical points in the neurosurgeon’s projection and calculated various angles formed by connecting these points. By utilizing machine learning on these angles, we were able to classify images of the surgeon’s posture and hands into correct positions and various types of errors with an accuracy of at least 0.9. Computer vision enables successful detection and objective assessment of the neurosurgeon’s posture and hand positions. The high accuracy of this detection can pave the way for a new training approach in neurosurgery.
Optimizing Data Extraction: Harnessing RAG and LLMs for German Medical Documents
Authors: Wang Yingding , Leutner Simon , Ingrisch Michael , Klein Christoph , Hinske Ludwig Christian , Danhauser Katharina
Published: August 2024

In the field of medical data analysis, converting unstructured text documents into a structured format suitable for further use is a significant challenge. This study introduces an automated local deployed data privacy secure pipeline that uses open-source Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) architecture to convert medical German language documents with sensitive health-related information into a structured format. Testing on a proprietary dataset of 800 unstructured original medical reports demonstrated an accuracy of up to 90% in data extraction of the pipeline compared to data extracted manually by physicians and medical students. This highlights the pipeline’s potential as a valuable tool for efficiently extracting relevant data from unstructured sources.
Enhancing Patient Empowerment and Health Literacy: Integrating Knowledge Graphs with Language Models for Personalized Health Content Delivery
Authors: Kakalou Christine , Karamanidou Christina , Dalamagas Theodore , Koubarakis Manolis
Published: August 2024

Health literacy empowers people to access, understand and apply health information to effectively manage their own health and to be an active participant in healthcare decisions. In this paper we propose a conceptual model for cognitive factors affecting health literacy and related socioeconomic aspects. Then we develop the HEALIE Knowledge Graph to represent the model, drawing from various medical ontologies, resources, and insights from domain experts. Finally, we combine the Knowledge Graph with a Large Language Model to generate personalised medical content and showcase the results through an example.
Testing Usability of a Medical Device Using Virtual Reality
Authors: Douze Laura , Netter Elie , Wallard Laura , Occelli Florent , Paget Thomas , Charlier Pierre , Schiro Jessica
Published: August 2024

Before commercialization of a medical device, it is important to evaluate its usability. Traditional methods such as user testing to evaluate usability of medical device encountered difficulties to put participants in simulation conditions that are sufficiently realistic to be representative of real life. Virtual reality can be used to immerse participants in a high-fidelity simulation at a lower cost, but is not widely used today. This feasibility study aims to compare the results obtained between user tests in a real simulated environment and in a virtual reality environment, with feedback on the advantages and disadvantages of both conditions.
Precise Identification of Oral Cancer Lesions Using Artificial Intelligence
Authors: Wuttisarnwattana Patiwet , Wongsapai Mansuang , Theppitak Sarit , Ittichaicharoen Jitjiroj , Warin Kritsasith , Thanathornwong Bhornsawan , Suebnukarn Siriwan
Published: August 2024

Dentists, especially those who are not oral lesion specialists and live in rural areas, need an artificial intelligence (AI) system for accurately assisting them in screening for oral cancer that may appear in smartphone images. Not many literatures present a viable model that addresses the needs, especially in the context of oral lesion segmentation in smartphone images. This study demonstrates the use of a deep learning-based AI for simultaneously identifying types of oral cancer lesions as well as precisely outlining the boundary of the lesions in the images for the first time. The lesions of interest were oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) lesions. The model could successfully (1) detect if the images contained the oral lesions, (2) determine types of the lesions, and (3) precisely outline the boundary of the lesions. With future success of our project, patients will be diagnosed and treated early before the pre-cancer lesions can progress into deadly cancerous ones.
Digitalising Clinical Guidelines: The Challenge of Patient’s Preferences
Authors: Rajput Vije Kumar , Kaltoft Mette Kjer , Dowie Jack
Published: August 2024

Will digitalised clinical guidelines be compatible with individualised and personalised patient care if the disease definitions and classifications used within them contain embedded preferences? Taking bone health as a case study, we found the dominant definition of osteoporosis installs the consensus preference judgement of a 1992 International Expert Committee in the form of a threshold cut-off on the bone mineral density continuum. We found that subsequent UK clinical guidelines follow suit on this diagnostic threshold, but also endorse preference-sensitive thresholds for interventions to prevent fractures, including ones underpinned by cost-effectiveness analysis. The resulting pre-emption of patient’s preferences needs to be removed if ‘computable’ guidelines are to be reconcilable with personalised care. The challenges to be met in digitalisation therefore include major conceptual ones as well as the technical ones that are currently the almost exclusive focus.
Development and Implementation of Standardized, Structured Clinical Reporting for Oncology
Authors: Ebben Kees C.W.J. , Swinkels Astrid C.P. , Dermois Marijke M.A. , van der Werf Jurrian , Vermeulen Lonneke R.E.
Published: August 2024

Reuse of clinical data within the healthcare process and for secondary purposes is particularly valuable. This study emphasizes the crucial role of Standardized, Structured Reports (SSRs) in supporting continuity of care while also advancing reusability of data, decision support functionalities, and accommodating future developments. Integrating SSRs with existing information systems poses a serious challenge. The integration of SSRs with information standards enhances their utility in diverse applications. The significance of SSRs is further highlighted by their seamless integration into healthcare processes, and development and implementation is supported by various available applications. This research contributes to the evolution of medical informatics by emphasizing the importance of collaborative efforts in standardized, structured reporting, all aimed at enhancing patient care.
OntoBridge Versus Traditional ETL: Enhancing Data Standardization into CDM Formats Using Ontologies Within the DATOS-CAT Project
Authors: Bracons Cucó Guillem , Gil Rojas Jessyca , Peñafiel Macias Petter , Borrat Frigola Xavier , Lymperidou Aikaterini , Martínez-González Judith , Labarga Alberto , Frid Santiago
Published: August 2024

Common Data Models (CDMs) enhance data exchange and integration across diverse sources, preserving semantics and context. Transforming local data into CDMs is typically cumbersome and resource-intensive, with limited reusability. This article compares OntoBridge, an ontology-based tool designed to streamline the conversion of local datasets into CDMs, with traditional ETL methods in adopting the OMOP CDM. We examine flexibility and scalability in the management of new data sources, CDM updates, and the adoption of new CDMs. OntoBridge showed greater flexibility in integrating new data sources and adapting to CDM updates. It was also more scalable, facilitating the adoption of various CDMs like i2b2, unlike traditional methods reliant on OMOP-specific tools developed by OHDSI. In summary, while traditional ETL provides a structured approach to data integration, OntoBridge offers a more flexible, scalable, and maintenance-efficient alternative.
Key Research Areas for Building and Deploying a Common Data Model for an Intensive Medicine Data Space in Europe and Beyond
Authors: Parra-Calderón Carlos Luis , Rodríguez Mejías Silvia , Colpaert Kirsten , Balzer Felix , Delange Boris , Cuggia Marc , Delamarre Denis , Daniel Christel , Moinat Maxim , van den Brand Jan , van Genderen Michel E. , Fleureng Lucas , Jungh Christian , Álvarez-Romero Celia
Published: August 2024

Key Research Areas (KRAs) were identified to establish a semantic interoperability framework for intensive medicine data in Europe. These include assessing common data model value, ensuring smooth data interoperability, supporting data standardization for efficient dataset use, and defining anonymization requirements to balance data protection and innovation.
Futureproofing Medical Education Through Scenario Analysis: Instrumental Case Study
Authors: Giunti Guido , Doherty Colin P.
Published: August 2024

Developments in technology and climate change, as well as other “megatrends” are having lasting impacts in society and healthcare. A scenario analysis was conducted to explore the impact of megatrends on medical education. Three scenarios were developed for the year 2035, showing varying levels of technological integration and environmental focus. Implications for an updated curricula focus on health inequalities, digital health, and globalization effects.
Beyond Docker: Enhancing vantage6 with Kubernetes for Federated Learning
Authors: Cadavid Héctor , Geng Cunliang
Published: August 2024

Vantage6, a powerful platform for privacy-preserving analysis in the life-sciences domain, employs Docker in its data nodes when performing tasks like federated learning. This tight bond to Docker poses challenges on infrastructure security, efficient computing resources usage, and integration of alternative container technologies. To overcome these challenges, we explored the integration of Kubernetes into vantage6 through a Proof-of-Concept (PoC) approach. The PoC designed and implemented a Kubernetes-based architecture for vantage6, which can be easily deployed on a single machine or a cluster. This PoC serves as the reference to accelerate the adoption of Kubernetes in vantage6.