bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Clinical Data Extraction from Pathology Reports: A Comparative Analysis of Large Language Models
Authors: Park Sunghyeon , Choi Wona , Choi InYoung
Published: August 2024

This study evaluates the efficacy of a small large language model (sLLM) in extracting critical information from free-text pathology reports across multiple centers, addressing the challenges posed by the narrative and complex nature of these documents. Employing three variants of the Llama 2 model, with 7 billion, 13 billion, and 70 billion parameters, the research assesses model performance in both zero-shot and five-shot settings, offering insights into the impact of example-based learning. A specialized information extraction tool utilizing regular expressions for pattern identification serves as the benchmark for evaluating the models’ accuracy. Conducted within a hospital’s internal environment, the study emphasizes the clinical applicability of these findings. The results reveal significant variations in model performance, with the 70 billion parameter model achieving remarkable accuracy in the five-shot scenario, demonstrating the potential of sLLMs in enhancing the efficiency and accuracy of data extraction from pathology reports. The study highlights the importance of example-driven learning and the trade-offs between model size, accuracy, hallucination rates, and processing time. These findings contribute to the ongoing efforts to integrate advanced language models into clinical settings, potentially transforming patient care and biomedical research by mitigating the limitations of manual data extraction processes.
A Conformal Prediction Approach to Enhance Predictive Accuracy and Confidence in Machine Learning Application in Chronic Diseases
Authors: Papangelou Christina , Chatzikonstantinou Thomas , Stamatopoulos Kostas , Chatzidimitriou Anastasia , Minga Evangelia
Published: August 2024

Heterogeneity in chronic malignancies raises an increasing interest for the integration and study of predictive models. This study presents a machine learning model approach to predict outcomes and improve their trustworthiness in multi-factorial diseases with highly heterogeneous outcomes, like Chronic Lymphocytic Leukemia (CLL). We incorporated Conformal Prediction to quantify our models uncertainty, and generate confident personalized prediction outcomes that can be integrated into clinical practice.
From EHR to Machine Learning: A Preliminary Report on an Ingestion Pipeline Based on JSON-LD
Authors: Baroni Giulia Lucrezia , Della Mea Vincenzo , Foresti Gian Luca
Published: August 2024

In this paper, we present the preliminary experiments for the development of an ingestion mechanism to move data from Electronic Health Records to machine learning processes, based on the concept of Linked Data and the JSON-LD format.
Application of Artificial Intelligence in Clinical Practice – Perception of a Multinational Group of Nephrologists
Authors: Silva Eliana , Santos-Araújo Carla , Correia Ricardo , Macário Fernando
Published: August 2024

This study investigates the perception of a multinational group of nephrologists on artificial intelligence (AI) application in clinical practice. A validated on-line survey was performed in March 2024, in 4 continents. The results revealed a prevalent familiarity with AI and machine learning (ML) terms, but traditional tools remained favored for clinical decision support. AI’s future relevance was acknowledged by more than two thirds of the sample but concerns related to the use of this tool in clinical practice were shared, particularly by nephrologists without any previous contact with AI. This reinforces the need for education in this group of health professionals, to allow full adoption of AI in the management of chronic kidney disease (CKD) in the near future.
Accelerating Clinical Text Annotation in Underrepresented Languages: A Case Study on Text De-Identification
Authors: Xu He A. , Loftsson Valentin , Kulynych Bogdan , Kaabachi Bayrem , Raisaro Jean Louis
Published: August 2024

Clinical notes contain valuable information for research and monitoring quality of care. Named Entity Recognition (NER) is the process for identifying relevant pieces of information such as diagnoses, treatments, side effects, etc., and bring them to a more structured form. Although recent advancements in deep learning have facilitated automated recognition, particularly in English, NER can still be challenging due to limited specialized training data. This exacerbated in hospital settings where annotations are costly to obtain without appropriate incentives and often dependent on local specificities. In this work, we study whether this annotation process can be effectively accelerated by combining two practical strategies. First, we convert usually passive annotation tasks into a proactive contest to motivate human annotators in performing a task often considered tedious and time-consuming. Second, we provide pre-annotations for the participants to evaluate how recall and precision of the pre-annotations can boost or deteriorate annotation performance. We applied both strategies to a text de-identification task on French clinical notes and discharge summaries at a large Swiss university hospital. Our results show that proactive contest and average quality pre-annotations can significantly speed up annotation time and increase annotation quality, enabling us to develop a text de-identification model for French clinical notes with high performance (F1 score 0.94).
Integrated Cognitive Ergonomics of the Remote Evaluation of the Grafts with Robotics and Machine Organ Perfusion Technology in Solid Organ Transplantation
Authors: Mammas Constantinos S. , Mamma Adamantia S.
Published: August 2024

Aim: To extend reliability of the integrated Tele-Radiological (TRE) and Tele-Pathological (TPE) evaluation of the Renal Graft (RG) of Prometheus Digital Medical Device (pn 2003016) via integration with Machine Organ Perfusion and Tele-Robotics (Stamoulis Rb) in Organ Transplantation. Material and Methods: A sensitivity-specificity analysis by a simulation of the TRE of RG on 15 MR abdominal images by a radiologist and of the TPE of RG by 26 specialists based on 130 human RG images assessing damages and lesions. Results: The integrated analysis of TRE and TPE of RG showed: Sensitivity=96.7%, Specificity=100% and Accuracy=97.6%. Integration of Machine Organ Perfusion based results pattern recognition and AI programming offers deep learning and improves morbidity-mortality and organ viability prognosis. Conclusion: The TRE integrated with TPE and AI programming of RG machine organ perfusion based results pattern recognition by AI programming and Deep Learning supported virtual benching is feasible and seems more reliable for instant morbidity-mortality and organ viability prognosis in renal transplant decision support and operational planning.
Evaluation of BERT-Based Models on Patient Data from French Social Media
Authors: Le Priol Emma , Talmatkadi Manissa , Schück Stéphane , Texier Nathalie , Burgun Anita
Published: August 2024

With the objective of extracting new knowledge about rare diseases from social media messages, we evaluated three models on a Named Entity Recognition (NER) task, consisting of extracting phenotypes and treatments from social media messages. We trained the three models on a dataset with social media messages about Developmental and Epileptic Encephalopathies and more common diseases. This preliminary study revealed that CamemBERT and CamemBERT-bio exhibit similar performance on social media testimonials, slightly outperforming DrBERT. It also highlighted that their performance was lower on this type of data than on structured health datasets. Limitations, including a narrow focus on NER performance and dataset-specific evaluation, call for further research to fully assess model capabilities on larger and more diverse datasets.
Unveiling Medical Insights: Advanced Topic Extraction from Scientific Articles
Authors: Bitaraf Ehsan , Jafarpour Maryam , Shool Sina , Saboori Amleshi Reza
Published: August 2024

In the ever-evolving landscape of medical research and healthcare, the abundance of scientific articles presents both a treasure trove of knowledge and a daunting challenge. Researchers, clinicians, and data scientists grapple with vast amounts of unstructured information, seeking to extract meaningful insights that can drive advancements in the biomedical domain including, research trends, patient care, drug discovery, and disease understanding. This paper utilizes the topic extraction algorithms on Breast Cancer Research to shed light on the current trends and the path to follow in this field. We utilized TextRank and Large Language Models (LLM) using the TripleA tool to extract topics in the field, analyzing and comparing the results.
Large Language Models for Synthetic Tabular Health Data: A Benchmark Study
Authors: Miletic Marko , Sariyar Murat
Published: August 2024

Synthetic tabular health data plays a crucial role in healthcare research, addressing privacy regulations and the scarcity of publicly available datasets. This is essential for diagnostic and treatment advancements. Among the most promising models are transformer-based Large Language Models (LLMs) and Generative Adversarial Networks (GANs). In this paper, we compare LLM models of the Pythia LLM Scaling Suite with varying model sizes ranging from 14M to 1B, against a reference GAN model (CTGAN). The generated synthetic data are used to train random forest estimators for classification tasks to make predictions on the real-world data. Our findings indicate that as the number of parameters increases, LLM models outperform the reference GAN model. Even the smallest 14M parameter models perform comparably to GANs. Moreover, we observe a positive correlation between the size of the training dataset and model performance. We discuss implications, challenges, and considerations for the real-world usage of LLM models for synthetic tabular data generation.
Multimodal Signal Fusion for Heartbeat Monitoring on eScooters
Authors: Singh Himanshu , Warnecke Joana M. , Picker Alexander , Ganapathy Nagarajan , Deserno Thomas M.
Published: August 2024

Integrating continuous monitoring into everyday objects enables the early detection of diseases. This paper presents a novel approach to heartbeat monitoring on eScooters using multi-modal signal fusion. We explore heartbeat monitoring using electrocardiography (ECG) and photoplethysmography (PPG) and evaluate four signal fusion approaches based on convolutional neural network (CNN) and long short-term memory (LSTM) architectures. We perform an evaluation study using skin-attached ECG electrodes for ground truth generation. The CNN+LSTM late fusion accurately measures the heartbeat for 76.17% of the driving time.
Explaining Deep Learning Models Applied in Histopathology: Current Developments and the Path to Sustainability
Authors: Pesecan Cătălin-Mihai , Stoicu-Tivadar Lăcrămioara
Published: August 2024

The digital pathology landscape is in continuous expansion. The digitalization of slides using WSIs (Whole Slide Images) fueled the capacity of automatic support for diagnostics. The paper presents an overview of the current state of the art methods used in histopathological practice for explaining CNN classification useful for histopathological experts. Following the study we observed that histopathological deep learning models are still underused and that the pathologists do not trust them. Also we need to point out that in order to get a sustainable use of deep learning we need to get the experts to trust the models. In order to do that, they need to understand how the results are generated and how this information correlates with their prior knowledge and for obtaining this they can use the methods highlighted in this study.
Continuing Educational Program in Nursing Informatics for Greek Nurses
Authors: Liaskos Joseph , Anagnostopoulos Sotirios , Zogas Spyros , Mantas John
Published: August 2024

Nurses are required to adapt to a complex and rapidly evolving environment that incorporates new IT technologies. Continuing, lifelong learning, especially in the fast-growing fields of Nursing Informatics, eHealth and related Information and Communication Technologies, is vital to the professionalism of nurses. The study presents the curriculum development and implementation of the educational project entitled “Training of Nurses in Nursing Informatics and Information and Communication Technologies (ICT)” which has been designed and offered to Greek nurses as a continuing – lifelong online educational program by the Health Informatics Laboratory of the National and Kapodistrian University of Athens. The training material was created in three modules and 29 sub-modules, considering the new advancements and recommended competencies, necessary for the nurse, in the fields of Nursing Informatics and ICT. So far, eight training cycle courses have been offered to 1388 nurses, free of charge. Each course integrates synchronous and asynchronous distance learning methods and requires a workload of 70 hours over 10 weeks. The initial evaluation of the course by the participants, concerning its effectiveness, usefulness, and innovation are very encouraging.
What People Know vs. What They Should Know About Laboratory Reference Ranges
Authors: Bailey Dana , MacDonald Leah , Monkman Helen
Published: August 2024

Laboratory test results are increasingly available to health consumers. Almost all test results are accompanied by a reference range to aid in interpretation. This study asked 25 non-healthcare providers to explain the term reference range. The descriptions highlighted four principal themes: health consumers were unsure about their understanding of the term reference range; they equated a reference range with a normal range; few had some degree of awareness of the limitations of reference ranges; and few had limited awareness of the difference between a reference range and clinical cutoff. Further efforts should be made to educate health consumers on the utility and limitations of reference ranges. When providing results to health consumers, laboratories should consider including other means of contextualizing test results in order to support their understanding of potential clinical significance.
Evaluation of End-User Participation in Artificial Intelligence Nursing Projects
Authors: Nowak Alessia , Gubser Rahel , Poncette Akira-Sebastian , Fürstenau Daniel
Published: August 2024

Artificial Intelligence (AI) projects in healthcare, particularly in nursing, currently gain relevance but encounter challenges in user acceptance. Active participation of end-users in the development and implementation of AI can enhance acceptance. This study proposes a scale to measure the degree of end-user participation in AI development and implementation for nursing on the project level, rated by project managers. It employs the qualitative-analytical COARSE method for scale development and evaluation. The instrument includes 11 items across two sub-scales: activities for active participation of end-users and empowerment activities. It highlights the importance of the measurement’s purpose and consequences for interpreting the results of the evaluated degree of end-user participation. The study points to future research opportunities, underscored by the need for psychometric validation, such as reliability and validity.
Enhancing Patient Care: Evaluating Clinical Utility in Measurement Instruments
Authors: Prochaska Eveline , Henke Elisa , Sedlmayr Martin
Published: August 2024

The evaluation of clinical utility is essential for the successful adoption of new technology in clinical practice. An approach to evaluating clinical utility is presented here using the example of digitized measurement instruments.
Tunable Privacy Risk Evaluation of Generative Adversarial Networks
Authors: Kaabachi Bayrem , Briki Farah , Kulynych Bogdan , Despraz Jérémie , Raisaro Jean Louis
Published: August 2024

Generative machine learning models such as Generative Adversarial Networks (GANs) have been shown to be especially successful in generating realistic synthetic data in image and tabular domains. However, it has been shown that such generative models, as well as the generated synthetic data, can reveal information contained in their privacy-sensitive training data, and therefore must be carefully evaluated before being used. The gold standard method through which such privacy leakage can be estimated is simulating membership inference attacks (MIAs), in which an attacker attempts to learn whether a given sample was part of the training data of a generative model. The state-of-the art MIAs against generative models, however, rely on strong assumptions (knowledge of the exact training dataset size), or require a lot of computational power (to retrain many “surrogate” generative models), which make them hard to use in practice. In this work, we propose a technique for evaluating privacy risks in GANs which exploits the outputs of the discriminator part of the standard GAN architecture. We evaluate our attacks in terms of performance in two synthetic image generation applications in radiology and ophthalmology, showing that our technique provides a more complete picture of the threats by performing worst-case privacy risk estimation and by identifying attacks with higher precision than the prior work.
Childhood Cancer Survivorship Passport Challenges in the European Health Data Space
Authors: Gazzarata Roberta ,   PanCareSurPass Consortium, Strübin Michael , Chronaki Catherine , Cangioli Giorgio , Saraceno Davide , Schreier Günter , Beyer Stefan , Trauner Florian , Gredinger Gerald , Ladenstein Ruth , Ae de Beijer Ismay , Cavalca Giacomo , Trinkunas Justas , Cervero Beltran Lucas , Vanautgaerden Mark , Kock-Schoppenhauer Ann-Kristin , Neumann Anke , Muraca Monica , Filbert Anna-Liesa , Haupt Riccardo , Grabow Desiree
Published: August 2024

Innovation in cancer therapy has increased childhood cancer survival rates. However, survivors are still at risk of developing late effects. In the digital transformation of the health sector, the Survivorship Passport (SurPass) can support long-term follow-up care plans. Gaps in seamless connectivity among hospital departments, primary care, combined with the time of health professionals required to collect and fill-in health data in SurPass, are barriers to its adoption in daily clinical practice. The PanCareSurPass (PCSP) project was motivated to address these gaps by a new version of SurPass (v2.0) that supports semi-automatic assembly from organizational Electronic Health Record (EHR) systems of the treatment summary data using HL7 FHIR, to create SurPass, and to link it to regional or national digital health infrastructures in six European countries. In this paper we present the methodology used to develop the SurPass technical implementation strategy with special focus on the European Health Data Space (EHDS). The recently provisionally approved EHDS regulation instruments a digital health data ecosystem with opportunities for cost-effective SurPass implementation across Europe. Moving forward, a European HL7 FHIR SurPass Implementation Guide along with synthetic data sets, and validation tools can enrich the European Electronic Health Record Exchange Format (EEHRxF) with use cases on health & wellness of childhood cancer survivors.
Advancing Healthcare Research Through Live-Data Integration: Implementing IEEE 11073 and FHIR in Clinical Environment
Authors: Hillmer Tobias , Riech Kathrin , Gisch Simon , Bergh Björn , Schreiweis Björn , Ulrich Hannes
Published: August 2024

The efficient direct integration of real-time medical device data is a promising approach to improve patient care enabling a direct and eminent intervention. This study presents a comprehensive approach for integrating real-time medical device data into clinical environments using the HL7® FHIR® standards and IEEE 11073 Service-Oriented Device Connectivity (SDC). The study proposes a conceptual framework and an opensource proof-of-concept implementation for real-time data integration within the Medical Data Integration Center (MeDIC) at UKSH. Key components include a selective recording mechanism to mitigate storage issues and ensure accurate data capture. Our robust network architecture utilizes Kafka brokers for seamless data transfer in isolated networks. The study demonstrates the selective capturing of real-time data within a clinical setting to enable medical device data for a down-stream processing and analysis.
Review of Key Elements in Developing a Common Data Model for Rare Diseases: Identifying Common Success Factors
Authors: Graefe Adam S.L. , Rehburg Filip , Hübner Miriam , Thun Sylvia , Beyan Oya
Published: August 2024

This paper explores key success factors for the development and implementation of a Common Data Model (CDM) for Rare Diseases (RDs) focusing on the European context. Several challenges hinder RD care and research in diagnosis, treatment, and research, including data fragmentation, lack of standardisation, and Interoperability (IOP) issues within healthcare information systems. We identify key issues and recommendations for an RD-CDM, drawing on international guidelines and existing infrastructure, to address organisational, consensus, interoperability, usage, and secondary use challenges. Based on these, we analyse the importance of balancing the scope and IOP of a CDM to cater to the unique requirements of RDs while ensuring effective data exchange and usage across systems. In conclusion, a well-designed RD-CDM can bridge gaps in RD care and research, enhance patient care and facilitate international collaborations.
Modeling Rare Disease Datasets with ART-DECOR
Authors: Schaarschmidt Marco
Published: August 2024

This paper presents ongoing work on the modeling of different datasets using the ART-DECOR modeling tool, with a focus on adherence to the FAIR principles (Findable, Accessible, Interoperable, and Reusable). The successful modeling of the French minimal dataset for rare diseases (Set de donnees minimal des maladies rares (SDM-MR.fr)) should provide inspiration for the development of the German minimal dataset for rare diseases (Minimalbasisdatensatz fur Seltene Erkrankungen (MBDS-SE.de)).
Teaching Artificial Intelligence to Medical Students
Authors: Mihalas Gheorghe Ioan , Boru Casiana , Cotoraci Coralia
Published: August 2024

Among the measures taken by UVVG to modernize the educational process is the pioneering undertaking regarding the introduction of a course on Artificial Intelligence in Medicine (AIM). Such an action has to face several challenges, at three levels, starting from its inclusion in the curricular vision of the university, its positioning in the didactic program as well as the content of the syllabus suitable for medical students. The first part presents the necessity and opportunity of introducing the course in the current context of the rapid growth of AIM applications. The second part refers to the concrete implementation of the introduction of the course in the curriculum, while the last and most developed part is dedicated to the preparation of the syllabus starting from the premises that the field is growing very fast and we should provide the basic knowledge to assure an easy understanding and a smooth assimilation of further developments.
Electronic Prescribing by Medical Students: Descriptive Data and Inferred Clinical Impact
Authors: Chin Paul , Pairman Lorna , Quayle John
Published: August 2024

We analysed data from the electronic medication chart at tertiary healthcare facilities during Nov 2020-Nov 2023 following a reconfiguration of prescribing permissions allowing medical student inpatient prescribing. A total of 262 students prescribed for 2205 patients. The 17,769 prescriptions were spread throughout the 7-day week and were mainly created in the evening (1600-2100) and within 4 hours of patient entry into hospital. The supervising doctors approved 11,434 (64%) prescriptions, which are inferred to have saved 203 hours of doctor time. Allowing medical students to prescribe facilitates clinical workflow. The impact on student education and prescribing errors requires further work.
Unlocking the Power of Real-World Data: A Framework for Sustainable Healthcare
Authors: Pirmani Ashkan , Moreau Yves , Peeters Liesbet M.
Published: August 2024

Real-world data (RWD) has the potential to revolutionize healthcare by offering valuable insights into patient outcomes and treatment efficacy. However, leveraging RWD effectively presents challenges, including its inherent limitations, diverse stakeholders, and insufficient data management pipelines. A proposed framework advocates three essential elements: adherence to FAIR principles (Findable, Accessible, Interoperable, and Reusable), stakeholder engagement and education, and highlighting the need for inclusive, pragmatic federated hybrid pipelines. By employing these strategies, healthcare organizations can overcome obstacles to RWD utilization and foster sustainable progress in patient care.
Network Insights of Dementia Comorbidities Based on Genetic Background
Authors: Hung Fei-Hung , Tung Hsin
Published: August 2024

Dementia is a global public health concern. This study focuses on the genetic factors underlying dementia. We analyzed electronic medical records (EMR) from Taichung Veterans General Hospital, Taiwan, to confirm differences between dementia and non-dementia patients. This work was supported by Taipei Medical University [TMU111-AE1-B45].
Predicting a Risk of Transition to a Higher Level of Care for Home Support Service Recipients
Authors: Fukui Innes Junko , Roudsari Abdul Vahabpour , Courtney Karen L. , Purkis Mary Ellen
Published: August 2024

This longitudinal, non-randomized, retrospective study uses the Kaplan-Meier estimates method and the Cox Proportional Hazard model to assess the risk of home support recipients’ transitioning to a higher level of care after being hospitalized. The Kaplan-Meier survival analysis revealed that 50% of home support recipients were expected to move on to a higher level of care by day 1,374. The Cox Proportional Hazard model indicated that the risk of transitioning to a higher level of care increases by about 2% as a client ages by one year, and by about 10% if there were no emergency room visits in the last 12 months. Also, the risk will decrease by about 13% if an individual is getting more than one hour of home support service per visit on average, compared to those who are receiving less than one hour of home support services per visit. These results will help project long term home support demand and resource planning for home support and the health care system.
Germany’s Approach for the Secondary Use of Federated Real-World Data in the German Portal for Medical Research Data
Authors: Kleinert Philip , Gebhardt Marie , Buckow Karoline , Gruendner Julian , Ganslandt Thomas , Prokosch Hans-Ulrich , Semler Sebastian C.
Published: August 2024

In the light of big data driven clinical research, fair access to real world clinical health data enables evidence to improve patient care. Germany’s healthcare system provides an abundant data resource but unique challenges due to its federated nature, heterogeneity and high data-protection standards. The Medical Informatics Initiative (MII) developed concepts that are being implemented in the German Portal for Medical Research Data (FDPG) to grant access to distributed data-sources across state borders. The portal currently provides access to more than 10 million patient resources containing hundreds of millions of laboratory parameters, diagnostic reports, administered medications, procedures and specimens. Upcoming datasets include among others oncological data, molecular analysis results and microbiological findings. Here, we describe the philosophy, implementation and experience behind the framework: standardized access processes, interoperable fair data, software for in depth feasibility requests, tools to support researchers and hospital stakeholders alike as well as transparency measures to provide data use information for patients. Challenges remain to improve data quality and automatization of technical and organizational processes.
Visualizing Nursing Narratives: An Evaluation of Latent Dirichlet Allocation Topic Modeling for Care Reports
Authors: Danler Martin , Hackl Werner O. , Neururer Sabrina B. , Huber Lukas , Pfeifer Bernhard
Published: August 2024

The increasing volume of unstructured textual data in healthcare, particularly in nursing care reports, presents both challenges and opportunities for enhancing patient care and operational efficiency. This study explores the application of Latent Dirichlet Allocation (LDA) topic modeling to analyze free-text nursing narratives from inpatient stays in three different clinics, aiming to uncover the latent thematic structures within. Utilizing the R programming environment and the visualization tool LDAvis, we identified three main themes: “Patient Well-being,” “Patient Mobility and Care Activities,” and “Treatment and Pain Management,” the latter combining two closely related but initially distinct topics due to their overlapping content. Our findings demonstrate the potential of LDA topic modeling in extracting meaningful insights from nursing narratives, which could inform patient care strategies and healthcare practices. However, the study also highlights significant challenges associated with the method, including the sensitivity to parameter settings, the lack of updates for key software packages, and concerns about reproducibility. These issues highlight the need for meticulous parameter validation and the exploration of alternative text analysis methodologies for future research. By addressing these methodological challenges and emphasizing the importance of comparative method analysis, this study contributes to the advancement of text analytics in healthcare. It opens avenues for further research aimed at developing more robust, efficient, and accessible tools for analyzing free-text data, thereby enhancing the ability of healthcare professionals to use unstructured data to improve decision making and patient outcomes.
Development of a Basal Insulin Titration System to Support Healthcare Providers in Treatment of People with Type 2 Diabetes: Preliminary Results
Authors: Thomsen Camilla H.N. , Hangaard Stine , Kronborg Thomas , Vestergaard Peter , Hejlesen Ole , Jensen Morten H.
Published: August 2024

The study aims to develop a system for insulin titration in type 2 diabetes to assist primary healthcare providers. Usability testing with end-users is ongoing.
Sensor-Based Fuzzy Inference of COVID-19 Transmission Risk in Cruise Ships
Authors: Triantafyllou Georgios , Sovatzidi Georgia , Dimas George , Kalozoumis Panagiotis G. , Drikakis Dimitris , Kokkinakis Ioannis W. , Markakis Ioannis A. , Golna Christina , Iakovidis Dimitris
Published: August 2024

Cruise ships are densely populated ecosystems where infectious diseases can spread rapidly. Hence, early detection of infected individuals and risk assessment (RA) of the disease transmissibility are critical. Recent studies have investigated the long-term assessment of transmission risk on cruise ships; however, short-term approaches are limited by data unavailability. To this end, this work proposes a novel short-term knowledge-based method for RA of disease transmission based on fuzzy rules. These rules are constructed using knowledge elicited from domain experts. In contrast to previous approaches, the proposed method considers data captured by several sensors and the ship information system, according to a recently proposed smart ship design. Evaluation with agent-based simulations confirms the effectiveness of the proposed method across various cases.
Integrated Perspectives on Clinical Decision Support: A Comparative Analysis of Knowledge Management Approaches
Authors: Grob Moritz , Jenders Robert A. , Rappelsberger Andrea , Adlassnig Klaus-Peter
Published: August 2024

We analyze five approaches to knowledge management in clinical decision support (CDS) systems: pattern recognition based on annotated imaging data, mining of stored structured medical data, text mining of published texts, computable knowledge design, and general or specific text corpora for large language models. Each method’s strengths and limitations in automating clinical knowledge management while striving for a zero-error policy are evaluated, offering insights into their roles in enhancing healthcare through intelligent decision support. The study aims to inform decisions in the development of effective, transparent CDS systems in clinical and patient care settings.
TikTok and YouTube Shorts by Autistic Individuals for Increasing Autism Awareness
Authors: Berg Egge Hanna Sofie , Gabarron Elia
Published: August 2024

Introduction: Autistic individuals, parents, organizations, and healthcare systems worldwide are actively sharing content aimed at increasing awareness about autism. This study aims at analyzing the type of contents presented in TikTok and YouTube Shorts videos under the hashtag #actuallyautistic and their potential to increase autism awareness. Methods: A sample of 60 videos were downloaded and analyzed (n=30 from TikTok and n=30 from YouTube Shorts). Video contents were analyzed using both thematic analysis and the AFINN sentiment analysis tool. The understandability and actionability of the videos were assessed with The Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT A/V). Results: The contents of these videos covered five main themes: Stigmatization; Sensory difficulties; Masking; Stimming; and Communication difficulties. No statistically significant differences were found on sentiment expressed on videos from both channels. TikTok videos received significantly more views, comments, and likes than videos on YouTube Shorts. The PEMAT A/V tool showed that there is a high level of understandability, but little reference to actionability. Discussion: Autistic people videos content spread valid and reliable information in hopes of normalizing difficulties and provide hope and comfort to others in similar situations. Conclusions: Social media videos posted by autistic individuals provide accurate portrayals about autism but lack information on actionability. These shared personal stories can help increase public literacy about autism, dispel autism stigmas and emphasize individuality.
DESAM-ForNet Portal: A Novel Infrastructure to Integrate Distributed Information from Practice-Based Research Networks in the German Healthcare System
Authors: Becker Arndt , Schmutz Patrick , Krauss Arthur , Koch Roland , Polanc Andreas , Heinz Leonor , Niemeyer Anna , Thies Christian
Published: August 2024

Currently the German healthcare system does not have a generic structure to answer research questions in primary care through clinical studies. The DESAM-ForNet initiative was founded as an association of German Practice-Based Research Networks (PBRN), to propose an appropriate and feasible solution. Aim is the integration of distributed, consensual information from practices into a single point of contact. To this end, a consensus-based concept for a digital infrastructure was developed in cooperation with all partners involved. Based on a joint requirements analysis the new concept integrates the federal structure of the German health system and the existing research structures.
Impact of Terrorism on the Use of Healthcare Services in Burkina Faso Between 2015 and 2022
Authors: Konate Sitapha , Somda Serge M.A. , Malo Sadouanouan
Published: August 2024

Burkina Faso has been facing a security crisis due to terrorism since 2015. This study aims to assess the impact of the attacks on the use of healthcare services. This is a secondary study on data from the country’s health data warehouse and the ACLED security data warehouse. After a description, generalized additive models were used to assess the impact of attacks on the use of health services. Between January 2015 and December 2022, 2449 kidnap/disappearance attacks, armed attacks, bombings and landmine explosions were perpetrated, causing 4965 deaths. The Sahel region was the most targeted (36.37% of attacks and 50.57% of deaths). Only population density had a significant impact on the use of health services (p<5%). The models were valid. Our study has shown that, despite the persistent insecurity in Burkina Faso, people are resilient and, above all, continue to seek out the most important healthcare services. It is therefore important to work to maintain the supply of these services.
Coordination and Referral Platforms for Children Aged 7 and Under with Suspected Neurodevelopmental Outcome: What Data for Research on Care and Developmental Trajectories?
Authors: Ansoborlo Marie , Gaudichon Valentin , Hazard Margaux , Zopppe Hugo , Defresne Diane , Guinoiseau Katty , Salpetrier Christine , Grammatico-Guillon Leslie , Bonnet-Brilhault Frédérique
Published: August 2024

At the core of the child’s medical, social, and educational pathway, coordination and referral platforms (CRPs) for neurodevelopmental disorders (NDDs) have been gradually deployed in France since 2018 and support the early detection of NDDs in children. The 112 nationwide CRPs do not benefit from a common electronic health record system. Our aim was to propose an HER model for CRP to enable real-life data reuse, optimize care pathway management and conduct pre-screening for research. CRP data were collected (n=34) into an application enriched by a NLP tool extracting standardized scales for NDDs assessments from medical and paramedical professionals. NLP tool evaluation presented a precision of 86.4% and recall of 90.5%. CRP support was provided to 195 children included between 1 September 2022 and 31 August 2023, aged 4 years, with a sex ratio of 2.8, with delays reported in language (75%) and concerned by global developmental delays (16%). Children’s ND phenotype and care pathway description could be automated by a harmonized and structured EHR. While many clinical situations are at an impasse, real-life data-driven evidence is particularly relevant in the context of NDDs, where early intervention plays such a key role in children’s development and prognosis. A harmonized and enriched CRP database could benefit both individual and public health levels with pathway monitoring, intervention proposals and research pre-screenings.
Development of a Framework for Establishing ‘Gold Standard’ Outbreak Data from Submitted SARS-CoV-2 Genome Samples
Authors: Shen Yannan , Steele Russell , Buckeridge David
Published: August 2024

Submitted genomic data for respiratory viruses reflect the emergence and spread of new variants. Although delays in submission limit the utility of these data for prospective surveillance, they may be useful for evaluating other surveillance sources. However, few studies have investigated the use of these data for evaluating aberration detection in surveillance systems. Our study used a Bayesian online change point detection algorithm (BOCP) to detect increases in the number of submitted genome samples as a means of establishing ‘gold standard’ dates of outbreak onset in multiple countries. We compared models using different data transformations and parameter values. BOCP detected change points that were not sensitive to different parameter settings. We also found data transformations were essential prior to change point detection. Our study presents a framework for using global genomic submission data to develop ‘gold standard’ dates about the onset of outbreaks due to new viral variants.
Effects of monaural sound stimulation on subjective visual vertical
Authors: Yokoyama Takako , Takahashi Koji , Kudo Yosuke , Jono Takashi , Johkura Ken
Published: August 2024

BACKGROUND: Sound stimulation can influence electrophysiological vestibular reflexes. However, the effects of sound stimulation on space perception remain unknown. OBJECTIVE: To know the effects of monaural sound stimulation on subjective visual vertical (SVV). METHODS: We measured SVV with and without monaural sound stimulation (105 dB, 500 Hz short tone burst presented at 4.7 Hz) in 50 healthy volunteers (aged 20–77 [mean = 42.7] years). RESULTS: The mean SVV was deviated 0.139° to the left by right monaural sound stimulation and 0.123° to the right by left monaural sound stimulation. SVV changes due to right and left ear stimulations were significantly different (p = 0.019). Sound stimulation resulted in a significant change in SVV on the left side (p = 0.014) in participants aged 50 or younger (mean = 35.6 years) (n = 37). CONCLUSIONS: This study is the first to show the possibility that the monaural sound input deviates the SVV toward the opposite side and is more pronounced for left-ear input. The vestibular-evoked myogenic potential responses may be involved in the mechanism of the contralateral SVV deviation due to sound input.
Mangrove Care Social Action for Conservation and Development Ecotourism in East Kutai
Authors: Heriyati Eny , Haryasakti Anshar
Published: August 2024

The social activities of STIPER East Kutai Marine Science lecturers as a form of Community Service activity is carrying out mangrove conservation activities in the Teluk Lingga area. Conservation is a method and effort to utilize available resources to ensure their sustainability in the future. Community service in the form of planting 6000 mangrove seedlings aims to conserve the Lingga Bay mangrove forest and ecotourism in the East Kutai coastal area. The smooth implementation of this Community Service was thanks to the collaboration of all levels of the East Kutai community with the Ministry of Forestry, the Watershed Forum, the Coastal Area Observer Forum  and regional leadership in East Kutai. The type of mangrove planted is Avicennia sp. Apart from being carried out by lecturers, this 6000 mangrove planting event was also attended by students, East Kutai High School and Vocational School students and the general public in the Teluk Lingga area. The success of mangrove conservation activities can make the Lingga Bay coastal area an ecotourism area for the East Kutai Community.
Career Coaching: Developing a Constructive Mindset and Ethical Conduct in the Realm of Employment from a Young Age
Authors: Anggraini Dewi , Pratiwi Marisya , Rachmawati Rachmawati , Juniarly Amalia
Published: August 2024

The current era in which the world of work is evolving into 4.0 requires businesses to be more competitive; therefore, human resources are essential to this end. Effective employee performance positively influences the company's overall performance. This pertains to the correlation between good attitudes and behaviours in employees, leading to increased engagement in work, heightened motivation, and job satisfaction. Developing a positive attitude and behaviour in individuals from a young age is crucial. Students, as future human resources entering the workforce, should be equipped with abilities and character that align with the company's requirements. Through this career coaching programme, students are expected to develop insight, knowledge, and positive attitudes and behaviours related to the workplace from an early age by implementing them in an educational environment. 63 persons from the 1st-semester students of the Psychology Study Programme at Sriwijaya University participated in this activity. This service activity is aligned with the industrial and organisational psychology courses that participants will complete in semester 3. The outcomes of this activity may be determined through pre and post-evaluation, revealing that 95.3% of participants grasp the career coaching activity effectively.
Marketing Analysis Of Cayenne Pepper In Paya Benua Village, West Mendo District, Bangka Regency
Authors: Oktari Venny , Sitorus Rostiar , Astuti Rufti Puji
Published: August 2024

The research will be conducted in June 2023 in Paya Benua Village, West Mendo District, Bangka Regency. The purpose of this study is to analyze marketing channels, understand the marketing functions carried out by marketing agencies, calculate marketing profits, analyze marketing efficiency by calculating marketing margins, determine farmer's share value, and measure cayenne pepper marketing efficiency in Payacontinent Village, West Mendo District, Bangka Regency. The research method used is a survey method with probability sampling techniques, where 30 farmers are randomly selected as samples. The sample of traders was determined using the Snowball Sampling method, with the results of 6 middlemen and 9 retailers. The results showed that the marketing function performed by farmers and traders involves buying, selling, transporting, sorting & grading, risk bearing, and market information. The marketing of cayenne pepper in Payacontinent Village uses three marketing channels: Channel I (Farmers-Middlemen-Consumers),   Channel   II   (Farmers-Retailers-Consumers),   and   Channel   III (Farmers-Middlemen-Traders-Retailers-Consumers).   Marketing   profits   obtained   from   Channel   I amounted to Rp2,517/kg, Channel II amounted to Rp7,632/kg, and Channel III amounted to Rp8,572/kg. Marketing efficiency on all channels is classified as efficient, with values of 13.61%, 8.95%, and 13.82%, respectively. Of the three channels, Channel II is considered the most efficient.
Techniques For Harvesting Forest Honey (Apis dorsata) In Pelawan Forest, Central Bangka District, Indonesia
Authors: Evahelda Evahelda , Astuti Rufti Puji , Ramdani Dian Fery , Dioca Elakenia Novsa , Wirani Nabila Ayu
Published: August 2024

Forest honey harvest and post-harvest activities in Namang Village still apply traditional methods, namely based on local wisdom carried out by the local community. The research was conducted with the aim of describing harvest and post-harvest activities of forest honey in the Pelawan Forest Biodiversity Park, Namang Village, Central Bangka Regency. The research was carried out in the Pelawan Forest Biodiversity Park, Namang Village, Central Bangka Regency using the case study method. The research location was determined using purposive sampling and respondents were interviewed using a questionnaire. Based on the research results, it is known that the forest honey harvesting activities carried out in Namang Village include characteristics of ready-to-harvest honey, scavenging using a pusung, and nest cutting/harvesting carried out using the circumcision technique. Next, post-harvest activities are carried out which consist of transportation using ropes, jerry cans and motorized vehicles, pressing activities carried out manually, filtering carried out using traditional techniques, and packaging stages.
Pelatihan Public Speaking Bagi Kader PKK Kelurahan Bendungan Hilir Jakarta Sebagai Tenaga Penyuluhan Penanganan Stunting
Authors: Sungkono Nono , Pasaribu Mukka , Santoso Prasetya Yoga , Dewi Yuni Retna , Ridwan Widyarini , Hamsinah Hamsinah
Published: August 2024

Community service activities have the aim of empowering the community through public speaking training for PKK cadres in the Bendungan Hilir subdistrict of Jakarta. Providing information to the public: how big is the role of public speaking in outreach to the community This training activity was carried out at RPTRA Taman Guntur Jl. Danau Bratan No. 3, RT.10/RW 3, Bendungan Hilir, Tanah Abang sub-district, Central Jakarta City, a special area of the capital city of Jakarta. The residents who attended were mostly active PKK cadres. The method used in this activity is in the form of lectures and public speaking training for outreach to the community. Residents are also taught how to be ethical and prepare for public speaking. Public speaking training for PKK cadres at Bendungan Hilir Jakarta City can be an effective tool in dealing with stunting. Public speaking skills can be used to increase public awareness, disseminate accurate information, and encourage behavioral changes related to diet and child nutrition. With careful preparation, a clear structure, effective use of language, and frequent practice, PKK cadres can improve their public speaking skills and become more reliable speakers in their efforts to deal with stunting.