bluuebunny/crossref_metadata_2025 huggingface dataset:
How Data Infrastructure Deals with Bias Problems in Medical Imaging
Authors: Li Feifei , Kutafina Ekaterina , Schoneck Mirjam , Caldeira Liliana Lourenco , Beyan Oya
Published: August 2024

The paper discusses biases in medical imaging analysis, particularly focusing on the challenges posed by the development of machine learning algorithms and generative models. It introduces a taxonomy of bias problems and addresses them through a data infrastructure initiative: the PADME (Platform for Analytics and Distributed Machine-Learning for Enterprises), which is a part of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) project. The PADME facilitates the structuring and sharing of health data while ensuring privacy and adherence to FAIR principles. The paper presents experimental results that show that generative methods can be effective in data augmentation. Complying with PADME infrastructure, this work proposes a solution framework to deal with bias in the different data stations and preserve privacy when transferring images. It highlights the importance of standardized data infrastructure in mitigating biases and promoting FAIR, reusable, and privacy-preserving research environments in healthcare.
Comparing a Large Language Model with Previous Deep Learning Models on Named Entity Recognition of Adverse Drug Events
Authors: Tiffet Théophile , Pikaar Alexis , Trombert-Paviot Béatrice , Jaulent Marie-Christine , Bousquet Cédric
Published: August 2024

The ability to fine-tune pre-trained deep learning models to learn how to process a downstream task using a large training set allow to significantly improve performances of named entity recognition. Large language models are recent models based on the Transformers architecture that may be conditioned on a new task with in-context learning, by providing a series of instructions or prompt. These models only require few examples and such approach is defined as few shot learning. Our objective was to compare performances of named entity recognition of adverse drug events between state of the art deep learning models fine-tuned on Pubmed abstracts and a large language model using few-shot learning. Hussain et al’s state of the art model (PMID: 34422092) significantly outperformed the ChatGPT-3.5 model (F1-Score: 97.6% vs 86.0%). Few-shot learning is a convenient way to perform named entity recognition when training examples are rare, but performances are still inferior to those of a deep learning model fine-tuned with several training examples. Perspectives are to evaluate few-shot prompting with GPT-4 and perform fine-tuning on GPT-3.5.
How Trueness of Clinical Decision Support Systems Based on Machine Learning Is Assessed?
Authors: Poiron Alex , Cabon Sandie , Cuggia Marc
Published: August 2024

The application of machine learning algorithms in clinical decision support systems (CDSS) holds great promise for advancing patient care, yet practical implementation faces significant evaluation challenges. Through a scoping review, we investigate the common definitions of ground truth to collect clinically relevant reference values, as well as the typical metrics and combinations employed for assessing trueness. Our analysis reveals that ground truth definition is mostly not in accordance with the standard ISO expectation and that used combination of metrics does not usually cover all aspects of CDSS trueness, particularly neglecting the negative class perspective.
Stratification of Heart Sounds Morphology Through Unsupervised Learning
Authors: Giordano Noemi , Bolognini Irene , Knaflitz Marco , Rosati Samanta , Balestra Gabriella
Published: August 2024

The use of heart sounds for the assessment of the hemodynamic condition of the heart in telemonitoring applications is object of wide research at date. Many different approaches have been tried out for the analysis of the first (S1) and second (S2) heart sounds, but their morphological interpretation is still to be explored: in fact, the sound morphology is not unique and this impact the separability of the heart sounds components with methods based on envelopes or model optimization. In this study, we propose a method to stratify S1 and S2 according to their morphology to explore their diversity and increase their morphological interpretability. The method we propose is based on unsupervised learning, which we obtain using the cascade of four Self-Organizing Maps (SOMs) of decreasing dimensions. When tested on a publicly available heart sounds dataset, the proposed clustering approach proved to be robust and consistent, with over 80% of the heartbeats of the same patient being clustered together. The identified heart sounds templates highlight differences in the time and energy domains which may open to new directions of analysis in the future.
A Comprehensive Natural Language Processing Pipeline for the Chronic Lupus Disease
Authors: Lilli Livia , Bosello Silvia Laura , Antenucci Laura , Patarnello Stefano , Ortolan Augusta , Lenkowicz Jacopo , Gorini Marco , Castellino Gabriella , Cesario Alfredo , D’Agostino Maria Antonietta , Masciocchi Carlotta
Published: August 2024

Electronic Health Records (EHRs) contain a wealth of unstructured patient data, making it challenging for physicians to do informed decisions. In this paper, we introduce a Natural Language Processing (NLP) approach for the extraction of therapies, diagnosis, and symptoms from ambulatory EHRs of patients with chronic Lupus disease. We aim to demonstrate the effort of a comprehensive pipeline where a rule-based system is combined with text segmentation, transformer-based topic analysis and clinical ontology, in order to enhance text preprocessing and automate rules’ identification. Our approach is applied on a sub-cohort of 56 patients, with a total of 750 EHRs written in Italian language, achieving an Accuracy and an F-score over 97% and 90% respectively, in the three extracted domains. This work has the potential to be integrated with EHR systems to automate information extraction, minimizing the human intervention, and providing personalized digital solutions in the chronic Lupus disease domain.
Assessment of Valance Emotional State Using EEG-EDA Coupling and Explainable Classifiers
Authors: Banik Sourabh , Kumar Himanshu , Ganapathy Nagarajan , Swaminathan Ramakrishnan
Published: August 2024

Emotion influences human life and impacts daily life activities. During emotional processes, physiological signals interact with each other instead of functioning separately. Although unimodal and multimodal approaches have been explored for emotion classification, there is a lack of inclusion of central and peripheral nervous system signal interaction-based approaches. In this study, an attempt has been made to characterize valance emotional states using Electroencephalogram (EEG)- Electrodermal activity (EDA) based coupling. For this, multimodal signals are obtained from the publicly available DEAP database (n=32 subjects). The EEG signals are decomposed into θ, α, β, and bands and EDA signals are decomposed into phasic and tonic components. Then two EEG, three EDA, and two EEG-EDA coupling-based features are extracted and applied to three classifiers namely Random Forest (RF), Linear discriminant analysis, and Adaptive boosting. In addition, SHAP analysis is performed to explain classifiers’ performance with respect to features. The result shows that the proposed approach is able to classify valence emotional states. The feature combination of EEG, EDA, and EEG-EDA coupling-based features with an RF classifier performs best with an F1-score of 68.21%. SHAP analysis in frontal electrodes with γ band obtained better discrimination among different valance states. This study underscores the significance of the coupling studies of EEG with EDA in classifying emotion. Therefore, the proposed approach can be extended to emotional state assessment in clinical settings.
Detection of Pharmaceutical Personal Protective Equipment in Video Stream
Authors: Leontiev Michael , Zhilikov Danil , Klimov Lenar , Chertan Vyacheslav , Bobrov Daniel , Lobanov Dmitry , Vologdin Vasilii , Maslov Vladislav , Balabaeva Ksenia
Published: August 2024

Pharmaceutical manufacturing is a complex process, where each stage requires a high level of safety and sterility. Personal Protective Equipment (PPE) is used for this purpose. Despite all the measures of control, human factor (improper PPE wearing) causes numerous losses for human health and material property. This research proposes solid computer vision system for ensuring safety in pharmaceutical laboratories. For this we have tested wide range of state-of-the-art object detection methods. Composing previously obtained results in this sphere with our own approach to this problem, we have reached the high accuracy (mAP@0.5) ranging from 0.77 up to 0.98 in detecting all the elements of common set of PPE used in pharmaceutical laboratories. Our system is the step towards safe medicine producing.
Route Planning for Intra-Hospital Patient Transportation Using Metaheuristics and Mixed Integer Linear Programming
Authors: Karthikeyan Saran , Potluri Sasanka
Published: August 2024

Healthcare processes are complex and involve uncertainties to influence the service quality and health of patients. Patient transportation takes place between the hospitals or between the departments within the hospital (i.e., Inter- or Intra-Hospital Transportation respectively). The focus of our paper is route planning for transporting patients within the hospital. The route planning task is complex due to multiple factors such as regulations, fairness considerations (i.e., balanced workload amongst transporters), and other dynamic factors (i.e., transport delays, wait times). Transporters perform the physical transportation of patients within the hospital. In principle, each job allocation respects the transition time between the subsequent jobs. The primary objective was to determine the feasible number of transporters, and then generate the route plan for all determined transporters by distributing all transport jobs (i.e., from retrospective data) within each shift. Secondary objectives are to minimize the sum of total travel time and sum of total idle time of all transporters and minimize the deviations in total travel time amongst transporters. Our method used multi-staged Local Search Metaheuristics to attain the primary objective. Metaheuristics incorporate Mixed Integer Linear Programming to allocate fairly the transport jobs by formulating optimization constraints with bounds for satisfying the secondary objectives. The obtained results using formulated optimization constraints represent better efficacy in multi-objective route planning of Intra-Hospital Transportation of patients.
AI4Work Project: Human-Centric Digital Twin Approaches to Trustworthy AI and Robotics for Improved Working Conditions in Healthcare and Education Sectors
Authors: Maglogiannis Ilias , Trastelis Filimon , Kalogeropoulos Michael , Khan Arsalan , Gallos Parisis , Menychtas Andreas , Panagopoulos Christos , Papachristou Petros , Islam Najmul , Wolff Annika , Soares Salviano , Hansen Scott , Scholze Sebastian
Published: August 2024

AI and robotics aim to transform workplace landscapes in a several sectors such as manufacturing, logistics, healthcare, construction, agriculture, and education. Central to this evolution is the innovative use of Digital Twin technology, which creates real-time updated virtual replicas of physical systems and entities. This technology is especially transformative in healthcare and education, promising customized and efficient experiences for all involved. This paper outlines the AI4Work project’s approach to leveraging Digital Twin Technology to improve work environments in these sectors. The goal of AI4Work is to formulate a workplace where AI and robots seamlessly collaborate with humans, while explores how to best share tasks between humans and machines in six different domains. For healthcare, AI4Work will explore how Digital Twin technology can assist occupational doctors and psychologists in monitoring the physical and mental health of hospital personnel in order to predict burnout symptoms and to create a sustainable working environment. In education, AI4Work will investigate how to uphold the mental health of both educators and students while fostering a more supportive and enduring educational setting.
Implementation of an Atrioventricular Valve Intervention Registry: Comparative Study of REDCap vs. CDR-Based openEHR Registry
Authors: Kinast Benjamin , Rohde Henrik , Anywar Michael , Bronsch Tobias , Voran Jakob , Kreidl Felix , Frank Derk , Schreiweis Björn
Published: August 2024

This comparative study examines the transition from isolated registries to a consolidated data-centric approach at University Hospital Schleswig-Holstein, focusing on migrating the Atrioventricular Valve Intervention Registry (AVIR) from REDCap to a Medical Data Integration Center based openEHR registry. Through qualitative analysis, we identify key disparities and strategic decisions guiding this transition. While REDCap has historical utility, its limitations in automated data integration and traceability highlight the advantages of a data-centric approach, which include streamlined data (integration) management at a single-point-of-truth based on e.g., centralized consent management. Our findings lay the groundwork for the AVIR project and a proof-of-concept data-centric registry, reflecting a broader industry trend towards data-centric healthcare initiatives.
Usability Checklists for Health Technology: Case Study and Experts’ Opinions
Authors: Marcilly Romaric , Monkman Helen , Quindroit Paul , David Annaëlle , Lesselroth Blake
Published: August 2024

Application of usability evaluations throughout the health technology lifecycle is necessary to improve the efficiency, safety, and effectiveness of health service delivery. Unfortunately, technology vendors and healthcare organizations may not have funding, time or expertise to conduct usability studies. In this paper, we describe how usability checklists can potentially fill this gap. First, we introduce a case study using a checklist to identify usability issues with a primary care dashboard. Then we provide an expert summary of the strengths and limitations of usability checklists. Findings suggest that checklists are efficient to identify important usability issues. They can be used effectively by project team members – including clinicians – without formal usability training. However, checklists should complement rather than replace usability evaluations with representative users.
Adapting Gamification Strategies According to Targeted Health Behavior Change: Findings from a Focus Group
Authors: Ehrler Frederic , Blondon Katherine
Published: August 2024

In healthcare, improving lifestyle behaviors like medication adherence remains challenging, with an impact on treatment success and patient outcomes. Mobile health (mHealth) applications offer promising solutions by leveraging smartphone ubiquity and digital accessibility. However, sustaining user engagement and behavior change presents obstacles. This study explores the specificities of gamification—integrating game-like elements into non-game contexts—to improve mHealth applications for 3 health behaviors. During a focus group, 15 participants discussed the potential of 17 gamification strategies to improve medication adherence, physical activity, and diet within mHealth applications. Individual choices varied widely, with preferences influenced by targeted behaviors. Physical activity focused on self-monitoring and challenges, while dieting emphasized cooperation. Simulation models were particularly intriguing, projecting long-term benefits of behavior changes. Future research should explore personalized design options and consider individual characteristics. Iterative design cycles with end-users are crucial for refining concepts and sustaining user engagement. Overall, gamification presents an innovative avenue for empowering patients and transforming healthcare delivery.
Developing a Help Desk Service for Enhanced Coordination in Health Informatics Projects: A Sharepoint and Power Automate Approach
Authors: Pournik Omid , Ahmad Bilal , Gour Shramika , Peake Ashley , Tong Chao , Churm James , Arvanitis Theodoros N.
Published: August 2024

Healthcare projects necessitate effective collaboration between clinical and technical partners, particularly during pivotal phases like lab testing and piloting. However, challenges in coordination often impede seamless collaboration, leading to inefficiencies and delays. This paper presents a comprehensive approach to developing a help desk service tailored for CAREPATH projects, leveraging SharePoint services and Power Automate. The solution aims to bridge communication gaps, foster collaboration, and enhance coordination among clinical and technical partners. Through iterative development and testing, we refined the system based on stakeholder feedback, resulting in streamlined workflows and improved document management. During the lab testing phase, the help desk system demonstrated significant improvements in resolution duration, communication efficiency, and success solution rates. Stakeholder feedback highlighted enhanced collaboration and improved access to project documentation. With successful testing, the help desk is poised for implementation in subsequent phases, promising further enhancements in patient engagement, technology integration, and scalability. These findings underscore the critical role of help desks in healthcare ICT projects, offering a transformative approach to project management and stakeholder collaboration. Future directions include enhancing patient engagement, leveraging advanced technologies, and conducting longitudinal studies to evaluate long-term impact. Embracing these directions will drive positive change, delivering better outcomes for patients and caregivers in healthcare ICT projects.
Providing Provenance in eHealth
Authors: Llorente Silvia , Delgado Jaime , Naro Daniel , Fotos Nikolaos
Published: August 2024

eHealth data can be generated by different health professionals. Documenting and storing the provenance of medical data will help in providing patients and medical institutions with trustable and traceable information regarding medical procedures and their results. The use of a modular architecture like HIPAMS allows covering different eHealth information representation formats as well as providing protection mechanisms and access control.
Federated Electronic Health Records Using a Blockchain
Authors: Chelsom John , Marco-Ruiz Luis , Skeidsvoll Solvang Øivind , Cassidy Sonja , Lintvedt Ove , Granja Conceição , Solvoll Terje
Published: August 2024

The Valkyrie project aims to develop a demonstration Federated Electronic Health Record for the use of mental health practitioners in Norway. Information for the record is drawn from existing records in Source Systems operating across primary and secondary care. Recording of information in any such system, in response to a healthcare event, triggers the generation of an Encrypted Token, containing summary metadata about the event, clinical coding indicating its clinical context and a locator that can be used to retrieve the full record of the event from the original Source System. The Valkyrie architecture consists of a number of interlinked Security Domains, each with its own private and public keys, through which the Encrypted Tokens are passed. Each Security Domain performs a specific function on a set of Tokens and only has access to the information within each Token that is necessary to perform that function. This paper describes the structure of the Encrypted Token, the function of each Security Domain and the orchestration of the flow of Tokens through the Domains. Together this allows a user to run a Valkyrie Session, in which they can view the content of a patient record, where all content has been drawn in real-time from heterogenous Source Systems (ISO13606- and openEHR-based) and is destroyed when the session terminates.
A Comparative Study of Access Analysis Service Utilization on Japanese Medical Institutions’ Websites with GDPR-Compliant Cases
Authors: Seki Tomohisa , Kawazoe Yoshimasa , Ohe Kazuhiko
Published: August 2024

The browsing history of a medical institution’s website can potentially reveal or identify information about the health condition of the website visitor through browser cookies and fingerprints. In Japan, although the Personal Information Protection Law was revised in April 2022, the use of access analysis services to collect browsing history on medical institution websites has not been investigated. Therefore, this study investigates the actual usage of access analysis services on Japanese medical institution websites and compares it with the current situation in France, which follows the General Data Protection Regulation. The results revealed that the larger the size of the hospital, the higher the percentage of adoption of access analytics services in Japan. However, the implementation of a system for obtaining consent for the use of access analysis in Japan was found to be poor compared to that of French medical institutions. While access analysis tools are used in the websites of several medical institutions in Japan, the implementation of the process of obtaining consent to acquire browsing history is poor.
Methods and Algorithms of Ensuring Data Privacy in AI-Based Healthcare Systems and Technologies
Authors: Jeelani Omar Farshad , Njie Makaire , Korzhuk Viktoriia
Published: August 2024

This project seeks to devise novel algorithms and techniques leveraged in healthcare to guarantee data privacy in AI-powered systems. To bolster its credibility, the study review presents various modern approaches and technologies used to preserve data privacy of healthcare data. The project conducted an empirical study of the current development in healthcare regarding AI privacy protection to compile a steadfast literature on the subject.
Securing Reproducibility and Accountability in Distributed Healthcare Analytics: A Framework Based on Blockchain and Cryptography
Authors: Nucciarelli Leonardo , Gottardelli Benedetta , Gatta Roberto , Damiani Andrea
Published: August 2024

The results and details of the clinical studies and research must be securely stored to ensure reliability, accountability, and prevent malicious misuse. To accomplish this, a secure method for storing metadata and study results is crucial. Also, a mechanism to ensure accountability for both data owners and researchers is needed. In this way, data owners and the scientific community can rely on and verify results and methods presented by researchers, while researchers can check the validity of the analyzed data and have proof of authorship for their work. A modular framework is presented in this paper, which utilizes blockchain and cryptography to store study results and metadata, along with proof of accountability. The framework has been tested within a privacy-preserving distributed analytics infrastructure.
Developing a Sustainable Shared Child’s Health Record in Low Resources Setting, Lao PDR
Authors: Sorsavanh Thepphouthone , Mori Yukiko , Yamamoto Goshiro , Liu Chang , Kuroda Tomohiro
Published: August 2024

Lao PDR’s Ministry of Health has problem with fragmented data, lack of standardization, and inability to interoperate hinder child health data collection. Electronic health information exchange (HIE) could improve data quality and shared child health records (SCHR). In this study we developed the SCHR by interoperating with existing healthcare systems. The FHIR IPS standards were customized based on data elements. 14 experts from the technical working group (TWG) had determined the minimum dataset for data transactions and system testing was analyzed on the HAPI FHIR server. Used FHIR IPS standards for interoperability within Client Registry (CR-DHIS2), Electronic Immunization Record (EIR-DHIS2) and Electronic Medical Record (EMR) works well for data transactions. The initial result of the SCHR could be able to provide a real target child and to monitor full vaccination. However, prior to the actual deployment of the system, it is necessary to validate it and guarantee the confidentiality and safety of the data.
Privacy-Preserving Linkage of Distributed Pseudonymised Datasets in a Virtual European Rare Disease Platform
Authors: Hayn Dieter , Sandner Emanuel , Vengadeswaran Abishaa , Tãtaru Elena-Alexandra , Wilkinson Mark , Hanauer Marc , Kreiner Karl , Schreier Guenter
Published: August 2024

Secondary use of data for research purposes is especially important in rare diseases (RD), since, per definition, data are sparse. The European Joint Programme on Rare Diseases (EJP RD) aims at developing an RD infrastructure which supports the secondary use of data. Significant amounts of RD data are a) distributed and b) available only in pseudonymised format. Privacy-Preserving Record Linkage (PPRL) concerns the linking of such distributed datasets without disclosing the participant’s identities. We present a concept for linking a PPRL Service to the EJP RD Virtual Platform (VP). Level 1 (resource discovery) connection is provided by running an FDP within the PPRL Service. On Level 2 (data discoverability), the PPRL Service can represent both, an individual and a catalog endpoint. Our solution can count patients in PPRL-supporting resources, count duplicates only once, and count only patients registered to multiple resources. Currently, we are preparing the deployment within the EJP RD VP.
‘SNOMEDizing’ Questionnaires for Standardizing Stroke Registry Data
Authors: Riedel Andrea , Schulz Stefan , Martínez Costa Catalina
Published: August 2024

International interoperability of healthcare and research data requires a commitment to standards. To this end, SNOMED CT was evaluated for representing questionnaire items of the European Registry of Stroke Care Quality using a complex annotation protocol. The agreement between validators and annotators was 72.4%. At least 64% of the information could be represented by using SNOMED CT only, including complex post-coordinations. 9% of the information would require an information model, and 14% the addition of new content to SNOMED CT. Next steps will be the creation of an annotation guideline for questionnaires, a specific reference set, and the combination of both with an information model such as HL7 FHIR.
Knowledge Base Prototype Creating with Using Interdisciplinary Metathesaurus
Authors: Astanin Pavel A. , Ronzhin Lev V. , Borisov Aleksandr A. , Usova Anastasia V. , Potapova Irina I. , Budykina Anna V.
Published: August 2024

This article presents our experience in development an ontological model can be used in clinical decision support systems (CDSS) creating. We have used the largest international biomedical terminological metathesaurus the Unified Medical Language System (UMLS) as the basis of our model. This metathesaurus has been adapted into Russian using an automated hybrid translation system with expert control. The product we have created was named as the National Unified Terminological System (NUTS). We have added more than 33 million scientific and clinical relationships between NUTS terms, extracted from the texts of scientific articles and electronic health records. We have also computed weights for each relationship, standardized their values and created symptom checker in preliminary diagnostics based on this. We expect, that the NUTS allow solving task of named entity recognition (NER) and increasing terms interoperability in different CDSS.
Development of Unified Terminological System Based on UMLS: Experience and Perspectives
Authors: Rauzina Svetlana E. , Ronzhin Lev V. , Astanin Pavel A. , Yadgarova Polina A. , Nikolaidi Elena N. , Zarubina Tatyana V.
Published: August 2024

This article presents experience in construction the National Unified Terminological System (NUTS) with an ontological structure based on international Unified Medical Language System (UMLS). UMLS has been adapted and enriched with formulations from national directories, relationships, extracted from the texts of scientific articles and electronic health records, and weight coefficients.
Mixed Reality in Medical Education - Introduction of a Practical Course Module
Authors: Brenner Alexander , Warnecke Yannik , Fujarski Michael , Varghese Julian
Published: August 2024

With increasing digitalization in healthcare, there is growing potential for the use of Mixed Reality (MR) in medicine. In response to this, we developed a course to provide students with hands-on experience in MR and familiarize them with the technology. Utilizing the HoloLens 2, we designed an application enabling multiple users to interact with virtual objects overlaid on the real world and shared across devices. We used 3D heart anatomy models to let students discuss cardiovascular diseases together. We conducted a preliminary evaluation of our prototype, involving ten participants. The first test indicated a positive reception of the course, with students expressing keen interest in MR. With this work we share our experience to contribute to the field of MR applications.
Undergraduate Degree in Medical Data Science: A Conceptual Framework
Authors: Agha-Mir-Salim Louis , Michl Susanne , Balzer Felix
Published: August 2024

The undergraduate degree program in medical data science aims to train future data scientists with a medical lens to tackle healthcare challenges using a data-driven approach. The program is a collaborative effort within the Berlin University Alliance, addressing the lack of healthcare-focused data science education in Berlin and Germany. The curriculum covers mathematics, informatics, medical informatics, and medicine, featuring diverse didactic formats. Graduates will be equipped to lead data science and digital transformation projects in healthcare.
Integrating FHIR and UMLS in an Intelligent Tutoring System
Authors: Frey Nicolas , Haffer Nina , Vogelsang Lennart , Sass Julian , Thun Sylvia , Balzer Felix , Landgraf Philipp
Published: August 2024

Our novel Intelligent Tutoring System (ITS) architecture integrates HL7 Fast Healthcare Interoperability Resources (FHIR) for data exchange and Unified Medical Language System (UMLS) codes for content mapping.
SIGEPS: A System for Managing the Health Internship Program
Authors: Leandro Gabrielle dos Santos , Lima Guilherme Carvalho dos Reis , Maciel Rosemeri Aparecida , Lazarini Flaviane Mello
Published: August 2024

The healthcare sector’s demand for a skilled workforce necessitates effective management of internship and residency programs. This study introduces the System for Managing the Health Internship Program (SIGEPS), aimed at addressing internship and residency management challenges in Joinville’s municipal health network. The method of this study focused on developing a computational solution with applied and exploratory objectives. The research adopted a User-Centered Development approach, involving seven stages from scope determination to system implementation. Key stages included selecting Google’s free technological tools, developing forms for information input, integrating these into a Google Sheets spreadsheet, and creating a website for schedule display. The primary result was the successful development of SIGEPS, which automated the process from initial requests to scheduling and analysis of healthcare institutions and displayed schedules online. The system offers several advantages over previous models, such as concurrent scheduling by multiple employees, process automation, enhanced information transparency, and improved efficiency with significant time savings. Future enhancements include integrating decision support systems and enabling data analysis reports through dashboards to further reduce scheduling efforts and enhance professional efficiency. This study demonstrates the feasibility of developing and implementing a free computational solution using the Google platform for managing internships and residencies in the healthcare sector.
Sustainability in Participatory Health: Applying WHO’s Operational Framework
Authors: Petersen Carolyn , Lopez Campos Guillermo
Published: August 2024

The World Health Organization’s Operational Framework for Climate Resilient and Low Carbon Health Systems outlines 10 broad components for the development and maintenance of health systems that respond effectively to climate-related challenges. This work analyses the WHO framework from a participatory health informatics perspective and identifies specific initiatives involving non-professionals that align with the framework. Citizen and patient participation in the development of climate-resilient health systems is not only possible but also necessary to maximize climate resiliency at the community and global levels.
Initiating a One Digital Health Unified Terminology (ODH-UT) to Facilitate Community Expansion
Authors: Bauberg Helit , Nehama Dana , Blinder Constantine , Davidman Roy , Cohen Eliyahu , Tamburis Oscar , Darmoni Stefan J. , Grosjean Julien , Benis Arriel
Published: August 2024

One Digital Health (ODH) merges the Digital Health and One Health approaches to create a comprehensive framework for future health ecosystems. In this rapidly evolving field, a standardized vocabulary is not just a convenience, but a necessity to ensure efficient communication. This research proposes the development of a “One Digital Health-Unified Terminology” (ODH-UT) to facilitate communication among researchers and practitioners in Digital Health and One Health, addressing this crucial need.
Intensive Care Quality Indicators Dashboard Using Observational Medical Outcomes Partnership Common Data Model
Authors: Delange Boris , Bouzille Guillaume , Popoff Benjamin , Pierre-Jean Morgane , Maamar Adel , Cuggia Marc
Published: August 2024

This paper presents the development of a visualization dashboard for quality indicators in intensive care units (ICUs), using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The dashboard enables the user to visualize quality indicator data using histograms, pie charts and tables. Our project uses the OMOP CDM, ensuring a seamless implementation of our dashboard across various hospitals. Future directions for our research include expanding the dashboard to incorporate additional quality indicators and evaluating clinicians’ feedback on its effectiveness.
Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients
Authors: Hügel Jonas , Schäfer Donata A. , Schneider Jan J. , Tian Jiazi , Estiri Hossein , Koch Raphael , Overbeck Tobias R. , Sax Ulrich
Published: August 2024

This paper presents a comprehensive workflow for integrating revolving events into the transitive sequential pattern mining (tSPM+) algorithm and Machine Learning for Health Outcomes (MLHO) framework, emphasizing best practices and pitfalls in its application. We emphasize feature engineering and visualization techniques, demonstrating their efficacy in capturing temporal relationships. Applied to an EGFR lung cancer cohort, our approach showcases reliable temporal insights even in a small dataset. This work highlights the importance of temporal nuances in healthcare data analysis, paving the way for improved disease understanding and patient care.
Development of a CNN for Adult Brain Tumour Characterisation: Implications and Future Directions for Transfer Learning
Authors: Mukherjee Teesta , Gour Shramika , Abbasi Saadullah Farooq , Pournik Omid , Arvanitis Theodoros N.
Published: August 2024

Brain tumours are the most commonly occurring solid tumours in children, albeit with lower incidence rates compared to adults. However, their inherent heterogeneity, ethical considerations regarding paediatric patients, and difficulty in long-term follow-up make it challenging to gather large homogenous datasets for analysis. This study focuses on the development of a Convolutional Neural Network (CNN) for brain tumour characterisation using the adult BraTS 2020 dataset. We propose to transfer knowledge, from models pre-trained on extensive adult brain tumour datasets to smaller cohort datasets (e.g., paediatric brain tumours) in future studies, by leveraging Transfer Learning (TL). This approach aims to extract relevant features from pre-trained models, addressing the limited availability of annotated paediatric datasets and enhancing tumour characterisation in children. The implications and potential applications of this methodology in paediatric neuro-oncology are discussed.
Comparing Sequence-Based and Literature-Based Pathogenicity Scoring Methods for Human Variants
Authors: Mottin Luc , Naderi Nona , Mottaz Anaïs , Michel Pierre-André , Been Gerieke , Johansson Lennart , Swertz Morris , Stubbs Andrew , Pasche Emilie , Gobeill Julien , Ruch Patrick
Published: August 2024

Assessing the pathogenicity of genetic variants is a critical aspect of genomic medicine and precision healthcare. Over the last decades, the identification of genetic variants and their characterization has become simpler (advent of high-throughput sequencing technologies, analysis, and visualization support tools, etc.). However, the quality of assessments to distinguish benign from pathogenic variants is critical to inform clinical decision-making and improve patient outcomes. In this article, we investigate the relationships using correlation tests between the characterization of genetic variants in the literature and their pathogenicity scores computed by two state-of-the-art assessment tools (SIFT and PolyPhen-2).
A Chatbot-Like Platform to Enhance the Discovery of OMOP CDM Databases
Authors: Reis João A. , Almeida João R. , Almeida Tiago M. , Oliveira José L.
Published: August 2024

Multicentre studies become possible with the current strategies to solve the interoperability problems between databases. With the great adoption of those strategies, new problems regarding data discovery were raised. Some were solved using database catalogues and graphical dashboards for data analysis and comparison. However, when these communities grow, these strategies become obsolete. In this work, we addressed those challenges by proposing a platform with a chatbot-like mechanism to help medical researchers identify databases of interest. The tool was developed using the metadata extracted from OMOP CDM databases.
Similarity Analysis of Clinical Notes for Revealing Writing Styles in an Electronic Medical Record System
Authors: Murata Wataru , Liu Chang , Yamamoto Goshiro , Mori Yukiko , Kuroda Tomohiro
Published: August 2024

Background: One of the disadvantages of medical documentation in Electronic Medical Record (EMR) systems is that records tend to be redundant by “copying and pasting”, a writing style to duplicate and revise previous records. In this study, we analyzed the similarity between records to identify the factors affecting the writing style of clinical notes. Method: We analyzed 98,038 records of 4,149 patients from two years in the Department of Obstetricians and Gynecology at Kyoto University Hospital, Japan. We observed the correlation between the distribution of the record similarity and string amounts, as well as the disease codes and ratios of outpatient visit. Results: The patient group with high record similarity and large number of strings was the group with reproductive medicine, followed by the group of malignant tumor follow-up or Women’s Healthcare. Discussion: In reproductive medicine, physicians have a demand for an overarching evaluation, and in follow-up malignancies or in Women’s Healthcare, they have a demand to check for subtle differences from the last time. These facts along with our data insist that the writing style in EMR systems is related to the patient’s status. Conclusion: We declared that the writing style in EMR systems is affected by the patient’s status. The writing style of duplicating and revising is preferred (1) when there is a clinical demand for an overarching evaluation, and (2) when there is a clinical demand to check for subtle differences from the last time.
SemOntoMap: A Hybrid Approach for Semantic Annotation of Clinical Texts
Authors: Aouina Ons , Hilbey Jacques , Charlet Jean
Published: August 2024

This study addresses the challenge of leveraging free-text descriptions in Electronic Health Records (EHR) for clinical research and healthcare improvement. Despite the potential of this data, its direct interpretation by computers is limited. Semantic annotation emerges as a method to make EHR free text machine-interpretable but struggles with specific domain ontologies and faces heightened difficulties in psychiatry. To tackle these challenges, this study proposes a system based on unsupervised learning techniques to extract entities and their relationships, aligning them with a domain ontology. The effectiveness of this system has been validated within PsyCARE project by analyzing 60 patient discharge summaries.
Creating Digital Rescue Sheets
Authors: Sobotta Viktor M.G. , Deserno Thomas M.
Published: August 2024

Rescue sheets enable rescue personnel to timely extricate trapped victims of road traffic accidents and increase their chance of survival. However, in the year 2024, these rescue sheets are still paper based DIN A4 documents. The digital transformation of the rescue process through new reporting technologies, such as eCall or the International Standard Accident Number (ISAN) facilitates digital rescue sheets, providing benefits for availability and functionality. This work addresses design considerations raised by previous research to suggest a process for the creation of digital rescue sheets. Our process transforms high-resolution models provided by car manufacturers and vendors into small files by shape abstraction of the components. The system maps the body of the car to generic representative models of defined car body types reducing the number of models that need to be stored. We develop a hierarchical tree data structure with three levels that allows appending new components of the increasingly complex cars. Our data format for transmission of the rescue sheet sends all relevant data for visualization while still retaining a small file size to account for a poor internet connection. In the future, we aim to evaluate our approach involving car manufacturers and other stakeholders.
Costs of Health Registries: An Exploration for Health Services Research
Authors: Stausberg Jürgen , Harkener Sonja , Schoenthaler Martin
Published: August 2024

Health registries are an established methodology in health services research. Regarded as cheap bidder for observation studies in earlier times, many registries now benefit from an appropriate funding. However, reference figures for costs of registries in health services research are missing. Based on literature data as well as figures from a German funding initiative, the relationship between costs per newly recruited case and the annual sample size was analyzed. One can assume that a standard multicenter registry in health services research with 1,000 new patients each year will be appropriately financed with 1.1 to 2.2 million Euro in a period of five years. So smaller the sample size, so higher the costs for a newly recruited patient. The cost model of health registries should be further elaborated taking into account specifics of individual registries and differences in the level of case money.
A Web-Based Thai School Lunch Program Promotes Children’s Heights: A Cross-Sectional Study in Rural Schools
Authors: Tudpor Kukiat , Kareechum Worrameth , Sriphuwong Charnyuth , Nghiep Le Ke , Turnbull Niruwan
Published: August 2024

Background: Nutrition has an impact on development and linear growth. However, a few studies examine the connections between children’s age-standardized height and their nutritional status and diet quality. Objectives: This study aimed to find a relationship between dietary consumption and height for age among school students under a web-based Thai school lunch program. Methods: Anthropometric data and nutrient consumption were obtained from 24-hour records. Nutrient consumption was calculated using Inmucal. The parent and custodian’s data were from the electronic health records. Results: Children’s heights were not correlated with parents’ heights (P<0.720). Moreover, children with low height-for-age Z-score (HAZ) had significantly lower intakes of minerals (iron, magnesium, and selenium) and vitamins (B6, B12, C, and E). On the other hand, magnesium, selenium, vitamin B12, and vitamin E intakes of the children with normal HAZ were higher than their custodians. Conclusion: The Thai school lunch program effectively maintains the normal HAZ of children. Nutritional education in the community is recommended.
Leveraging Zero-Effort Technology for Quantification of Sleep Quality Metrics
Authors: Kaur Jasleen , Chauhan Vivek , Oetomo Arlene , Morita Plinio P.
Published: August 2024

Sleep quality is a critical factor in human health and well-being, with implications for various physiological and psychological processes. Traditional methods of sleep data collection are often limited by the quality and reliability of the data due to issues such as recall bias and subjective interpretation. This research aims to propose a novel framework that objectively measures and evaluates sleep quality using smart thermostats equipped with motion sensors, providing noninvasive and effortless sleep monitoring. The study conducts a comprehensive analysis of sleep patterns, exploring the relationship between activity sensors and sleep quality. By analyzing behavioral characteristics, the study identifies periods or clusters of days that require attention in terms of health and stress levels. The approach ensures privacy, ease of access, and integrates environmental factors, enabling a comprehensive understanding of an individual’s sleep health. The findings suggest that this zero-effort technology can significantly enhance sleep monitoring at both individual and population levels, with implications for health monitoring, stress management, and personalized healthcare interventions. Future work will focus on expanding the data set, incorporating more variables, and integrating contextual data to further improve sleep quality analysis and support real-time health interventions.
Practice on Designing Stages of Work Creation Methods as a Basis for Creating Photographic Works.
Authors: Tahalea Silviana Amanda , Novianty Erlina , Purnama Pongky , Ariani Ariani
Published: August 2024

Photography, as a combination of art and technology, represents the evolution of the field of fine arts in general, which is interdependent. The scientific aspect of the method of creating photographic works lies in its logical explanation, including the narrative structure and intrinsic qualities of the photographic results. This activity uses several methods, such as counseling or outreach methods, by providing material on creating photographic works for beginners and professional photographers and practicing creating photographic works for around 40 participants at the Bulungan Blok M Theater at 13.00 WIB. Participants are members of the Jakarta International Photo Festival (JIPFEST) photography community, which includes individuals with various levels of ability and photography educational backgrounds. This systematic approach is actively implemented by the JIPFEST community, a group of photography enthusiasts who regularly hold exhibitions, workshops, and mentoring sessions. The aim of this PKM is to equip participants with the ability to design photographic works with a strong message as well as develop appropriate concept elaborations. From training activities that provide a concrete approach to developing photographic works of art, including the steps in creating photographic works, it has proven to be effective as an important instrument in creating photographic works that not only convey a solid concept but also succeed in conveying a clear message. Both beginners and professional photographers can use these stages as a foundation for their creative process and improve their abilities in creating photography.