bluuebunny/crossref_metadata_2025 huggingface dataset:
Presynaptic Congenital Myasthenic Syndromes: Understanding Clinical Phenotypes through In vivo Models
Authors: Pugliese Alessia , Holland Stephen H. , Rodolico Carmelo , Lochmüller Hanns , Spendiff Sally
Published: May 2023

Presynaptic congenital myasthenic syndromes (CMS) are a group of genetic disorders affecting the presynaptic side of the neuromuscular junctions (NMJ). They can result from a dysfunction in acetylcholine (ACh) synthesis or recycling, in its packaging into synaptic vesicles, or its subsequent release into the synaptic cleft. Other proteins involved in presynaptic endplate development and maintenance can also be impaired. Presynaptic CMS usually presents during the prenatal or neonatal period, with a severe phenotype including congenital arthrogryposis, developmental delay, and apnoeic crisis. However, milder phenotypes with proximal muscle weakness and good response to treatment have been described. Finally, many presynaptic genes are expressed in the brain, justifying the presence of additional central nervous system symptoms. Several animal models have been developed to study CMS, providing the opportunity to identify disease mechanisms and test treatment options. In this review, we describe presynaptic CMS phenotypes with a focus on in vivo models, to better understand CMS pathophysiology and define new causative genes.
Development of an International SMA Bulbar Assessment for Inter-professional Administration
Authors: Dunaway Young Sally , McGrattan Katlyn , Johnson Emily , van der Heul Marise , Duong Tina , Bakke Merete , Werlauff Ulla , Pasternak Amy , Cattaneo Camilla , Hoffman Katie , Fanelli Lavinia , Breaks Anne , Allison Kristen , Baranello Giovanni , Finkel Richard , Coratti Giorgia , Lofra Robert Muni
Published: May 2023

Background:

Progressive weakness can affect bulbar muscles in individuals with moderate to severe forms of spinal muscular atrophy (SMA). The paucity of standardized, valid bulbar assessments capturing clinically significant deficits in SMA impedes the ability to monitor function, facilitate intervention, or detect treatment response.

Objective:

To fill this void, an international multidisciplinary team gathered to develop an agreed upon consensus-derived assessment of bulbar function in SMA for inter-professional administration to enhance our ability to monitor disease progression, support clinical management, and evaluate treatment effects.

Methods:

Fifty-six international clinicians experienced in SMA were invited and engaged using the Delphi method over multiple rounds of web-based surveys to establish consensus.

Results:

Serial virtual meetings occurred with 42 clinicians (21 speech and language therapists, 11 physical therapists, 5 neurologists, 4 occupational therapists, and 1 dentist). Seventy-two validated assessments of bulbar function were identified for potential relevance to individuals with SMA (32 accessible objective, 11 inaccessible objective, 29 patient-reported outcomes). Delphi survey rounds ( n = 11, 15, 15) achieved consensus on individual items with relevance and wording discussed. Key aspects of bulbar function identified included: oral intake status, oral facial structure and motor strength, swallowing physiology, voice & speech, and fatigability.

Conclusions:

Multidisciplinary clinicians with expertise in bulbar function and SMA used Delphi methodology to reach consensus on assessments/items considered relevant for SMA across all age groups. Future steps include piloting the new scale moving towards validation/reliability. This work supports the advancement of assessing bulbar function in children and adults with SMA by a variety of professionals.
Intake of dark green vegetables may benefit specific cognitive domains in US men and women aged 60 years or older
Authors: Bigman Galya , Shea Kyla M. , Rusu Marius Emil , Ryan Alice S.
Published: May 2023

BACKGROUND:

The prevalence and relationship between dark green vegetables (DGV) and specific cognitive domains in the aging US population are not well-established for men and women.

OBJECTIVE:

To explore the associations between DGV, its bioactive nutrients, and cognitive function, including its specific domains, and whether they differ by sex

METHODS:

The study analyzed a cross-sectional sample of 2,793 US adults aged 60 or older from the 2011–2014 NHANES. DGV was dichotomized, and bioactive nutrients were divided into quartiles. Weighted linear regressions were used to analyze the association between DGV, bioactive nutrients, and standardized cognitive function scores, including specific domains (CERAD, DWR, AFT, DSST), while controlling for covariates. The study also tested for sex-based effect modification

RESULTS:

Overall, 61.7% of participants reported no DGV intake, and men reported no DGV intake more frequently than women (67.8 vs.56.5%, p < 0.001). DGV was associated with overall cognitive function(β= 0.10, p = 0.024) and by its specific domains: memory-related i.e., learning and remembering (CERAD:β= 0.10, p = 0.015; DWR:β= 0.10, p = 0.010), marginally associated with executive function (AFT:β= 0.10, p = 0.075), but not with problem-solving(DSST:β= 0.03, p = 0.587). Although the associations between bioactive nutrients and specific cognitive domains were mixed, a higher intake of these nutrients was still linked to higher overall cognitive function. Only β-carotene and its associations with overall cognitive and AFT were modified by sex.

CONCLUSION:

The majority of US older adults (>60%) lack DGV in their diet. Intake of DGV, which is rich in phylloquinone, β-carotene, and α-tocopherol, may benefit certain domains of cognition in men and women, such as learning and memory
Disability and caregiver burden: Unique challenges in a developing country
Authors: Hewawitharana Bimba D.R. , Wijesinghe Champa J. , De Silva Aruna , Phillips John P. , Hewawitharana Gemunu P.
Published: May 2023

PURPOSE: This study aimed to identify factors, including degree of disability, that contribute to the caregiver burden of raising children with cerebral palsy in Sri Lanka. METHODS: Participants were caregivers of children with cerebral palsy attending the pediatric neurology clinic of the only tertiary care center in southern Sri Lanka. The locally validated Caregiver Difficulties Scale (CDS) was administered, and demographic information was obtained in a structured interview. Disability data was accessed through the medical record. RESULTS: Of 163 caregivers who participated in this study, 133 (81.2%) demonstrated a moderate to high level of burden, and 91 (55.8%) were at high risk for psychological burden. In the bivariate analysis, caregiver burden significantly correlated with degree of physical disability based on the Gross Motor Function Classification System (GMFCS) and the Manual Ability Classification System (MACS), the presence of medical co-morbidities, and having two or more children. However, only the GMFCS level and number of children remained significant predictors of caregiver burden after controlling for confounding effects. CONCLUSION: Raising a child with cerebral palsy in Sri Lanka is likely to cause caregiver burden, particularly if they have a high level of disability or one or more siblings. Monitoring caregiver burden as part of routine cerebral palsy management is important, which allows targeting psychosocial support to families most in need.
WHO’s Community-Centered Epidemic and Pandemic Information Platform: Hive
Authors: Briand Sylvie , Hess Sarah , Lee John , Sheridan Katherine , Nguyen Tim , Yau Brian
Published: May 2023

Each epidemic and pandemic is accompanied by an infodemic. The infodemic during the COVID-19 pandemic was unprecedented. Accessing accurate information was difficult and misinformation harmed the pandemic response, the health of individuals and trust in science, governments and societies. WHO is building a community-centered information platform, the Hive, to deliver on the vision of ensuring that all people everywhere have access to the right information, at the right time, in the right format in order to make decisions to protect their health and the health of others. The platform provides access to credible information, a safe space for knowledge-sharing, discussion, and collaborating with others, and a forum to crowdsource solutions to problems. The platform is equipped with many collaboration features, including instant chats, event management, and data analytics tools to generate insights. The Hive platform is an innovative minimum viable product (MVP) that seeks to leverage the complex information ecosystem and the invaluable role communities play to share and access trustworthy health information during epidemics and pandemics.
An Annotation Workbench for Semantic Annotation of Data Collection Instruments
Authors: Sasse Julia , Fluck Juliane
Published: May 2023

Semantic interoperability, i.e., the ability to automatically interpret the shared information in a meaningful way, is one of the most important requirements for data analysis of different sources. In the area of clinical and epidemiological studies, the target of the National Research Data Infrastructure for Personal Health Data (NFDI4Health), interoperability of data collection instruments such as case report forms (CRFs), data dictionaries and questionnaires is critical. Retrospective integration of semantic codes into study metadata at item-level is important, as ongoing or completed studies contain valuable information, which should be preserved. We present a first version of a Metadata Annotation Workbench to support annotators in dealing with a variety of complex terminologies and ontologies. User-driven development with users from the fields of nutritional epidemiology and chronic diseases ensured that the service fulfills the basic requirements for a semantic metadata annotation software for these NFDI4Health use cases. The web application can be accessed using a web browser and the source code of the software is available with an open-source MIT license.
Towards Prediction of Injuries in Traffic Accidents
Authors: Sobotta Viktor M.G. , Flormann Maximilian , Henze Roman , Deserno Thomas M.
Published: May 2023

For people involved in road traffic accidents, the time necessary to respond is crucial and it is hard to discern, which persons in which cars most urgently need help. To plan the rescue operation before arriving at the scene, digital information regarding the severity of the accident is vital. Our framework aims to transmit available data from the in-car sensors and to simulate the forces enacted on occupants using injury models. To avoid data security and privacy issues, we install low-cost hardware in the car for aggregation and preprocessing. Our framework can be retrofitted to existing cars and therefore could extend the benefits to a wide range of people
Implementation of the Regulation on the European Health Data Space (EHDS) in Croatia
Authors: Brađašević Emanuel
Published: May 2023

Since the European Commission has published a Proposal for a Regulation – The European Health Data Space, Croatia is actively working on the implementation. Public sector bodies including the Croatian Institute of Public Health, Ministry of Health and Croatian Health Insurance Fund play a key role in this process. The establishment of a Health Data Access Body is the main challenge in this effort. Potential challenges and obstacles in this process and projects that follow the efforts are described in this paper.
Secure Sharing of Health-Related Data: Research Description of the VINTER, DELFIN, and HEIDA Projects
Authors: Linge Helena M. , Brännvall Rickard
Published: May 2023

The need for secure and integrity-preserved data sharing has become increasingly important in the emerging era of changed demands on healthcare and increased awareness of the potential of data. In this research plan, we describe our path to explore the optimal use of integrity preservation in health-related data contexts. Data sharing in these settings is poised to increase health, improve healthcare delivery, improve the offering of services and products from commercial entities, and strengthen healthcare governance, all with a maintained societal trust. The HIE challenges relate to legal boundaries and to the importance of maintaining accuracy and utility in the secure sharing of health-related data.
Enabling Clinical Trials of Artificial Intelligence: Infrastructure for Heart Failure Predictions
Authors: Ranjbar Arian , Ravn Jesper , Ronningen Erland , Hanseth Ole
Published: May 2023

The last decade has seen a large increase in artificial intelligence research within healthcare. However, relatively few attempts of clinical trials have been made for such configurations. One of the main challenges arise in the extensive infrastructure necessary, both for development, but particularly to run prospective studies. In this paper, infrastructural requirements are first presented, together with constraints due to underlying production systems. Then, an architectural solution is presented, with the aim of both enabling clinical trials and streamline model development. Specifically, the suggested design is intended for research of heart failure prediction from ECG, but is generalizable to projects using similar data protocols and installed base.
Post Hoc Sample Size Estimation for Deep Learning Architectures for ECG-Classification
Authors: Bickmann Lucas , Plagwitz Lucas , Varghese Julian
Published: May 2023

Deep Learning architectures for time series require a large number of training samples, however traditional sample size estimation for sufficient model performance is not applicable for machine learning, especially in the field of electrocardiograms (ECGs). This paper outlines a sample size estimation strategy for binary classification problems on ECGs using different deep learning architectures and the large publicly available PTB-XL dataset, which includes 21801 ECG samples. This work evaluates binary classification tasks for Myocardial Infarction (MI), Conduction Disturbance (CD), ST/T Change (STTC), and Sex. All estimations are benchmarked across different architectures, including XResNet, Inception-, XceptionTime and a fully convolutional network (FCN). The results indicate trends for required sample sizes for given tasks and architectures, which can be used as orientation for future ECG studies or feasibility aspects.
Conception and Development of a Targeted Alert System: Multisystem Considerations
Authors: Ehrler Frederic , Blondon Katherine
Published: May 2023

Alerting systems have a strong potential to improve quality of care in hospital by ensuring that clinicians provide more effective and timely care to their patients. Many systems have been implemented but often fail to unleash their full potential due to the problem of alert fatigue. As an attempt to reduce this fatigue we have developed a targeted alerting system ensuring only the concerned clinicians receives the alerts. The conception of the system went through several steps going from the identification of the requirement, the prototyping and implementation into several systems. The results present the different parameters taken into consideration and developed frontends. We finally discuss the important considerations of alerting system, such as the necessity of a governance. The system still needs a formal evaluation to validate that it responds to its promises before being deployed more largely.
Nationally Shared Medication Lists – Describing Systems in the Nordic Countries
Authors: Hammar Tora , Bergmo Trine S. , Bülow Cille , Clausen Stine S. , Manskow Unn Sollid , Timonen Johanna , Jøsendal Anette Vik
Published: May 2023

This paper provides an overview of shared medication lists (SMLs) in four Nordic countries (Denmark, Finland, Norway and Sweden) with a focus on the type of information the list is based on. This is a structured comparison conducted in stages using an expert group, grey papers, unpublished materials, web pages, as well as scientific papers. Denmark and Finland have implemented their solutions for an SML and Norway and Sweden are working on the implementation of their solution. Denmark and Norway have or are aiming at a list based on medication orders, while Finland and Sweden have lists based on prescriptions.
An Architecture for Providing Personalized Digital Health
Authors: Delgado Jaime , Llorente Silvia
Published: May 2023

Patients need mechanisms to integrate health information coming from different sources, including personal devices. This would lead to Personalized Digital Health (PDH). HIPAMS (Health Information Protection And Management System) is a modular and interoperable secure architecture that helps in achieving this objective and building a Framework for PDH. The paper presents HIPAMS and how it supports PDH.
Social Determinants of Health Data Quality at Different Levels of Geographic Detail
Authors: Greer Melody L. , Garza Maryam Y. , Sample Steve , Bhattacharyya Sudeepa
Published: May 2023

Social determinants of health (SDOH) impact 80% of health outcomes from acute to chronic disorders, and attempts are underway to provide these data elements to clinicians. It is, however, difficult to collect SDOH data through (1) surveys, which provide inconsistent and incomplete data, or (2) aggregates at the neighborhood level. Data from these sources is not sufficiently accurate, complete, and up-to-date. To demonstrate this, we have compared the Area Deprivation Index (ADI) to purchased commercial consumer data at the individual-household level. The ADI is composed of income, education, employment, and housing quality information. Although this index does a good job of representing populations, it is not adequate to describe individuals, especially in a healthcare context. Aggregate measures are, by definition, not sufficiently granular to describe each individual within the population they represent and may result in biased or imprecise data when simply assigned to the individual. Moreover, this problem is generalizable to any community-level element, not just ADI, in so far as they are an aggregate of the individual community members.
Low Valence Low Arousal Stimuli: An Effective Candidate for EEG-Based Biometrics Authentication System
Authors: Jeswani Jahanvi , Govarthan Praveen Kumar , Selvaraj Abirami , Prince Amalin , Thomas John , Kalathe Mohanavelu , Sreeraj V S  , Subramaniam Venkat , Agastinose Ronickom Jac Fredo
Published: May 2023

Electroencephalography (EEG) has recently gained popularity in user authentication systems since it is unique and less impacted by fraudulent interceptions. Although EEG is known to be sensitive to emotions, understanding the stability of brain responses to EEG-based authentication systems is challenging. In this study, we compared the effect of different emotion stimuli for the application in the EEG-based biometrics system (EBS). Initially, we pre-processed audio-visual evoked EEG potentials from the ‘A Database for Emotion Analysis using Physiological Signals’ (DEAP) dataset. A total of 21 time-domain and 33 frequency-domain features were extracted from the considered EEG signals in response to Low valence Low arousal (LVLA) and High valence low arousal (HVLA) stimuli. These features were fed as input to an XGBoost classifier to evaluate the performance and identify the significant features. The model performance was validated using leave-one-out cross-validation. The pipeline achieved high performance with multiclass accuracy of 80.97% and a binary-class accuracy of 99.41% with LVLA stimuli. In addition, it also achieved recall, precision and F-measure scores of 80.97%, 81.58% and 80.95%, respectively. For both the cases of LVLA and LVHA, skewness was the stand-out feature. We conclude that boring stimuli (negative experience) that fall under the LVLA category can elicit a more unique neuronal response than its counterpart the LVHA (positive experience). Thus, the proposed pipeline involving LVLA stimuli could be a potential authentication technique in security applications.
Towards a Consistent Representation of Contradictions Within Health Data for Efficient Implementation of Data Quality Assessments
Authors: Yusuf Khalid O. , Hanss Sabine , Krefting Dagmar
Published: May 2023

Contradictions as a data quality indicator are typically understood as impossible combinations of values in interdependent data items. While the handling of a single dependency between two data items is well established, for more complex interdependencies, there is not yet a common notation or structured evaluation method established to our knowledge. For the definition of such contradictions, specific biomedical domain knowledge is required, while informatics domain knowledge is responsible for the efficient implementation in assessment tools. We propose a notation of contradiction patterns that reflects the provided and required information by the different domains. We consider three parameters (α, β, θ): the number of interdependent items as α, the number of contradictory dependencies defined by domain experts as β, and the minimal number of required Boolean rules to assess these contradictions as θ. Inspection of the contradiction patterns in existing R packages for data quality assessments shows that all six examined packages implement the (2,1,1) class. We investigate more complex contradiction patterns in the biobank and COVID-19 domains showing that the minimum number of Boolean rules might be significantly lower than the number of described contradictions. While there might be a different number of contradictions formulated by the domain experts, we are confident that such a notation and structured analysis of the contradiction patterns helps to handle the complexity of multidimensional interdependencies within health data sets. A structured classification of contradiction checks will allow scoping of different contradiction patterns across multiple domains and effectively support the implementation of a generalized contradiction assessment framework.
A Model for Multi-Institutional Clinical Data Repository
Authors: Natarajan Karthik , Weng Chunhua , Sengupta Soumitra
Published: May 2023

Creating a sustainable model for clinical data infrastructure requires the inclusion of key stakeholders, harmonization of their needs and constraints, integration with data governance considerations, conforming to FAIR principles while maintaining data safety and data quality, and maintaining financial health for contributing organizations and partners. This paper reflects on Columbia University’s 30+ years of experiences in designing and developing clinical data infrastructure that synergizes both patient care and clinical research missions. We define the desiderata for a sustainable model and make recommendations of best practices to achieve a sustainable model.
Status of Phlebitis in South Korean Hospitals: Focusing on Electronic Incident Reporting Systems
Authors: Cho Insook , Lee Jae-Ho , Kim Eun Man
Published: May 2023

Peripheral venous catheterization (PVC) is the most commonly used invasive technique, and its importance to patient safety is increasing. And phlebitis is a common complication which can lead to increased costs and extended hospital stays. This study attempted to characterize the current status of phlebitis based on incident reports in the Korea Patient Safety Reporting & Learning System. This retrospective descriptive study analysed 259 phlebitis cases reported in that system from 1 July 2017 to 31 December 2019. The analysis results were summarized using numbers and percentages or means with standard deviations. Among the reported phlebitis cases, antibiotics and high-osmolarity fluids comprised 48.2% of the intravenous inflammatory drugs used. All reported cases presented blood-flow infections. Insufficient observation or management was the most common cause of phlebitis. It was found that interventions for phlebitis were inconsistent with those recommended in evidence-based guidelines. Recommendations for nurses to alleviate complications in PVC must be promoted and educated. It is necessary to provide feedback from the incident reports analysis.
From FAIR4Health Project to 1+MG Initiative: A Spain – Italy Collaboration
Authors: Boaglio Ariam , Parra-Calderón Carlos Luis , Martinez-García Alicia , Escalona-Cuaresma María José , Maggi Norbert , Coviello Domenico , Uva Paolo , Giacomini Mauro
Published: May 2023

Results of two major projects funded by the European Union are taken into consideration: Fair4Health regarding the possibility of sharing clinical data in various environments applying FAIR principles and 1+Million Genome for the in-depth study of the human genome in Europe. Specifically, the Gaslini hospital plans to move on both areas joining the Hospital on FHIR initiative matured within the fair4health project and also collaborate with other Italian healthcare facilities through the implementation of a Proof of Concept (PoC) in the 1+MG. The aim of this short paper is to evaluate the applicability of some of the tools of the fair4health project to the Gaslini infrastructure to facilitate its participation in the PoC. One of the aims is also to prove the possibility of reuse the results of well-performed European funded projects to boost routine research in qualified healthcare facilities.
Transfer Learning for Early Prediction of Adverse Drug Reactions: Docetaxel and Alopecia in Breast Cancer as a Case Study
Authors: Dimitsaki Stella , Natsiavas Pantelis , Jaulent Marie-Christine
Published: May 2023

Transfer Learning (TL) is an approach which has not yet been widely investigated in healthcare, mostly applied in image data. This study outlines a TL pipeline leveraging Individual Case Safety reports (ICSRs) and Electronic Health Records (EHR), applied for the early detection Adverse Drug Reactions (ADR), evaluated using of alopecia and docetaxel on breast cancer patients as use case.
Extended Reality Is Underutilized in Medical Device Training: A Descriptive Literature Review
Authors: Laukkavirta Minna , Tarkkala Krista , Tienari Marjo , Ikonen Tuija S.
Published: May 2023

This descriptive review provides a synthesis of existing literature about the use of extended reality (XR) including virtual (VR) and augmented reality (AR) technology solutions for competence assurance, training and orientation regarding digital skills and medical device training. From the literature, only few original studies were recognized with a study question or aim to assess medical device training as the target of virtual training modalities. XR methods could provide potential useful solutions to improve medical device competence. Based on the literature, it was evident that further studies are required to research the possibilities of XR technologies to improve medical device training.
Medical Semiology Teaching Based on Intelligent eLearning
Authors: Ayumba Eustache Muteba
Published: May 2023

The paper proposes a methodology that emphasis techno-pedagogy, namely the constructivist and the adaptive intelligent learning of specialized semiology of COVID-19. An e-learning built on a constructivist pedagogy with a technology such adaptive intelligent environment, can be individualized (adaptive learning), can enhance learners’ interactions with others (collaborative learning), and transforming the role of the teacher as facilitators of learning and assessor of competency. To make our system intelligent, we cope with Artificial Intelligence and Big data.
Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification
Authors: Gharehbaghi Arash , Partovi Elaheh , Babic Ankica
Published: May 2023

This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for the screening heart abnormalities.
Patient Electronic Health Record as Temporal Graphs for Health Monitoring
Authors: Le Baher Hugo , Azé Jérôme , Bringay Sandra , Poncelet Pascal , Rodriguez Nancy , Dunoyer Caroline
Published: May 2023

Machine learning methods are becoming increasingly popular to anticipate critical risks in patients under surveillance reducing the burden on caregivers. In this paper, we propose an original modeling that benefits of recent developments in Graph Convolutional Networks: a patient’s journey is seen as a graph, where each node is an event and temporal proximities are represented by weighted directed edges. We evaluated this model to predict death at 24 hours on a real dataset and successfully compared our results with the state of the art.
How to Design Successful Participatory Design Workshops for Digital Health Solutions?
Authors: Denecke Kerstin , von Kaenel François , Miletic Marko , Fernández-Llatas Carlos , Ibañez-Sánchez Gema , Valero-Ramón Zoe , Martînez-Millana Antonio , Segura María , Rivera Romero Octavio
Published: May 2023

Participatory design (PD) is increasingly used to support design and development of digital health solutions. The involves representatives of future user groups and experts to collect their needs and preferences and ensure easy to use and useful solutions. However, reflections and experiences with PD in designing digital health solutions are rarely reported. The objective of this paper is to collect those experiences including lessons learnt and moderator experiences, and to identify challenges. For this purpose, we conducted a multiple case study to explore the skill development process required to successfully design a solution in the three cases. From the results, we derived good practice guidelines to support designing successful PD workshops. They include adapting the workshop activities and material to the vulnerable participant group and considering their environment and previous experiences, planning sufficient time for preparation and supporting the activities with appropriate material. We conclude that PD workshop results are perceived as useful for designing digital health solutions, but careful design is very relevant.
Ontological Modeling of Clinical Study Forms
Authors: Hilbey Jacques , Raboudi Amel , Krebs Marie-Odile , Charlet Jean
Published: May 2023

The use of eCRFs is now commonplace in clinical research studies. We propose here an ontological model of these forms allowing to describe them, to express their granularity and to link them to the relevant entities of the study in which they are used. It has been developed in a psychiatry project but its generality may allow a wider application.
Exploring New Possibilities for Research Data Exploration Using the Example of the German Core Data Set
Authors: Menzel Felix , Waltemath Dagmar , Henkel Ron
Published: May 2023

The German Medical Informatics Initiative (MII) aims to increase the interoperability and reuse of clinical routine data for research purposes. One important result of the MII work is a German-wide common core data set (CDS), which is to be provided by over 31 data integration centers (DIZ) following a strict specification. One standard format for data sharing is HL7/FHIR. Locally, classical data warehouses are often in use for data storage and retrieval. We are interested to investigate the advantages of a graph database in this setting. After having transferred the MII CDS into a graph, storing it in a graph database and subsequently enriching it with accompanying meta-information, we see a great potential for more sophisticated data exploration and analysis. Here we describe the extract-transform-load process which we set up as a proof of concept to achieve the transformation and to make the common set of core data accessible as a graph.
Modeling Patient Treatment Trajectories Using Markov Chains for Cost Analysis
Authors: Haug Markus , Kolde Raivo , Oja Marek , Pajusalu Maarja
Published: May 2023

Electronically stored medical records offer a rich source of data for investigating treatment trajectories and identifying best practices in healthcare. These trajectories, which consist of medical interventions, give us a foundation to evaluate the economics of treatment patterns and model the treatment paths. The aim of this work is to introduce a technical solution for the aforementioned tasks. The developed tools use the open source Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership Common Data Model to construct treatment trajectories and implement these to compose Markov models for composing financial analysis between standard of care and alternatives.
Learning to Classify Medical Discharge Summaries According to ICD-9
Authors: Moros Leonardo , Azé Jérôme , Bringay Sandra , Poncelet Pascal , Servajean Maximilien , Dunoyer Caroline
Published: May 2023

Context: We present a post-hoc approach to improve the recall of ICD classification. Method: The proposed method can use any classifier as a backbone and aims to calibrate the number of codes returned per document. We test our approach on a new stratified split of the MIMIC-III dataset. Results: When returning 18 codes on average per document we obtain a recall that is 20% better than a classic classification approach.
User-Centered Design of a Speech-Based Application to Support Caregivers
Authors: Reichenpfader Daniel , Gira Matthias
Published: May 2023

The shortage of skilled nursing personnel is – among other reasons – due to the low attractiveness of the profession, comprising high workloads and atypical working hours. Studies show that speech-based documentation systems increase documentation efficiency and satisfaction of physicians. This paper describes the development process of a speech-based application to support nurses, according to the user-centered design approach. User requirements were collected based on interviews (n=6) as well as observations (n=6) in three institutions and were evaluated by means of qualitative content analysis. A prototype of the derived system architecture was implemented. Based on a usability test (n=3), further potentials for improvement were determined. The resulting application enables nurses to dictate personal notes, share them with colleagues and transmit notes to the existing documentation system. We conclude that the user-centered approach ensures the extensive consideration of the nursing staff’s requirements and shall be continued for further development.
Investigating Canadian Public Attitudes Toward COVID-19 Vaccine Mandates with a Nested Analysis Framework
Authors: Yang Yang , Lei Xiaomeng , Zhou Zeping , Tsao Shu-Feng , Butt Zahid A. , Chen Helen H.
Published: May 2023

Background: Social media is an important medium for studying public attitudes toward COVID-19 vaccine mandates in Canada, and Reddit network communities are a good source for this. Methods: This study applied a “nested analysis” framework. We collected 20378 Reddit comments via the Pushshift API and developed a BERT-based binary classification model to screen for relevance to COVID-19 vaccine mandates. We then used a Guided Latent Dirichlet Allocation (LDA) model on relevant comments to extract key topics and assign each comment to its most relevant topic. Results: There were 3179 (15.6%) relevant and 17199 (84.4%) irrelevant comments. Our BERT-based model achieved 91% accuracy trained with 300 Reddit comments after 60 epochs. The Guided LDA model had an optimal coherence score of 0.471 with four topics: travel, government, certification, and institutions. Human evaluation of the Guided LDA model showed an 83% accuracy in assigning samples to their topic groups. Conclusion: We develop a screening tool for filtering and analyzing Reddit comments on COVID-19 vaccine mandates through topic modelling. Future research could develop more effective seed word-choosing and evaluation methods to reduce the need for human judgment.
Ontology-Based Semantic Annotation of French Psychiatric Clinical Documents
Authors: Aouina Ons , Hilbey Jacques , Charlet Jean
Published: May 2023

Building a timeline of psychiatric patient profiles can answer many valuable questions, such as how important medical events affect the progression of psychosis in patients. However, the majority of text information extraction and semantic annotation tools, as well as domain ontologies, are only available in English and cannot be easily extended to other languages, due to fundamental linguistic differences. In this paper, we describe a semantic annotation system based on an ontology developed in the PsyCARE framework. Our system is being manually evaluated by two annotators on 50 patient discharge summaries, showing promising results.
Classifiers of Medical Eponymy in Scientific Texts
Authors: Toddenroth Dennis
Published: May 2023

Many concepts in the medical literature are named after persons. Frequent ambiguities and spelling varieties, however, complicate the automatic recognition of such eponyms with natural language processing (NLP) tools. Recently developed methods include word vectors and transformer models that incorporate context information into the downstream layers of a neural network architecture. To evaluate these models for classifying medical eponymy, we label eponyms and counterexamples mentioned in a convenience sample of 1,079 Pubmed abstracts, and fit logistic regression models to the vectors from the first (vocabulary) and last (contextualized) layers of a SciBERT language model. According to the area under sensitivity-specificity curves, models based on contextualized vectors achieved a median performance of 98.0% in held-out phrases. This outperformed models based on vocabulary vectors (95.7%) by a median of 2.3 percentage points. When processing unlabeled inputs, such classifiers appeared to generalize to eponyms that did not appear among any annotations. These findings attest to the effectiveness of developing domain-specific NLP functions based on pre-trained language models, and underline the utility of context information for classifying potential eponyms.
Diagnosis Classification in the Emergency Room Using Natural Language Processing
Authors: van Buchem Marieke M. , ‘t Hart Hanna H. , Mosteiro Pablo J. , Kant Ilse M.J. , Bauer Martijn P.
Published: May 2023

Diagnosis classification in the emergency room (ER) is a complex task. We developed several natural language processing classification models, looking both at the full classification task of 132 diagnostic categories and at several clinically applicable samples consisting of two diagnoses that are hard to distinguish.
Data-Driven Identification of Clinical Real-World Expressions Linked to ICD
Authors: Kugic Amila , Pfeifer Bastian , Schulz Stefan , Kreuzthaler Markus
Published: May 2023

A semi-structured clinical problem list containing ∼1.9 million de-identified entries linked to ICD-10 codes was used to identify closely related real-world expressions. A log-likelihood based co-occurrence analysis generated seed-terms, which were integrated as part of a k-NN search, by leveraging SapBERT for the generation of an embedding representation.
Large Language Model as Unsupervised Health Information Retriever
Authors: Jiang Keyuan , Mujtaba Mohammed M. , Bernard Gordon R.
Published: May 2023

Retrieving health information is a task of search for health-related information from a variety of sources. Gathering self-reported health information may help enrich the knowledge body of the disease and its symptoms. We investigated retrieving symptom mentions in COVID-19-related Twitter posts with a pretrained large language model (GPT-3) without providing any examples (zero-shot learning). We introduced a new performance measure of total match (TM) to include exact, partial and semantic matches. Our results show that the zero-shot approach is a powerful method without the need to annotate any data, and it can assist in generating instances for few-shot learning which may achieve better performance.
Description of a French Population of Diabetics Treated Followed up by General Practitioners
Authors: Fruchart Mathilde , Lamer Antoine , Lemaitre Madleen , Beuscart Jean-Baptiste , Calafiore Matthieu , Quindroit Paul
Published: May 2023

In France, the prevalence of treated diabetes has been estimated at 4.6%, or more than 3 million people and 5.2% in Northern France. The reuse of primary care data allows to study outpatient clinical data such as laboratory results and drug prescriptions, which are not documented in claims and hospital databases. In this study, we selected the population of treated diabetics from the Wattrelos primary care data warehouse, in North of France. Firstly, we studied the laboratory results of diabetics by identifying whether the recommendations of the French National Authority for Health (HAS) were respected. In a second step, we studied the prescriptions of diabetics by identifying the oral hypoglycemic agents treatments and insulins treatments. The diabetic population represents 690 patients of the health care center. The recommendations on labortatory are respected for 84% of diabetics. The majority of diabetics are treated with oral hypoglycemic agents 68.6%. As recommended by the HAS, metformin is the first-line treatment in the diabetic population.
Data Collection and Analysis Methods for Smart Nudging to Promote Physical Activity: Protocol for a Mixed Methods Study
Authors: Dhanasekaran Seshathiri , Andersen Anders , Karlsen Randi , Håkansson Anne , Henriksen André
Published: May 2023

New digital technologies like activity trackers, nudge concepts, and approaches can inspire and improve personal health. There is increasing interest in employing such devices to monitor people’s health and well-being. These devices can continually gather and examine health-related information from people and groups in their familiar surroundings. Context-aware nudges can assist people in self-managing and enhancing their health. In this protocol paper, we describe how we plan to investigate what motivates people to engage in physical activity (PA), what influences them to accept nudges, and how participant motivation for PA may be impacted by technology use.
Converting HL7 CDA Based Nationwide Austrian Medication Data to OMOP CDM
Authors: Katsch Florian , Hussein Rada , Korntheuer Raffael , Duftschmid Georg
Published: May 2023

Austria’s national Electronic Health Record (EHR) system holds information on medication prescriptions and dispenses in highly structured HL7 Clinical Document Architecture (CDA) documents. Making these data accessible for research is desirable due to their volume and completeness. This work describes our approach of transforming the HL7 CDA data into Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and highlights a key challenge, namely mapping the Austrian drug terminology to OMOP standard concepts.
Linkage Health and Environmental Data: A Case Study on Asthma Prevalence in Children and Adolescents in Slovenia
Authors: Rejc Tanja , Kukec Andreja , Carli Tanja , Mlakar Ajda
Published: May 2023

For the last 10 years there is no data on the prevalence of asthma in Slovenian children. To ensure accurate and high-quality data we will conduct a cross-sectional survey Health Interview (HIS) and Health Examination Survey (HES) design type. Therefore, we first prepared the study protocol. To get the data for the HIS part of the study we developed a new questionnaire. The exposure to outdoor air quality will be evaluated from the National Air Quality network data. In Slovenia the problems with health data should be addressed with the common unified system at the national level.
Designing a Persuasive E-Coaching Application for Informal Caregivers
Authors: Premanandan Shweta , Ahmad Awais , Cajander Åsa , Ågerfalk Pär
Published: May 2023

Being an informal caregiver is not easy, and might cause physical and psychosocial burden, especially in the long run. However, the formal health care system has little support for informal caregivers who experience abandonment and lack information. Mobile health can potentially be an efficient and cost-effective way of supporting informal caregivers. However, research has shown that mHealth systems often have problems with usability, and people do not use the systems for more than a short period. Therefore, this paper explores the design of an mHealth app using Persuasive Design, an established design framework. This paper presents the design of the first version of the e-coaching application using the persuasive design framework and unmet needs of informal caregivers from the literature. This prototype version will be updated based on interview data from informal caregivers in Sweden.
Can Synthetic Images Improve CNN Performance in Wound Image Classification?
Authors: Malihi Leila , Hübner Ursula , Richter Mats L. , Moelleken Maurice , Przysucha Mareike , Busch Dorothee , Heggemann Jan , Hafer Guido , Wiemeyer Stefan , Heidemann Gunther , Dissemond Joachim , Erfurt-Berge Cornelia , Barkhau Carlotta , Hendriks Achim , Hüsers Jens
Published: May 2023

For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large amounts, Generative Adversarial Networks (GAN) are a standard tool for synthesising artificial training images that can be used to augment the data set. We investigated the quality of synthetic wound images regarding two aspects: (i) improvement of wound-type classification by a Convolutional Neural Network (CNN) and (ii) how realistic such images look to clinical experts (n = 217). Concerning (i), results show a slight classification improvement. However, the connection between classification performance and the size of the artificial data set is still unclear. Regarding (ii), although the GAN could produce highly realistic images, the clinical experts took them for real in only 31% of the cases. It can be concluded that image quality may play a more significant role than data size in improving the CNN-based classification result.
Few-Shot Meta-Learning for Recognizing Facial Phenotypes of Genetic Disorders
Authors: Sümer Ömer , Hellmann Fabio , Hustinx Alexander , Hsieh Tzung-Chien , André Elisabeth , Krawitz Peter
Published: May 2023

Computer vision has useful applications in precision medicine and recognizing facial phenotypes of genetic disorders is one of them. Many genetic disorders are known to affect faces’ visual appearance and geometry. Automated classification and similarity retrieval aid physicians in decision-making to diagnose possible genetic conditions as early as possible. Previous work has addressed the problem as a classification problem; however, the sparse label distribution, having few labeled samples, and huge class imbalances across categories make representation learning and generalization harder. In this study, we used a facial recognition model trained on a large corpus of healthy individuals as a pre-task and transferred it to facial phenotype recognition. Furthermore, we created simple baselines of few-shot meta-learning methods to improve our base feature descriptor. Our quantitative results on GestaltMatcher Database (GMDB) show that our CNN baseline surpasses previous works, including GestaltMatcher, and few-shot meta-learning strategies improve retrieval performance in frequent and rare classes.