bluuebunny/crossref_metadata_2025 huggingface dataset:
Evidence of Digital Health Applications from a State-Regulated Repository for Reimbursable Health Applications in Germany
Authors: Albrecht Urs-Vito , Von Jan Ute , Lawin Dennis , Pustozerov Evgenii , Dittrich Florian
Published: May 2023

17 RCTs for 15 digital health applications (DiGA) permanently listed in the state-regulated register were analyzed descriptively for methodological study aspects relevant to evidence analysis. The analysis revealed that several underlying studies had limitations, at least worthy of discussion, in terms of their power concerning sample size, intervention and control group specifications, drop-out rates, and blinding.
Factors to Consider when Introducing Digital Social Activities to Older Persons with Home Care
Authors: Blusi Madeleine , Lindgren Helena , Nilsson Ingeborg
Published: May 2023

Social isolation and loneliness have become everyday concerns for populations all over the world as these factors are affecting both physical and mental health in a negative way. Feelings of isolation and loneliness are increasingly acknowledged as a health risk among older persons. ICTs have been recognized as effective tools to combat social isolation among older people. The aim of this study was to explore factors of significance when introducing a tablet-based system providing digital social activities for older persons with home care. Participants were 17 persons, age 70 and older, who lived alone and had assistance from home care. This exploratory study used cross-sectional qualitative data analyzed through thematic analysis. Three themes were generated: 1) lacking vocabulary related to the context, 2) intuitive user interface may replace extensive instructions and 3) unwillingness to commit to a pre-defined measure of performance.
A Web-Based Public Health Intervention for Addressing Vaccine Misinformation: Analysis of Learner Engagement and Shift in Hesitancy to Vaccinate
Authors: Powell Leigh , Nour Radwa , Al Suwaidi Hanan , Zary Nabil
Published: May 2023

Web-based public health interventions can be a useful tool for disseminating evidence-based information to the public. However, completion rates are traditionally low, and misinformation often travels at a faster pace than evidence-based sources. This study describes the design of a web-based public health intervention to address COVID-19 vaccine hesitancy. A quasi-experimental approach was used in which a validated instrument, the Adult Vaccine Hesitancy Survey, was given to learners both pre and post intervention to observe any change in attitude towards vaccination. Our pilot observed a small positive shift in vaccine hesitancy and experienced higher than average completion rates. By integrating motivational learning design into public health interventions we increase the likelihood that learners finish the entire intervention, creating greater chance for positive behavior change.
Danish Citizens’ Expectations for mHealth Prescription
Authors: Jensen Victor Vadmand , Sørensen Tom Høg , Kristensen Alice Ørsted , Eriksen Jeppe
Published: May 2023

Use of mHealth in Denmark is growing, and prescription structures for mHealth apps are a political goal. In this pilot survey study, respondents generally perceive their mHealth use as beneficial, which correlates with their frequency of use. Willingness to substitute traditional treatments for prescribed mHealth varies based on type of substituted treatment.
Automated Monitoring Reports of the Activity of the French National Professional Suicide Prevention Helpline
Authors: Lamer Antoine , Saint-Dizier Chloé , Fares Emile , Debien Christophe , Cleva Elise , Whatelet Marielle , Notredame Charles-Edouard
Published: May 2023

The French Professional Suicidal Helpline 3114 was launched on October 1st, 2021. The objective of this study was to implement automated reports of the activity of the suicidal helpline. We developed automated reports and presentations with Rmarkdown. Two formats were developed, national reports to present for a funding agency and regional reports for each calling center. These reports fulfill a critical need to adjust call distribution patterns, identify problems, adjust communication across the territory and ensure that 3114 is delivering the service it is supposed to provide.
Health Informatics Training Program in Low and Middle Income Countries
Authors: Lulejian Armine , Kysh Lynn , Galoyan Tamara , Dickhoner James
Published: May 2023

We developed the first health informatics training program in Armenia and in the Caucasus region. The training program consists of four educational pillars, including a bootcamp, an individualized training program, a capstone, and a scholarly project. We conducted surveys and qualitative interviews to evaluate the training program. With trending positive results we acknowledge that it is important to understand the landscape of health informatics and conduct needs assessment prior to establishing such a training program in an LMIC.
Support of Young Talent in the GMDS
Authors: Schüttler Christina ,   GMDS Presidential Commission for the Support of Young Scientists, Ulrich Hannes
Published: May 2023

The German Association for Medical Informatics, Biometry and Epidemiology (GMDS) aims to develop subject-specific methods, which are then to be applied in collaboration with various medical domains. Furthermore, the support of young scientists is an essential field of activity of the GMDS, since the need for junior staff has increased due to the acceleration in medical digitization. A specially established Presidential Commission strives to promote young talents and scientists in the above-mentioned disciplines. For this purpose, various strategies and concepts are elaborated in regular meetings and finally implemented. These include online formats such as a lecture series on research-related topics, as well as events such as summer schools and PhD symposia.
Reduction in X-ray Retake Rate Using the Token Economy Method
Authors: Hada Hiroto , Hiragi Shusuke , Mori Yukiko , Saito Kenichi , Ai Masumi , Kuroda Tomohiro
Published: May 2023

Excessive retakes of X-ray images increase labor and material costs, as well as result in excess radiation exposure for patients and a long waiting time. In this study, we evaluated the effectiveness of the token economy method as a management method for reducing X-ray retake rate among radiology technicians. The results showed a 2.5% reduction in retake rate, indicating the effectiveness of our method. In addition, we suggest that the token-economy-based approach can be applied to other hospital management problems.
Digital Skills Among Elderly Care Workers
Authors: Kivekäs Eija , Kinnunen Ulla-Mari , Ikonen Johanna , Saranto Kaija
Published: May 2023

The aim of our study was to determine the current status of digital skills of elderly care workers (n=169) at well-being services. A survey was sent to elderly services providers in the municipalities (n=15) of North Savo, Finland. Respondents’ experience as client information systems users was higher than that as assistive technologies users. Devices supporting independent living were seldom used, but safety devices and alarm monitoring were used daily.
The Effect of Physicians’ Acknowledgement of Clinical Decision Support Systems Generated Alerts on Patient Diabetes Management in a Primary Care Setting
Authors: Faysel Mohammad A. , Miller Tameca , Singer Jonathan , Cummings Caroline
Published: May 2023

The purpose of the study is to evaluate whether clinician’s acknowledgement and adherence to Clinical Best Practice Advisories (BPA) system’s alerts improves the outcome of patients with chronic diabetes. We used deidentified clinical data of elderly (65 or older) diabetes patients with hemoglobin A1C (HbA1C) >= 6.5 that were extracted from the clinical database of a multi-specialty outpatient clinic that also provides primary care services. We performed paired ttest to evaluate whether clinician’s acknowledgement and adherence to BPA system’s alert has any impact on patients’ HbA1C management. Our findings showed that the average HbA1C values improved for patients whose alerts were acknowledged by their clinicians. For the group of patients whose BPA alerts were ignored by their clinicians, we found clinicians’ acknowledgement and adherence to BPA alerts for chronic diabetes patient management did not have a significant negative effect on improvement in patient outcome.
PDSS: A Pharmacological Decision Support System for Diabetics Patients with COVID-19
Authors: Amaya-Rodriguez Isabel , Larburu Nekane , Martinez-Herrera María Rollán , Rebescher Kristin , Macia Iván , Armengol De La Hoz Miguel A. , Rubio-Escudero Cristina , Garin-Muga Alba
Published: May 2023

With the advent of SARS-CoV-2, several studies have shown that there is a higher mortality rate in patients with diabetes and, in some cases, it is one of the side effects of overcoming the disease. However, there is no clinical decision support tool or specific treatment protocols for these patients. To tackle this issue, in this paper we present a Pharmacological Decision Support System (PDSS) providing intelligent decision support for COVID-19 diabetic patient treatment selection, based on an analysis of risk factors with data from electronic medical records using Cox regression. The goal of the system is to create real world evidence including the ability to continuously learn to improve clinical practice and outcomes of diabetic patients with COVID-19.
Clinical Decision Support: Evaluating the Development of a Tool for Nurses
Authors: Christensen Bente , Rotegård Ann Kristin
Published: May 2023

VAR Healthcare is a clinical decision support system for nurses that aspires to become even more advanced. By applying The Five Rights model, we have evaluated the status and direction of its development to bring potential lacks or barriers into the fore. The evaluation shows that ensuring APIs that will allow the nurses to combine the assets of VAR Healthcare with information on individual patients from EPRs would bring advanced decision support to nurses. This would adhere to all the principles of the five rights model.
Extracting Temporal Relationships in EHR: Application to COVID-19 Patients
Authors: Molina Carlos , Prados-Suarez Belén
Published: May 2023

Association rules are one of the most used data mining techniques. The first proposals have considered relations over time in different ways, resulting in the so-called Temporal Association Rules (TAR). Although there are some proposals to extract association rules in OLAP systems, to the best of our knowledge, there is no method proposed to extract temporal association rules over multidimensional models in these kinds of systems. In this paper we study the adaptation of TAR to multidimensional structures, identifying the dimension that establishes the number of transactions and how to find time relative correlations between the other dimensions. A new method called COGtARE is presented as an extension of a previous approach proposed to reduce the complexity of the resulting set of association rules. The method is tested in application to COVID-19 patients data.
The PrescIT Knowledge Graph: Supporting ePrescription to Prevent Adverse Drug Reactions
Authors: Chytas Achilleas , Dimitriadis Vlasios , Giannios Giorgos , Grammatikopoulou Margarita , Nikolaidis George , Pliatsika Jenny , Zachariadou Martha , Karanikas Haralampos , Kompatsiaris Ioannis , Nikolopoulos Spiros , Natsiavas Pantelis
Published: May 2023

Adverse Drug Reactions (ADRs) are an important public health issue as they can impose significant health and monetary burdens. This paper presents the engineering and use case of a Knowledge Graph, supporting the prevention of ADRs as part of a Clinical Decision Support System (CDSS) developed in the context of the PrescIT project. The presented PrescIT Knowledge Graph is built upon Semantic Web technologies namely the Resource Description Framework (RDF), and integrates widely relevant data sources and ontologies, i.e., DrugBank, SemMedDB, OpenPVSignal Knowledge Graph and DINTO, resulting in a lightweight and self-contained data source for evidence-based ADRs identification.
Convolutional Neural Networks for Optical Discrimination Between Histological Types of Colorectal Polyps Based on White Light Endoscopic Images
Authors: Panteris Vasileios , Feretzakis Georgios , Karantanos Panagiotis , Kalles Dimitris , Verykios Vassilios V. , Panoutsakou Maria , Karagianni Eirini , Zoubouli Christina , Vgenopoulou Stefani , Pierrakou Aikaterini , Theodorakopoulou Maria , Papalois Apostolos E. , Thomaidis Thomas , Dalainas Ilias , Kouroumalis Elias
Published: May 2023

The objective of this study was to compare different convolutional neural networks (CNNs), as employed in a Python-produced deep learning process, used on white light images of colorectal polyps acquired during the process of a colonoscopy, in order to estimate the accuracy of the optical recognition of particular histologic types of polyps. The TensorFlow framework was used for Inception V3, ResNet50, DenseNet121, and NasNetLarge, which were trained with 924 images, drawn from 86 patients.
Blood Vessel Segmentation Using U-Net for Glaucoma Diagnosis with Limited Data
Authors: Schiesser Lukas , Storp Jens Julian , Yildirim Kemal , Varghese Julian , Eter Nicole
Published: May 2023

Glaucoma is one of the leading causes of blindness worldwide. Therefore, early detection and diagnosis are key to preserve full vision in patients. As part of the SALUS study, we create a blood vessel segmentation model based on U-Net. We trained U-Net on three different loss functions and used hyperparameter tuning to find their optimal hyperparameters for each loss function. The best models for each of the loss functions achieved an accuracy of over 93%, Dice scores around 83% and Intersection over Union scores over 70%. They each identify large blood vessels reliably and even recognize smaller blood vessels in the retinal fundus images and thus pave the way for improved glaucoma management.
Decision Support for Signal Assessment of Large Case Series in Pharmacovigilance
Authors: Quirant Lucy , Sandberg Lovisa , Barrett Jim W. , Ellenius Johan
Published: May 2023

In pharmacovigilance, signal assessment of a medicinal product and adverse event can involve reviewing prohibitively large numbers of case reports. A prototype of a decision support tool guided by a needs assessment was developed to help manual review of many reports. In a preliminary qualitative evaluation, users said the tool was easy to use, improved efficiency and provided new insights.
Explainable Graph Neural Networks for Atherosclerotic Cardiovascular Disease
Authors: Lundström Jens , Hashemi Atiye Sadat , Tiwari Prayag
Published: May 2023

Understanding the aspects of progression for atherosclerotic cardiovascular disease and treatment is key to building reliable clinical decision-support systems. To promote system trust, one step is to make the machine learning models (used by the decision support systems) explainable for clinicians, developers, and researchers. Recently, working with longitudinal clinical trajectories using Graph Neural Networks (GNNs) has attracted attention among machine learning researchers. Although GNNs are seen as black-box methods, promising explainable AI (XAI) methods for GNNs have lately been proposed. In this paper, which describes initial project stages, we aim at utilizing GNNs for modeling, predicting, and exploring the model explainability of the low-density lipoprotein cholesterol level in long-term atherosclerotic cardiovascular disease progression and treatment.
Diagnosis Support for Rare Diseases Using Phenotypic Profiles
Authors: Noll Richard , Schaaf Jannik , Von Wagner Michael , Storf Holger
Published: May 2023

The common occurrence of characteristic symptoms can be used to infer diagnoses. The aim of this study is to show how syndrome similarity analysis using given phenotypic profiles can help in the diagnosis of rare diseases. HPO was used to map syndromes and phenotypic profiles. The system architecture described is planned to be implemented in a clinical decision support system for unclear diseases.
An Integrated Approach to Automated Diagnosis of Cervical Intraepithelial Neoplasia in Digital Histology Images
Authors: Brosnan Bríd , Skarga-Bandurova Inna , Biloborodova Tetiana , Skarha-Bandurov Illia
Published: May 2023

The study proposes an integrated approach to automated cervical intraepithelial neoplasia (CIN) diagnosis in epithelial patches extracted from digital histology images. The model ensemble, combined CNN classifier, and highest-performing fusion approach achieved an accuracy of 94.57%. This result demonstrates significant improvement over the state-of-the-art classifiers for cervical cancer histopathology images and promises further improvement in the automated diagnosis of CIN.
Digital Information Management for Advanced Practice Nursing: Needs Assessment
Authors: Laine Heidi , Heikkilä Asta , Peltonen Laura-Maria , Smed Jouni
Published: May 2023

The aim of this pilot study was to explore needs related to a quality dashboard for advanced practice nursing to support quality management in a Finnish university hospital.
Usability Engineering of Dynamic Biosignal Displays Using Ventilation Data
Authors: Coldewey Beatrice , Marie Klöckener Anne , Göbel Christof , Röhrig Rainer , Lipprandt Myriam
Published: May 2023

The aim of this work is to develop and evaluate a multi-stage procedure model for the identification of use problems and optimization of usability using biosignal data. The concept is divided into 5 steps: 1. static analysis of data to identify use problems; 2. conducting interviews within the context of use and requirements analysis to investigate problems in more detail; 3. developing new interface concepts to implement the requirements and a prototype of an interface including dynamic visualization of data; 4. formative evaluation using an unmoderated remote usability test; 5. usability test with realistic scenarios and influencing factors in the simulation room. The concept was evaluated in the ventilation setting as an example. The procedure allowed the identification of use problems in the ventilation of patients as well as the development of suitable concepts and their evaluation to counteract use problems. To relieve users, ongoing analyses of biosignals with respect to the use problem are to be carried out. To overcome technical barriers, further development is needed in this area.
Methodology for the Description of Socio-Technical Systems: A Case Study Approach
Authors: Lipprandt Myriam ,   DESIREE Study Group, Klausen Andrea D. , Röhrig Rainer
Published: May 2023

The ethical implications and regulatory requirements of AI applications and decision support systems are generally the subjects of interdisciplinary research. Case studies are a suitable means to prepare AI applications and clinical decision support systems for research. This paper proposes an approach that describes a procedure model and a categorization of the contents of cases for socio-technical systems. The developed methodology was applied to three cases and serve the researchers in the DESIREE research project as a basis for qualitative research and for ethical, social, and regulatory analyses.
How Do Informaticians and IT-Architects Collaborate, or Not? A Case Study from a Public Health Care Provider
Authors: Rossander Anna
Published: May 2023

Despite years of work from both informaticians and IT-architects interoperability within healthcare is still low. This explorative case study performed on a well-staffed public health care provider shows that the involved roles were unclear, processes did not include each other, and that tooling was incompatible. However, interest in collaboration was high and technical advances and inhouse development were seen as incentives for increased collaboration.
Healthcare Leaders’ Perceptions of the Usefulness of AI Applications in Clinical Work: A Qualitative Study
Authors: Petersson Lena , Svedberg Petra , Nygren Jens M. , Larsson Ingrid
Published: May 2023

Artificial intelligence (AI) is often presented as a technology that changes healthcare and is useful in clinical work in disease prediction, diagnosis, treatment effectiveness, and precision health. This study aimed to explore healthcare leaders’ perceptions of the usefulness of AI applications in clinical work. The study was based on qualitative content analysis. Individual interviews were conducted with 26 healthcare leaders. The usefulness of AI applications in clinical care was described in terms of expected benefits for 1) patients as supporting individualized self-management and person-centered information support tools 2) healthcare professionals in terms of providing decision-support in diagnostics, risk assessments, treatment recommendations, warning systems, and as a new colleague supporting the clinical work, and 3) organizations as providing patient safety and decision-support in prioritizing healthcare resources in organizing healthcare.
Towards Implementation of a Home-Based Phantom Limb Pain Treatment Facilitated by Textile-Electrode System – A Case Study
Authors: Björkquist Anna , Guo Li , Kristoffersen Morten B. , Novoa Maria M. , Ortiz-Catalan Max , Sandsjö Leif
Published: May 2023

This case study reports the use of a new textile-electrode system for self-administered Phantom Motor Execution (PME) treatment at home in one patient with Phantom Limb Pain (PLP). In follow-up interviews, the patient reported reduced pain, increased mobility, and improved mental health, and aspects such as motivation, usability, support, and treatment outcome, could be recognized from an earlier study as crucial for successful implementation and adoption of the home-based long-term treatment. The findings are of interest to developers, providers, users, and researchers planning home-based clinical studies and/or scenarios based on technology-assisted treatment.
Model and Strategy for Predicting and Discovering Drug-Drug Interactions
Authors: Mouazer Abdelmalek , Boudegzdame Nada , Sedki Karima , Tsopra Rosy , Lamy Jean-Baptiste
Published: May 2023

Taking several medications at the same time is an increasingly common phenomenon in our society. The combination of drugs is certainly not without risk of potentially dangerous interactions. Taking into account all possible interactions is a very complex task as it is not yet known what all possible interactions between drugs and their types are. Machine learning based models have been developed to help with this task. However, the output of these models is not structured enough to be integrated in a clinical reasoning process on interactions. In this work, we propose a clinically relevant and technically feasible model and strategy for drug interactions.
Building an Ontology for Traditional Medicine by Comparing Traditional Medicine Information of Chapter 26 of ICD-11 with SNOMED CT
Authors: Lundholm Tom , Bonacina Stefano
Published: May 2023

Chapter 26 of the 11th revision of the International Classification of Diseases (ICD-11-CH26) has introduced Traditional Medicine knowledge for use and integration with Western Medicine. Traditional Medicine is the use of beliefs, theories, and experiences to provide healing and care. The amount of information on Traditional Medicine in Systematized Nomenclature of Medicine – Clinical Terms (SCT), the world’s most comprehensive health terminology, is unclear. The purpose of this study is to address this unclarity and investigate to which extent the concepts of ICD-11-CH26 can be found in SCT. If a concept from ICD-11-CH26 has a corresponding, or similar, concept in SCT, the hierarchical structure of the concepts has been compared. Then, an ontology of Traditional Chinese Medicine using the concepts of SCT will be developed.
The Prevalence of mRNA Related Discussions During the Post-COVID-19 Era
Authors: Kokkinakis Dimitrios , Bruinsma Bastiaan , Hammarlin Mia-Marie
Published: May 2023

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy and skepticism are raising serious concerns for a portion of the population in many countries, including Sweden. In this study, we use Swedish social media data and structural topic modeling to automatically identify mRNA-vaccine related discussion themes and gain deeper insights into how people’s refusal or acceptance of the mRNA technology affects vaccine uptake. Our point of departure is a scientific study published in February 2022, which seems to once again sparked further suspicion and concern and highlight the necessity to focus on issues about the nature and trustworthiness in vaccine safety. Structural topic modelling is a statistical method that facilitates the study of topic prevalence, temporal topic evolution, and topic correlation automatically. Using such a method, our research goal is to identify the current understanding of the mechanisms on how the public perceives the mRNA vaccine in the light of new experimental findings.
SapBERT-Based Medical Concept Normalization Using SNOMED CT
Authors: Abdulnazar Akhila , Kreuzthaler Markus , Roller Roland , Schulz Stefan
Published: May 2023

Word vector representations, known as embeddings, are commonly used for natural language processing. Particularly, contextualized representations have been very successful recently. In this work, we analyze the impact of contextualized and non-contextualized embeddings for medical concept normalization, mapping clinical terms via a k-NN approach to SNOMED CT. The non-contextualized concept mapping resulted in a much better performance (F1-score = 0.853) than the contextualized representation (F1-score = 0.322).
Health Research Requires Efficient Platforms for Data Collection from Personal Devices
Authors: Johannessen Erlend , Henriksen André , Årsand Eirik , Horsch Alexander , Johansson Jonas , Hartvigsen Gunnar
Published: May 2023

Data from consumer-based devices for collecting personal health-related data could be useful in diagnostics and treatment. This requires a flexible and scalable software and system architecture to handle the data. This study examines the existing mSpider platform, addresses shortcomings in security and development, and suggests a full risk analysis, a more loosely coupled component- based system for long term stability, better scalability, and maintainability. The goal is to create a human digital twin platform for an operational production environment.
OpenDataPsy: An Open-Data Repository with Standardized Storage and Description for Research in Psychiatry
Authors: Saint-Dizier Chloé , Lamer Antoine , Zaanouar Majda , Amariei Alina , Quindroit Paul
Published: May 2023

Sharing health data could avoid duplication of effort in data collection, reduce unnecessary costs in future studies, and encourage collaboration and data flow within the scientific community. Several repositories from national institutions or research teams have making their datasets available. These data are mainly aggregated at spatial or temporal level, or dedicated to a specific field. The objective of this work is to propose a standardized storage and description of open datasets for research purposes. For this, we selected 8 publicly accessible datasets, covering the fields of demographics, employment, education and psychiatry. Then, we studied the format, nomenclature (i.e., files and variables names, modalities of recurrent qualitative variables) and descriptions of these datasets and we proposed on common and standardized format and description. We made available these datasets in an open gitlab repository. For each dataset, we proposed the raw data file in its original format, the cleaned data file in csv format, the variables description, the data management script and the descriptive statistics. Statistics are generated according to the type of variables previously documented. After one year of use, we will evaluate with the users if the standardization of the data sets is relevant and how they use the dataset in real life.
Identification of Subphenotypes of Opioid Use Disorder Using Unsupervised Machine Learning
Authors: Shah-Mohammadi Fatemeh , Finkelstein Joseph
Published: May 2023

This paper aimed to detect the latent clusters of patients with opioid use disorder and to identify the risk factors affecting drug misuse using unsupervised machine learning. The cluster with the highest proportion of successful treatment outcomes was characterized by the highest percentage of employment rate at admission and discharge, the highest percentage of patients who also recovered from alcohol and other drug co-use, and the highest proportion of patients who recovered from untreated health issues. Longer participation in opioid treatment programs was associated with the highest proportion of treatment success.
Prediction of Mental Health Support of Employee Perceiving by Using Machine Learning Methods
Authors: Jamalirad Hossein , Jajroudi Mahdie
Published: May 2023

Employees’ mental health addresses concerns in the technology industry phenomenon. Machine Learning (ML) approaches show promise in predicting mental health problems and identifying related factors. This study used three machine learning models on OSMI 2019 dataset: MLP, SVM, and Decision Tree. Five features are extracted by permutation ML’s method on the dataset. The results indicate that the models have been reasonably accurate. Moreover, they could effectively support predicting employee mental health comprehension in the technology industry.
Comparing Responses to COVID-19 Across Institutions: Conceptualization of an Emergency Response Maturity Model
Authors: Senathirajah Yalini , Kaufman David , Borycki Elizabeth , Kushniruk Andre , Cato Kenrick
Published: May 2023

The impact of Covid-19 on hospitals was profound, with many lower-resourced hospitals’ information technology resources inadequate to efficiently meet the new needs. We interviewed 52 personnel at all levels in two New York City hospitals to understand their issues in emergency response. The large differences in IT resources show the need for a schema to classify hospital IT readiness for emergency response. Here we propose a set of concepts and model, inspired by the Health Information Management Systems Society (HIMSS) maturity model. The schema is designed to permit evaluation of hospital IT emergency readiness, permitting remediation of IT resources where necessary.
Model-Driven Dementia Prevention and Intervention Platform
Authors: Schinle Markus , Dietrich Michael , Stock Simon , Gerdes Marius , Stork Wilhelm
Published: May 2023

Most types of dementia, including Alzheimer’s disease, are not curable. However, there are risk factors, such as obesity or hypertension, that can promote the development of dementia. Holistic treatment of these risk factors can prevent the onset of dementia or delay it in its early stages. To support individualized treatment of risk factors in dementia, this paper presents a model-driven digital platform. It enables monitoring of biomarkers using smart devices from the internet of medical things (IoMT) for the target group. The collected data from such devices can be used to optimize and adjust treatment in a patient in the loop manner. To this end, providers such as Google Fit and Withings have been connected to the platform as example data sources. To achieve treatment and monitoring data interoperability with existing medical systems, internationally accepted standards such as FHIR are used. The configuration and control of the personalized treatment processes are achieved using a self-developed domain-specific language. For this language, an associated diagram editor was implemented, which allows the management of the treatment processes through graphical models. This graphical representation should help treatment providers to understand and manage these processes more easily. To investigate this hypothesis, a usability study was conducted with twelve participants. We were able to show that such graphical representations provide advantages in clarity in reviewing the system, but lack in easy set-up (compared to wizard-style systems).
U-Net-Based Segmentation of Current Imaging Biomarkers in OCT-Scans of Patients with Age Related Macular Degeneration
Authors: Yildirim Kemal , Al-Nawaiseh Sami , Ehlers Sophia , Schießer Lukas , Storck Michael , Brix Tobias , Eter Nicole , Varghese Julian
Published: May 2023

Age-related macular degeneration (AMD) is the leading cause of blindness in the Western world. In this work, the non-invasive imaging technique spectral domain optical coherence tomography (SD-OCT) is used to acquire retinal images, which are then analyzed using deep learning techniques. The authors trained a convolutional neural network (CNN) using 1300 SD-OCT scans annotated by trained experts for the presence of different biomarkers associated with AMD. The CNN was able to accurately segment these biomarkers and the performance was further enhanced through transfer learning with weights from a separate classifier, trained on a large external public OCT dataset to distinguish between different types of AMD. Our model is able to accurately detect and segment AMD biomarkers in OCT scans, which suggests that it could be useful for prioritizing patients and reducing ophthalmologists’ workloads.
V-care: An Application to Support Lifestyle Improvement in Children with Obesity
Authors: Larizza Cristiana , Quaglini Silvana , Chasseur Michelangelo , Bevolo Valentina , Zuccotti Gianvincenzo , Calcaterra Valeria
Published: May 2023

Obesity is increasing in the pediatric population and it represents an important risk factor for the life-long development of several diseases. The aim of this work is to reduce children obesity through an educational program delivered through a mobile application. Novelties of our approach are the involvement of the families in the program and a design inspired to psychological/behavioral change theories, with the aim of maximizing the chance of patients’ compliance to the program. A pilot usability and acceptability study has been performed on ten children aged 6-12 years using a questionnaire to evaluate eight system features on a Likert scale from 1 to 5. Encouraging results were obtained: mean scores were all above 3.
Reliability of IMU-Derived Gait Parameters in Foot Drop Patients
Authors: Coccia Armando , Amitrano Federica , Pagano Gaetano , Biancardi Arcangelo , Tombolini Giuseppe , D’Addio Giovanni
Published: May 2023

Foot drop is a deficit in foot dorsiflexion causing difficulties in walking. Passive ankle-foot orthoses are external devices used to support the drop foot improving gait functions. Foot drop deficits and therapeutic effects of AFO can be highlighted using gait analysis. This study reports values of the major spatiotemporal gait parameters assessed using wearable inertial sensors on a group of 25 subjects suffering from unilateral foot drop. Collected data were used to assess the test-retest reliability by means of Intraclass Correlation Coefficient and Minimum Detectable Change. Excellent test-retest reliability was found for all the parameters in all walking conditions. The analysis of Minimum Detectable Change identified the gait phases duration and the cadence as the most appropriate parameters to detect changes or improvements in subject gait after rehabilitation or specific treatment.
On the Difficulty of Predicting Engagement with Digital Health for Substance Use
Authors: Günther Franziska , Yau Christopher , Elison-Davies Sarah , Wong David
Published: May 2023

Digital interventions can be an important instrument in treating substance use disorder. However, most digital mental health interventions suffer from early, frequent user dropout. Early prediction of engagement would allow identification of individuals whose engagement with digital interventions may be too limited to support behaviour change, and subsequently offering them support. To investigate this, we used machine learning models to predict different metrics of real-world engagement with a digital cognitive behavioural therapy intervention widely available in UK addiction services. Our predictor set consisted of baseline data from routinely-collected standardised psychometric measures. Areas under the ROC curve, and correlations between predicted and observed values indicated that baseline data do not contain sufficient information about individual patterns of engagement.
Interpretable EEG-Based Emotion Recognition Using Fuzzy Cognitive Maps
Authors: Sovatzidi Georgia , Iakovidis Dimitris K.
Published: May 2023

The brain is one of the most complex parts of the human body, consisting of billions of neurons and it is involved in almost all vital functions. To study the brain functionality, Electroencephalography (EEG) is used to record the electrical activity generated by the brain through electrodes placed on the scalp surface. In this paper, an auto-constructed Fuzzy Cognitive Map (FCM) model is used for interpretable emotion recognition, based on EEG signals. The introduced model constitutes the first FCM that automatically detects the cause-and-effects relations existing among brain regions and emotions induced by movies watched by volunteers. In addition, it is simple to implement and earns the trust of the user, while providing interpretable results. The effectiveness of the model over other baseline and state-of-the-art methods is examined using a publicly available dataset.
Trajectories of Anxiety Symptoms for COVID-19 Patients Using Multimodal Data Collected from Telemedicine
Authors: Sung Sumi , Hwan Kim Su , Kim Youlim , You Mirinae , Lee Hannah , Her Soojin , Bae Ye Seul , Chie Eui Kyu
Published: May 2023

We designed and developed Remote Patient Monitoring (RPM) system specific for coronavirus (COVID-19) patients, and collected multimodal data. Using the collected data, we explored the trajectories of anxiety symptoms for 199 COVID-19 patients quarantined at home. Two classes were identified using latent class linear mixed model. Thirty-six patients showed an exacerbation of anxiety. Presence of initial psychological symptoms, pain on the start day of quarantine, and abdominal discomfort at one month after finishing the quarantine were associated with exacerbation of anxiety.
Requirements to mHealth Applications for Animal Owners: A Narrative Review
Authors: Haase Laura
Published: May 2023

A narrative literature review was conducted to determine software requirements to mHealth applications for animal owners. Results focus on securing a complete user input, providing adequately formulated information to the users and ensuring the understanding of the applications limitations.
Effects of Ankle-Foot Orthosis on Balance of Foot Drop Patients
Authors: Amitrano Federica , Coccia Armando , Pagano Gaetano , Biancardi Arcangelo , Tombolini Giuseppe , D’Addio Giovanni
Published: May 2023

Ankle-Foot Orthoses (AFOs) are common non-surgical treatments used to support foot and ankle joint when their normal functioning is compromised. AFOs have relevant impact on gait biomechanics, while scientific literature about effects on static balance is less strong and confusing. This study aims to assess the effectiveness of a plastic semi-rigid AFO in improving static balance on foot drop patients. Results underline that no significant effects on static balance is obtained on the study population when the AFO is used on the impaired foot.
OmicSDK-Transcriptomics: A Web Platform for Transcriptomics Data Analysis
Authors: Sucre Aurora , Martinez Maria , Garin-Muga Alba
Published: May 2023

Omics sciences, especially transcriptomics, have grown exponentially since the first human genome was sequenced in 2003. Different tools have been developed in the past years for the analysis of this kind of data, but many of them require specific programming knowledge to be used. In this paper, we present omicSDK-transcriptomics, the transcriptomics module of OmicSDK, a comprehensive tool for omics data analysis that combines pre-processing, annotation and visualization tools to be used with omics data. OmicSDK comprises a command-line tool and a user-friendly web solution, so researchers having different backgrounds can take advantage of all its functionalities.
Electromagnetic Fields Literature Analysis for Precision Medicine
Authors: Hawthorne Christopher , Harrison Donal , McEvoy Eva , Lopez Campos Guillermo
Published: May 2023

During the last century technological advances have increased the number of anthropogenic electromagnetic fields (EMFs) and therefore human exposures. In this work we have mined from more than 30,000 EMF-related publications the genes, diseases and molecular mechanisms associated with the exposure to six different subsets of EMFs. Results show 3653 unique disease MeSH terms and 9966 unique genes identified of which only 4340 genes are human. Overall, our approach highlights the molecular aspects of the increasing exposure to EMFs.
Mapping Exposome Derived Phenotypes into SNOMED Codes
Authors: Hawthorne Christopher , Marco Ruiz Luis , Lopez Campos Guillermo
Published: May 2023

Human phenotypes define the healthy or diseased status of an individual and they arise from the complex interactions between environmental and genetic factors. The whole set of human exposures constitute the human exposome. These exposures have multiple sources including physical and socioeconomic factors. In this manuscript we have used text mining techniques to retrieve 1295 and 1903 Human Phenotype Ontology terms associated with these exposome factors and we have subsequently mapped 83% and 90% of the HPO terms respectively) into SNOMED as a clinically actionable code. We have developed a proof-of-concept approach to facilitate the integration of exposomic and clinical data
Child Fostering Care: Kafala in Western Countries
Authors: Abis Alessandra
Published: May 2023

After a survey on the essential features of the Islamic child foster care called kafala, the present Chapter investigates how domestic legal systems of Western States have dealt with it, particularly concerning to the right of family reunification and to intercountry adoptions. The Chapter also is aimed at highlighting the consequences of the recognition of the kafala related to the religious freedom of the immigrant’s family, with a special concern to intergenerational transmission of religious values and the religious education of children in host countries.
Intergenerational Processes and Diasporic Religious Identity Among Immigrant Coptic Families
Authors: Giuliani Cristina , Regalia Camillo
Published: May 2023

Considering the scarcity of psychological studies on Middle Eastern Christian immigrant families experiences, this chapter aims to explore the post-migration experience of Coptic Orthodox families immigrated from Egypt to Italy and chooses to adopt a family intergenerational perspective to compare narratives of two different family generations (first-generation parents and their second-generation adolescent children). Based on empirical data from 10 Coptic Orthodox families, for a total of 30 interviewed participants (10 first-generation fathers, 10 first-generation mothers, and 10 second-generation adolescent children), the chapter reveals that religiosity in its intertwined individual and social expressions is a salient part of interviewees’ everyday life and an essential source of resilience. However, identity-specific content reveals differences when comparing parents and children’s narratives. While a “diasporic” religious identity seems to emerge among first-generation parents, religious identity among children emphasizes the opposition with a highly secularized and “threatening” Italian society.
New Muslim Generation in Italy: Between Believing and Belonging
Authors: Branca Paolo , Cuciniello Antonio
Published: May 2023

The growing number of “new generations” of Muslims in Western countries is not only developing quantitatively into a complex phenomenon, but also implies gradual but important transformations within the Islamic communities (starting from families and mosques) and in society as a whole. Only a few of these dynamics are described and studied and rarely with a direct knowledge of the people concerned by them.
Intelligence model for Alzheimer’s disease detection with optimal trained deep hybrid model
Authors: RS Rajasree , Rajakumari S Brintha
Published: May 2023

Alzheimer’s disease (AD), a neurodegenerative disorder, is the most common cause of dementia and continuing cognitive deficits. Since there are more cases each year, AD has grown to be a serious social and public health issue. Early detection of the diagnosis of Alzheimer’s and dementia disease is crucial, as is giving them the right care. The importance of early AD diagnosis has recently received a lot of attention. The patient cannot receive a timely diagnosis since the present methods of diagnosing AD take so long and are so expensive. That’s why we created a brand-new AD detection method that has four steps of operation: pre-processing, feature extraction, feature selection, and AD detection. During the pre-processing stage, the input data is pre-processed using an improved data normalization method. Following the pre-processing, these pre-processed data will go through a feature extraction procedure where features including statistical, enhanced entropy-based and mutual information-based features will be extracted. The appropriate features will be chosen from these extracted characteristics using the enhanced Chi-square technique. Based on the selected features, a hybrid model will be used in this study to detect AD. This hybrid model combines classifiers like Long Short Term Memory (LSTM) and Deep Maxout neural networks, and the weight parameters of LSTM and Deep Maxout will be optimized by the Self Updated Shuffled Shepherd Optimization Algorithm (SUSSOA). Our Proposed SUSSOA-based method’s statistical analysis of best values such as 57%, 53%, 28%, 25%, and 21% is higher than the other models like SSO, BMO, HGS, BRO, BES, and ISSO respectively.
Optimal hybrid classification model for event recommendation system
Authors: BN Nithya , Geetha D. Evangelin , Kumar Manish
Published: May 2023

There is a growing need for recommender systems and other ML-based systems as an abundance of data is now available across all industries. Various industries are currently using recommender systems in slightly different ways. These programs utilize algorithms to propose appropriate products to consumers based on their prior choices and interactions. Moreover, Systems for recommending events to users suggest pertinent happenings that they might find interesting. As opposed to an object recommender that suggests books or movies; event-based recommender systems typically require distinct algorithms. A developed event recommendation method is introduced which includes two stages: feature extraction and recommendation. In stage, I, a Set of features like personal willingness, community willingness, informative content, edge weight, and node interest degree are extracted. Stage II of the event recommendation system performs a hybrid classification by combining LSTM and CNN. In the LSTM classifier, optimal tuning is done by Improvised Cat and Mouse optimization (ICMO) algorithm. The results of the ICMO technique at an 80% training percentage have the maximum sensitivity value of 95.19%, whereas those of the existing approaches SSA, DINGO, BOA, and CMBO have values of 93.89%, 93.35%, 92.36%, and 92.24%. Finally, the best result is then determined by evaluating the whole performance.