bluuebunny/crossref_metadata_2025 huggingface dataset:
User Satisfaction with an AI System for Chest X-Ray Analysis Implemented in a Hospital’s Emergency Setting
Authors: Rabinovich Diego , Mosquera Candelaria , Torrens Pierina , Aineseder Martina , Benitez Sonia
Published: May 2022

The acceptance of artificial intelligence (AI) systems by health professionals is crucial to obtain a positive impact on the diagnosis pathway. We evaluated user satisfaction with an AI system for the automated detection of findings in chest x-rays, after five months of use at the Emergency Department. We collected quantitative and qualitative data to analyze the main aspects of user satisfaction, following the Technology Acceptance Model. We selected the intended users of the system as study participants: radiology residents and emergency physicians. We found that both groups of users shared a high satisfaction with the system’s ease of use, while their perception of output quality (i.e., diagnostic performance) differed notably. The perceived usefulness of the application yielded positive evaluations, focusing on its utility to confirm that no findings were omitted, and also presenting distinct patterns across the two groups of users. Our results highlight the importance of clearly differentiating the intended users of AI applications in clinical workflows, to enable the design of specific modifications that better suit their particular needs. This study confirmed that measuring user acceptance and recognizing the perception that professionals have of the AI system after daily use can provide important insights for future implementations.
Scaling AI Projects for Radiology – Causes and Consequences
Authors: Ellingsen Gunnar , Silsand Line , Severinsen Gro Hilde , Linstad Line
Published: May 2022

Artificial intelligence (AI) for radiology has the potential to handle an ever-increasing volume of imaging examinations. However, the implementation of AI for clinical practice has not lived up to expectations. We suggest that a key problem with AI projects in radiology is that high expectations associated with new and unproven AI technology tend to scale the projects in ways that challenge their anchoring in local practice and their initial purpose of serving local needs. Empirically, we focus on the procurement of an AI solution for radiology practice at a large health trust in Norway where it was intended that AI technology would be used to process the screening of images more effectively. Theoretically, we draw on the information infrastructure literature, which is concerned with scaling innovative technologies from local settings, with a limited number of users, to broad-use contexts with many users.
Towards a Generic Description Schema for Clinical Decision Support Systems
Authors: Vogel Stefan , Krefting Dagmar
Published: May 2022

Many clinical decision support systems (CDSS) have shown good performance in the research context, but only a few have been brought into routine care. Analysis of success or failure factors in the design of CDSS may support translation from development to routine care by guiding CDSS design and development along these factors. In this work, we propose a schema to describe CDSS designs in a consistent way. We focus on design criteria with the aim to investigate the observed translation gap in CDSS. Existing description models on different aspects relevant for CDSS are combined to a comprehensive schema that allows description and comparison of CDSS without limitation to the domain or architecture.
Applying Artificial Intelligence Privacy Technology in the Healthcare Domain
Authors: Goldsteen Abigail , Farkash Ariel , Moffie Micha , Shmelkin Ron
Published: May 2022

Regulations set out strict restrictions on processing personal data. ML models must also adhere to these restrictions, as it may be possible to infer personal information from trained models. In this paper, we demonstrate the use of two novel AI Privacy tools in a real-world healthcare application.
Explainable Artificial Intelligence in Ambulatory Digital Dementia Screenings
Authors: Schinle Markus , Erler Christina , Hess Maximilian , Stork Wilhelm
Published: May 2022

Recently, digital apps have entered the market to enable the early diagnosis of dementia by offering digital dementia screenings. Some of these apps use Machine Learning (ML) to predict cognitive impairment. The aim of this work is to find explanations for the predictions of such a mobile application called DemPredict using methods from the field of Explainable Artificial Intelligence (XAI). In order to evaluate which method is best suited, different XAI approaches are used and compared. However, the comparability of the results is a key challenge. By evaluating the trustworthiness, stability, and computation time of the methods, it is possible to identify the optimal XAI approaches for the respective algorithms.
Behavioral Segmentation for Enhanced Peer-to-Peer Patient Education
Authors: Paglialonga Alessia , Theal Rebecca , Barber David , Kyba Robert , Guergachi Aziz , Keshavjee Karim
Published: May 2022

The aim of this study was to develop a peer-to-peer virtual intervention for patients with type 2 diabetes from different segments: patients who take several medications (medication group), patients who do not take diabetes medications (lifestyle group), and a mixed group. Preliminary results showed that patients in the lifestyle group were interested in preventive strategies, reporting better learning experience and higher motivation than those in the medication group. Future research is needed to design approaches tailored to patients in the medication group.
Robust Random Forest-Based All-Relevant Feature Ranks for Trustworthy AI
Authors: Pfeifer Bastian , Holzinger Andreas , Schimek Michael G.
Published: May 2022

Feature selection is a fundamental challenge in machine learning. For instance in bioinformatics, it is essential when one wishes to detect biomarkers. Tree-based methods are predominantly used for this purpose. In this paper, we study the stability of the feature selection methods BORUTA, VITA, and RRF (regularized random forest). In particular, we investigate the feature ranking instability of the associated stochastic algorithms. For stabilization of the feature ranks, we propose to compute consensus values from multiple feature selection runs, applying rank aggregation techniques. Our results show that these consolidated features are more accurate and robust, which helps to make practical machine learning applications more trustworthy.
Generation and Evaluation of Synthetic Data in a University Hospital Setting
Authors: Kaabachi Bayrem , Despraz Jérémie , Meurers Thierry , Prasser Fabian , Raisaro Jean Louis
Published: May 2022

In this study, we propose a unified evaluation framework for systematically assessing the utility-privacy trade-off of synthetic data generation (SDG) models. These SDG models are adapted to deal with longitudinal or tabular data stemming from electronic health records (EHR) containing both discrete and numeric features. Our evaluation framework considers different data sharing scenarios and attacker models.
Prediction of Hospitalization Using Machine Learning for Emergency Department Patients
Authors: Feretzakis Georgios , Sakagianni Aikaterini , Loupelis Evangelos , Kalles Dimitris , Panteris Vasileios , Tzelves Lazaros , Chatzikyriakou Rea , Trakas Nikolaos , Kolokytha Stavroula , Batiani Polyxeni , Rakopoulou Zoi , Tika Aikaterini , Petropoulou Stavroula , Dalainas Ilias , Kaldis Vasileios
Published: May 2022

The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area under the receiver operating characteristic curve (AUC ROC).
Metadata Definition in Registries: What Is a Data Element?
Authors: Stausberg Jürgen , Harkener Sonja , Burgmer Markus , Engel Christoph , Finger Robert , Heinz Carsten , Jenetzky Ekkehart , Martin David , Rupp Rüdiger , Schoenthaler Martin , Schuld Christian , Suwelack Barbara , Wegner Jeannine
Published: May 2022

Observational research benefits from a rich methodological foundation of registry development and operation published in international and national guidelines. Metadata management is an essential part of registry implementation based on concepts of data elements and value sets. The metadata from six German registries revealed vastly divergent interpretations of the concept of data elements. The different perspectives of research questions, data acquisition and data storage were all represented in the registries’ catalogs of data elements. Consequently, the whole life cycle of a registry needs to be accompanied by a catalog of data elements, which has to be continuously adapted to the changing perspectives. A standard for the representation of those metadata is still missing. The FAIR Guiding Principles introduce important methodological requirements, but the tools for their fulfillment in respect to the management of metadata are still in its infancy.
Linking EMR Data to REDCap: Implementation in the SOLKID Register
Authors: Brix Tobias J. , Greulich Leonard , Janssen Alice , Riepenhausen Sarah , Neuhaus Philipp , Oehm Johannes , Wegner Jeannine , Suwelack Barbara , Storck Michael , Varghese Julian
Published: May 2022

Secondary use, the reuse of medical patient data stored during routine care in the hospital’s electronic medical records (EMR) for research purpose is common, especially for registers and pragmatic trials. Often the medical data items are copied manually from the EMR into the used research database. This process is time consuming and error prone. In the “Safety of the Living Kidney Donor – The German National Register” (SOLKID-GNR), laboratory results gathered during control check-ups of the living donors before and after the transplantation are to be transferred from the EMR into the electronic data capture system REDCap of the register. In this work, we present our approach of realizing an automated transfer of time-dependent laboratory results from the EMR of the University Hospital of Münster to REDCap. A challenge lies in the multi-center structure of SOLKID-GNR. The participating transplant centers are using different EMR systems, which requires a flexible architecture design. In addition, we aimed to support reuse of the implementation for other research settings with other medical data items of interest.
Suitability of Reusing Pre-Doctoral Student Activity Data from an Educational Information System for Quality Measures of Caries Risk
Authors: Ballester Benoit , Casazza Estelle , Bukiet Frédéric , Dufour Jean-Charles
Published: May 2022

Dental caries management requires individualized follow-up and prophylaxis based on patients’ caries risk (PCR). In large pre-doctoral clinics, the use of institutional quality measures (QMs) is essential to control the quality of patient follow-up and to evaluate the need for improvement measures. The aim of this retrospective study was to evaluate the suitability of reusing student activity data for the development of QMs of caries risk. Two approaches for predicting PCR using student activity data were evaluated and compared. The first approach used the procedure codes recommended by the Dental Quality Alliance and the second used these same codes along with three educational codes. The sensitivity, specificity, overall accuracy of the two approaches were evaluated. A Receiver Operating Characteristic (ROC) curve analysis was carried out, and the areas under the ROC curve of the two approaches were compared using Delong’s test. A two-tailed P value ≤0.05 was considered statistically significant. While the two approaches were able to correctly predict PCR, the approach using both procedure and educational codes showed better predictive performance. The reuse of student activity data is an easy and robust method for the development of QMs of caries risk that can help improve monitoring and quality of patient care.
Establishing a Data Quality Baseline in the AKTIN Emergency Department Data Registry – A Secondary Use Perspective
Authors: Triefenbach Lucas ,   The Aktin Research Group, Otto Ronny , Bienzeisler Jonas , Kombeiz Alexander , Ehrentreich Saskia , Röhrig Rainer , Majeed Raphael W.
Published: May 2022

Secondary use of clinical data is an increasing application that is affected by the data quality (DQ) of its source systems. Techniques such as audits and risk-based monitoring for controlling DQ often rely on source data verification (SDV). SDV requires access to data generating systems. We present an approach to a targeted SDV based on manual input and synthetic data that is applicable in low resource settings with restricted system access. We deployed the protocol in the DQ management of the AKTIN Emergency Department Data Registry. Our targeted approach has shown to be feasible to form a DQ baseline that can be used for different DQ monitoring processes such as the identification of different error sources.
Collecting Data from a Mobile App and a Smartwatch Supports Treatment of Schizophrenia and Bipolar Disorder
Authors: Sigurðardóttir Steinunn Gróa , Islind Anna Sigridur , Óskarsdóttir María
Published: May 2022

Mental disorders affect individuals and societies around the world negatively, with the health-related burden of 32,4% out of the overall disease burden. This large part of the overall burden underlines a growing need for innovation to support the treatment of mental disorders like schizophrenia and bipolar disorder. This empirical study features two groups of patients; a group of nine patients diagnosed with bipolar disorder and a group of twelve patients diagnosed with schizophrenia. The patients in the study carry a smartwatch for six weeks, continuously collecting data into a digital health platform. Additionally, they answer five daily wellbeing questions in a mobile app. To supplement that data, they also answer a questionnaire three times over the interval and at the end of the period they attend a semi-structured interview. We offer four main aspects to consider for PGHD in mental health: i) sharing data easily with healthcare professionals, ii) being able to engage with your own PGHD, iii) the watch use can help the patients regulate routine in their daily life, iv) tonality and phrasing.
Electronic Phenotyping to Identify Patients with Arrythmia Disease from a Hospital Information System
Authors: Nakayama Masaharu , Inoue Ryusuke
Published: May 2022

Electronic phenotyping is an important method to identify a disease group by collecting clinical data from hospital information systems. This study aimed to extract accurate cases of supraventricular arrythmia, ventricular arrythmia, and bradycardia from clinical data of a hospital information system. The electronic phenotyping algorithm was improved using the machine learning method. Subsequently, it showed a higher area under the curve for prediction and higher specificity. However, the algorithm needs further improvement to classify each arrythmia disease accurately. In conclusion, phenotyping using clinical data from hospital information systems has some affinities and issues depending on the disease.
Microservices-Based Architecture to Support the Adaptive RECORDS-Trial
Authors: Nageba Ebrahim , Hilka Martin , Gozlan Rafael , Dubiel Julien , Baudoin Cendrine , Daniel Christel
Published: May 2022

Information systems used by platform trials should handle changes that are not predefined. Unfortunately, the technical architecture of most existing clinical data management systems (CDMS) do not support changes to be incorporated into an ongoing trial. Adaptive clinical trials need advanced architectural solutions setup to enable biomarker stratification and enrichment strategy necessary for the adaptive clinical trial operation. This short paper presents the microservices-based architecture solution that is used to run and support the adaptive RECORDS-Trial.
Mapping Korean National Health Insurance Pharmaceutical Claim Codes to SNOMED CT
Authors: Jung Hyesil , Park Hyeoun-Ae , Kang Hannah , Lee MinSun , Sung Sumi , Park SeulKi
Published: May 2022

The objective of this study was to map pharmaceutical claim codes to SNOMED CT and thereby facilitate multicenter collaborative research and improve semantic interoperability. The claim codes were mapped to SNOMED CT using rule-based automated and manual methods. The maps were internally validated by terminologists and a pharmacist. Finally, 80% of all claim codes were mapped to the concepts of Pharmaceutical/biologic product hierarchy in SNOMED CT. Of them, 50.6% of the codes were exactly mapped to one clinical drug branch concept.
Indexing Imaging Reports for Data Sharing: A Study of Mapping Using RadLex Playbook and LOINC
Authors: Lemordant Pierre , Mougin Fleur , Cabon Sandie , Gandon Yves , Bouzillé Guillaume , Cuggia Marc
Published: May 2022

New use cases and the need for quality control and imaging data sharing in health studies require the capacity to align them to reference terminologies. We are interested in mapping the local terminology used at our center to describe imaging procedures to reference terminologies for imaging procedures (RadLex Playbook and LOINC/RSNA Radiology Playbook). We performed a manual mapping of the 200 most frequent imaging report titles at our center (i.e. 73.2% of all imaging exams). The mapping method was based only on information explicitly stated in the titles. The results showed 57.5% and 68.8% of exact mapping to the RadLex and LOINC/RSNA Radiology Playbooks, respectively. We identified the reasons for the mapping failure and analyzed the issues encountered.
Preprocessing to Address Bias in Healthcare Data
Authors: Seker Emel , Talburt John R. , Greer Melody L.
Published: May 2022

Multimorbidity, having a diagnosis of two or more chronic conditions, increases as people age. It is a predictor used in clinical decision-making, but underdiagnosis in underserved populations produces bias in the data that support algorithms used in the healthcare processes. Artificial intelligence (AI) systems could produce inaccurate predictions if patients have multiple unknown conditions. Rural patients are more likely to be underserved and also more likely to have multiple chronic conditions. In this study, data collected during the course of care in a centrally located academic hospital, multimorbidity decreased with rurality. This decrease suggests a bias against rural patients for algorithms that rely on diagnosis information to calculate risk. To test preprocessing to address bias in healthcare data, we measured the amount of discrimination in favor of metropolitan patients in the classification of multimorbidity. We built a model using the biased data to test optimum classification performance. A new unbiased training data set and model were created and tested against unaltered validation data. The new model’s classification performance on unaltered data did not diverge significantly from the performance of the initial optimal model trained on the biased data suggesting that bias can be removed with preprocessing.
TerminoDiff – Detecting Semantic Differences in HL7 FHIR CodeSystems
Authors: Wiedekopf Joshua , Drenkhahn Cora , Rosenau Lorenz , Ulrich Hannes , Kock-Schoppenhauer Ann-Kristin , Ingenerf Josef
Published: May 2022

While HL7 FHIR and its terminology package have seen a rapid uptake by the research community, in no small part due to the wide availability of tooling and resources, there are some areas where tool availability is still lacking. In particular, the comparison of terminological resources, which supports the work of terminologists and implementers alike, has not yet been sufficiently addressed. Hence, we present TerminoDiff, an application to semantically compare FHIR R4 CodeSystem resources. Our tool considers differences across all levels required, i.e. metadata and concept differences, as well as differences in the edge graph, and surfaces them in a visually digestible fashion.
Data-Driven Modeling of Randomized Controlled Trial Outcomes
Authors: Chen Zhehuan , Fang Yilu , Liu Hao , Weng Chunhua
Published: May 2022

Anecdotally, 38.5% of clinical outcome descriptions in randomized controlled trial publications contain complex text. Existing terminologies are insufficient to standardize outcomes and their measures, temporal attributes, quantitative metrics, and other attributes. In this study, we analyzed the semantic patterns in the outcome text in a sample of COVID-19 trials and presented a data-driven method for modeling outcomes. We conclude that a data-driven knowledge representation can benefit natural language processing of outcome text from published clinical studies.
Ontology-Driven Data Cleaning Towards Lossless Data Compression
Authors: Kiourtis Athanasios , Mavrogiorgou Argyro , Manias George , Kyriazis Dimosthenis
Published: May 2022

With the available data in healthcare, healthcare organizations and practitioners require interoperable, efficient, and non-time-consuming data exchange. Currently, several cases aim to the exchanged data security, without considering the complexity of the data to be exchanged. This paper provides an Ontology-driven Data Cleaning mechanism, facilitating Lossless Healthcare Data Compression to efficiently compress healthcare data of different nature (textual, audio, image). The latter is being evaluated considering three datasets of different formats, concluding to the added value of the described mechanism.
Towards a Clinical Decision Support System for Helping Medical Students in Emergency Call Centers
Authors: Michot Edouard , Woo Jules , Mouline Louis , Sinnappan Catherine , Boukobza Adrien , Campeotto Florence , Dupic Laurent , Burgun Anita , Vivien Benoît , Tsopra Rosy
Published: May 2022

In critical situations such as pandemic, medical students are often called to help in emergency call centers. However, they may encounter difficulties in phone triage because of a lack of medical skills. Here, we aim at developing a Clinical Decision Support System for helping medical students in phone call triage of pediatric patients. The system is based on the PAT (Pediatric Assessment Triangle) and local guidelines. It is composed of two interfaces. The first allows a quick assessment of severity signs, and the second provides recommendations and additional elements such as “elements to keep in mind” or “medical advice to give to patient”. The system was evaluated by 20 medical students, with two fictive clinical cases. 75% of them found the content useful and clear, and the navigation easy. 65% would feel more reassured to have this system in emergency call centers. Further works are planned to improve the system before implementation in real-life.
Implementation of a Clinical Trial Recruitment Support System Based on Fast Healthcare Interoperability Resources (FHIR) in a Cardiology Department
Authors: Scherer Clemens , Endres Stephan , Orban Martin , Kääb Stefan , Massberg Steffen , Winter Alfred , Löbe Matthias
Published: May 2022

Clinical Trial Recruitment Support Systems can booster patient inclusion of clinical trials by automatically analyzing eligibility criteria based on electronic health records. However, missing interoperability has hindered introduction of those systems on a broader scale. Therefore, our aim was to develop a recruitment support system based on FHIR R4 and evaluate its usage and features in a cardiology department. Clinical conditions, anamnesis, examinations, allergies, medication, laboratory data and echocardiography results were imported as FHIR resources. Clinical trial information, eligibility criteria and recruitment status were recorded using the appropriate FHIR resources without extensions. Eligibility criteria linked by the logical operation “OR” were represented by using multiple FHIR Group resources for enrollment. The system was able to identify 52 of 55 patients included in four clinical trials. In conclusion, use of FHIR for defining eligibility criteria of clinical trials may facilitate interoperability and allow automatic screening for eligible patients at multiple sites of different healthcare providers in the future. Upcoming changes in FHIR should allow easier description of “OR”-linked eligibility criteria.
CDS-Compare: A Web Application for Machine Learning Assisted Curation of Clinical Order Sets
Authors: Falls Zackary , Brown Steven H. , Shah Naveed , LeHouillier Frank , Badwal Randeep , Hall Kendria , Franklin Gillian , Kelly William H. , Holdaway Andrew , Montella Diane , Wedemeyer Linda , Keni Sarita , Rose Eric , Nebeker Jonathan R. , Elkin Peter L.
Published: May 2022

Order sets that adhere to disease-specific guidelines have been shown to increase clinician efficiency and patient safety but curating these order sets, particularly for consistency across multiple sites, is difficult and time consuming. We created software called CDS-Compare to alleviate the burden on expert reviewers in rapidly and effectively curating large databases of order sets. We applied our clustering-based software to a database of NLP-processed order sets extracted from VA’s Electronic Health Record, then had subject-matter experts review the web application version of our software for clustering validity.
Minor and Parental Access to Electronic Health Records: Differences Across Four Countries
Authors: Hagström Josefin , Scandurra Isabella , Moll Jonas , Blease Charlotte , Haage Barbara , Hörhammer Iiris , Hägglund Maria
Published: May 2022

An increasing number of countries are implementing patient access to electronic health records (EHR). However, EHR access for parents, children and adolescents presents ethical challenges of data integrity, and regulations vary across providers, regions, and countries. In the present study, we compare EHR access policy for parents, children and adolescents in four countries. Documentation from three areas: upper age limit of minors for which parents have access; age at which minors obtain access; and possibilities of access restriction and extension was collected from Sweden, Norway, Finland, and Estonia. Results showed that while all systems provided parents with automatic proxy access, age limits for its expiry differed. Furthermore, a lower minimum age than 18 for adolescent access was present in two of four countries. Differences between countries and potential implications for adolescents are discussed. We conclude that experiences of various approaches should be explored to promote the development of EHR regulations for parents, children and adolescents that increases safety, quality, and equality of care.
Using Explainable Artificial Intelligence Models (ML) to Predict Suspected Diagnoses as Clinical Decision Support
Authors: Ritter Zully , Vogel Stefan , Schultze Frank , Pischek-Koch Kerstin , Schirrmeister Wiebke , Walcher Felix , Röhrig Rainer , Kesztyüs Tibor , Krefting Dagmar , Blaschke Sabine
Published: May 2022

The complexity of emergency cases and the number of emergency patients have increased dramatically. Due to a reduced or even missing specialist medical staff in the emergency departments (EDs), medical knowledge is often used without professional supervision for the diagnosis. The result is a failure in diagnosis and treatment, even death in the worst case. Secondary: high expenditure of time and high costs. Using accurate patient data from the German national registry of the medical emergency departments (AKTIN-registry, Home – Notaufnahmeregister (aktin.org)), the most 20 frequent diagnoses were selected for creating explainable artificial intelligence (XAI) models as part of the ENSURE project (ENSURE (umg.eu)). 137.152 samples and 51 features (vital signs and symptoms) were analyzed. The XAI models achieved a mean area under the curve (AUC) one-vs-rest of 0.98 for logistic regression (LR) and 0.99 for the random forest (RF), and predictive accuracies of 0.927 (LR) and 0.99 (RF). Based on its grade of explainability and performance, the best model will be incorporated into a portable CDSS to improve diagnoses and outcomes of ED treatment and reduce cost. The CDSS will be tested in a clinical pilot study at EDs of selected hospitals in Germany.
Supporting Digital Inclusion and Web Accessibility for People with Cognitive Disabilities
Authors: Poultourtzidis Ioannis , Marina Katsouli Aikaterini , Anastasiades Savvas , Makroglou Stavroula , Sidiropoulos Efstathios , Bamidis Panagiotis D.
Published: May 2022

The topic of digital inclusion and web accessibility for People with Cognitive Disabilities has increased over the COVID-19 pandemic times. The LIVE IT project is attempting to shed some light into this. This piece of work uses insights gained from focus groups and interviews that were held to assess the needs analysis and the existing knowledge gap of this societal problem. To this end, preliminary results of user engagement with digital tools and web services as well as their evaluation are presented herein.
iCompanion: A Serious Games App for the Management of Frailty
Authors: Sykoutris Antonios , Kouroubali Angelina , Katehakis Dimitrios G. , Kondylakis Haridimos
Published: May 2022

The term frailty is often used to describe a particular state of health, related to the ageing process, often experienced by older people. The most common indicators of frailty are weakness, fatigue, weight loss, low physical activity, poor balance, low gait speed, visual impairment and cognitive impairment. The objective of this work is the creation of a serious games mobile application to conduct elderly frailty assessments in an accurate and objective way using mobile phone capabilities. The proposed app includes three games (memory card, endless runner, and clicker) and three questionnaires, aiming towards the prediction of signs of memory and reflection deterioration, as well as endurance and strength. The games, when combined with a set of qualified questionnaires, can provide an efficient tool to support adults in identifying frailty symptoms and in some cases prevent further deterioration. At the same time the app can support older adults in improving physical and mental fitness, while gathering useful information about frailty.
Classification of Digital Mental Health Interventions: A Rapid Review and Framework Proposal
Authors: Gagnon Marie-Pierre , Sasseville Maxime , Leblanc Annie
Published: May 2022

The modern context of mental health interventions asks for an inclusion of digital solutions to the face-to-face approach, providing better access and reduced inequity for patients. The current classification of digital mental health interventions can be system specific (mobile apps) or general (virtual therapy), which causes inadequacy in applications. The goal of this study was to develop a framework to improve digital mental health interventions classification. We performed a rapid review of the literature on existing digital mental health interventions frameworks. We identified four existing frameworks, extracted their purpose, categories and items, completed a thematic analysis and formulated a four domains framework proposal. This framework allows to classify digital mental health interventions on their system, function, time and facilitation, which should facilitate our understanding of the effect of singular characteristics on patient outcomes.
Atlantes: Automated Health Related & COVID-19 Data Management for Use in Predictive Models
Authors: Vangelatos George , Karanikas Haralampos , Tasoulis Sotiris
Published: May 2022

The scientific community, having turned its interest, almost entirely, to the treatment and understanding of COVID-19, is constantly striving to collect and use data from the countless available sources. That data, however, is scattered, not designed to be combined, collected in different time periods and their volume is constantly increasing. In this paper, we present an automated methodology that collects, refines, groups and combines data for a large number of countries. Most of these data resources are directly related to COVID-19 but we also choose to include other types of variables for each country, which may be of particular interest for researchers working in understanding the COVID-19 pandemic. The presented methodology unifies critical information regarding the pandemic. It is implemented in Python, provided as a simple script that extracts data, in the form of a daily time series, in a short period of time, directly available to be incorporated for analysis.
The COVID-19 Data Exchange Platform of the German University Medicine
Authors: Prokosch Hans-Ulrich , Bahls Thomas , Bialke Martin , Eils Jürgen , Fegeler Christian , Gruendner Julian , Haarbrandt Birger , Hampf Christopher , Hoffmann Wolfgang , Hund Hauke , Kampf Marvin , Kapsner Lorenz A. , Kasprzak Piotr , Kohlbacher Oliver , Krefting Dagmar , Mang Jonathan M. , Marschollek Michael , Mate Sebastian , Müller Armin , Prasser Fabian , Sass Julian , Semler Sebastian , Stenzhorn Holger , Thun Sylvia , Zenker Sven , Eils Roland
Published: May 2022

COVID-19 has challenged the healthcare systems worldwide. To quickly identify successful diagnostic and therapeutic approaches large data sharing approaches are inevitable. Though organizational clinical data are abundant, many of them are available only in isolated silos and largely inaccessible to external researchers. To overcome and tackle this challenge the university medicine network (comprising all 36 German university hospitals) has been founded in April 2020 to coordinate COVID-19 action plans, diagnostic and therapeutic strategies and collaborative research activities. 13 projects were initiated from which the CODEX project, aiming at the development of a Germany-wide Covid-19 Data Exchange Platform, is presented in this publication. We illustrate the conceptual design, the stepwise development and deployment, first results and the current status.
The Impact of Hospital Accessibility on Interregional Patient Mobility in Italy
Authors: Pecoraro Fabrizio , Luzi Daniela , Clemente Fabrizio
Published: May 2022

Patient mobility represents a proxy measure to assess the quality and availability of hospital services, especially in decentralized health systems. Different studies have been focused on the interregional mobility in Italy to capture factors influencing this phenomenon. Among them, hospital capacity is generally captured through the number of beds per population. However, this indicator does not consider the distance to hospitals and the accessibility of extra-regional beds, in particular for patients living at the regional borders. The aim of this paper is to analyse the effect of extra-regional spatial accessibility component on patient mobility among the Italian regions. This can help to capture the level of equity in the provision of services across the country providing a snapshot of the distribution of beds over the territory. Moreover, this study contributes to gain a deeper understanding of the allocation of health resources providing input for policy makers on the basis of the principles of service accessibility.
Covid-19 Positivity Differences Among Patients of a Rural, Southern US State Hospital System Based on Population Density, Rural-Urban Classification, and Area Deprivation Index
Authors: Wong Lori , Yu Feliciano , Bhattacharyya Sudeepa , Greer Melody L.
Published: May 2022

In this study we examined the correlation of COVID-19 positivity with area deprivation index (ADI), social determinants of health (SDOH) factors based on a consumer and electronic medical record (EMR) data and population density in a patient population from a tertiary healthcare system in Arkansas. COVID-19 positivity was significantly associated with population density, age, race, and household size. Understanding health disparities and SDOH data can add value to health and the creation of trustable AI.
Patient Values Associated with an Exergame Supporting COPD Treatment
Authors: Oberschmidt Kira , Broekhuis Marijke , Grünloh Christiane
Published: May 2022

Exercise games (exergames) can help COPD patients stay active and prevent exacerbation. When evaluating such exergames, patient values are an important variable to take into account. In this study, seven COPD patients used an exergame technology at the physiotherapist for six months. Their values regarding treatment and the exergames were identified in interviews. Values were very stable throughout the study, and closely interconnected. Personal Guidance and Independence were important values. Additionally, patients sometimes held conflicting values that they prioritized differently at different times or based on specific events. As the study identified important values that appeared stable over a period of time, albeit with different prioritization, they are worth considering when designing new technology. However, values cannot be looked at in isolation because of the strong connection between values.
Are People Ready to Report Digital Health Ethical Issues in Order to Contribute to Their Resolution?
Authors: Robert Magali , Baudiniere Nathalie , Seroussi Brigitte
Published: May 2022

Although guaranteed by the GDPR, transparency of health data processing may not be fully respected, leading citizens to mistrust eHealth and discard digital health services. Identifying and safeguarding ethics in eHealth services is thus important to promote their development. We conducted a survey to assess the extent of ethical issues induced by the use of digital health services, understand the efforts citizens would be willing to accept for reporting such issues, and evaluate citizens’ expectations regarding this reporting. Among 200 respondents, 36% reported having encountered ethical issues with the processing of their health data or with digital health services being poorly inclusive. Faced to ethical issues when using a digital health service, 49% of respondents were rather or very angry, and 33% felt rather or very dependent. Most respondents were ready to report digital health ethical issues if there is a feedback for each report.
Jupyter Notebooks for Introducing Data Science to Novice Users
Authors: Fruchart Mathilde , Guinhouya Benjamin , Pelayo Sylvia , Vilhelm Christian , Lamer Antoine
Published: May 2022

Data science is a bridge discipline involving computer science, statistics, and knowledge of the health field. We developed a Jupyter Notebook to enable novice users to easily and autonomously analyze data from social networks. We conducted an experimentation with non-programmer students. They had to adapt a R Notebook and complete 14 questions and to perform descriptive analyses. The average rate of correct answers was 90.7. Jupyter Notebook enabled novice users to easily and autonomously analyze data from Twitter.
Classification of Oncology Treatment Responses from French Radiology Reports with Supervised Machine Learning
Authors: Goldman Jean-Philippe , Mottin Luc , Zaghir Jamil , Keszthelyi Daniel , Lokaj Belinda , Turbé Hugues , Gobeil Julien , Ruch Patrick , Ehrsam Julien , Lovis Christian
Published: May 2022

The present study shows first attempts to automatically classify oncology treatment responses on the basis of the textual conclusion sections of radiology reports according to the RECIST classification. After a robust and extended manual annotation of 543 conclusion sections (5-to-50-word long), and after the training of several machine learning techniques (from traditional machine learning to deep learning), the best results show an accuracy score of 0.90 for a two-class classification (non-progressive vs. progressive disease) and of 0.82 for a four-class classification (complete response, partial response, stable disease, progressive disease) both with Logistic Regression approach. Some innovative solutions are further suggested to improve these scores in the future.
Automatic Prediction of Semantic Labels for French Medical Terms
Authors: Hamon Thierry , Grabar Natalia
Published: May 2022

We address the problem of semantic labeling of terms in two French medical corpora with the subset of the UMLS. We perform two experiments relying on the structure of words and terms, and on their context: 1) the semantic label of already identified terms is predicted; 2) the terms are detected in raw texts and their semantic label is predicted. Our results show over 0.90 F-measure.
Extreme Gradient Boosting Based Improved Classification of Blood-Brain-Barrier Drugs
Authors: Subha Ramakrishnan Manuskandan , Ganapathy Nagarajan
Published: May 2022

In this study, the analysis based on boosting approach namely linear and tree method are explored in extreme gradient boosting (XGBoost) to classify blood brain barrier drugs using clinical phenotype. The clinical phenotype features of BBB drugs are Public available SIDER dataset. The clinical features namely drug’s side effect, drug’s indication and the combination is fed to XGBoost. Results shows that the proposed approach is able to discriminate BBB drugs. The combination of XGBoost with tree boosting is found to be most accurate (F1=78.5%) in classifying BBB drugs. This method of tree boosting in XGBoost may be extended to access the drugs for precision medicine.
Named Entity Recognition in Pubmed Abstracts for Pharmacovigilance Using Deep Learning
Authors: Nghiem T. Trang , Bousquet Cedric
Published: May 2022

Methods of natural language processing associated with machine learning or deep learning can support detection of adverse drug reactions in abstracts of case reports available on Pubmed. In 2012, Gurulingappa et al. proposed a training set for the recognition of named entities corresponding to drugs and adverse reactions on 3000 Pubmed abstracts. We implemented a classifier using deep learning with a Bi-LSTM and a CRF layer that achieves an F-measure of 87.8%. Perspectives consist in using BERT for improving the classifier, and applying it to a large number of Pubmed abstract to build a database of case reports available in the literature.
Evaluation of the Efficiency of Telemedicine in the Management of Cardiovascular Diseases in Primary Healthcare in Sub-Saharan Africa: A Medico-Economic Study in Cameroon
Authors: Bediang Georges , Nganou-Gnindjio Chris Nadège , Kamga Yannick , Ndongo Jean Serge , Goethe Doualla Fred-Cyrille , Bagayoko Cheick Oumar , Nko’o Samuel
Published: May 2022

Objective: To assess the efficiency of tele-expertise (tele-ECG) for patients and for health facilities in managing patients with cardiovascular diseases (CVDs) in primary health care in Cameroon. Method: This study was a medico-economic study combining two approaches: cost minimization and cost-effectiveness analysis. It was conducted alongside the previous published controlled multicenter study carried out in Cameroon’s two health facilities where tele-ECG has been implemented (intervention centres) and two other where telemedicine has been not implemented (control centres). Results: The average total cost for patients was 9 286 F CFA (US$: 16) in the intervention centres compared to 28 357 F CFA (US$: 49) in the control centres (p <0.01). The calculated ICER favouring tele-ECG was 25 459.6 F CFA (US$: 44). Discussion: Telemedicine is efficient for managing patients with CVDs in primary health care in Cameroon. It enables health facilities in remote areas to offer new healthcare services at a lower cost and improve patients’ financial access to healthcare.
Decreased Memory Bias via a Mobile Application: A Symptom Tracker to Monitor Children’s Periodic Fever
Authors: Vallo Hult Helena , Islind Anna Sigridur , Rydenman Karin , Hällsjö Wekell Per
Published: May 2022

Memory bias, the tendency to rely on certain events over others, can become an issue in chronic illnesses, especially when symptoms are reported retrospectively. This paper examines a case where continuous symptom registration can be facilitated, memory supported, and memory bias reduced by introducing a mobile application. The aim of the paper is to report on the design of an app for collecting subjective data over an extended period to continuously follow children with periodic fever. The research approach is qualitative, building on interview data. The design method is co-design, a collaborative and participatory approach involving researchers, physicians and other key stakeholders, with focus on the views of the parents. We argue that collecting data continuously through an app moves the discussion from memory to the specific data points, which is illustrated through trends shown in the visualizations of the data. Moreover, we highlight the importance of systematically collecting data over an extended period through a data-driven approach to both forward clinical practice and research on complex, often chronic topics such as periodic fever, which is genuinely under-researched to date.
Timeliness of Single-Patch 12-Lead Electrocardiography for Patients with Chest Pain at the Emergency Department
Authors: Yoo Suyoung , Chang Hansol , Kim Taerim , Yoon Hee , Hwang Sungyeon , Sin Taegun , Sim Minsub , Jo Ikjoon , Choi Jinho , Cha Won Chul
Published: May 2022

A 12-lead ECG is used in emergency departments to diagnose and treat patients with chest discomfort. Performing ECGs at the proper time has been found to increase treatment outcomes. A timer on a wearable ECG ensures proper recording. We compared the timing accuracy of single-patch 12-lead ECG to conventional ECG, expecting the former to be more accurate. Adult patients with chest pain but not in shock were randomized into two groups: SP-ECG and C-ECG. The final analysis included 33 of the 36 recruited patients. The key result was the time taken to record the ECG in both groups. The two groups’ average ages were 63.7 and 58.1 years. The SP-ECG group was 87.5 percent timely, while the C-ECG group was 47.0 percent (P.74). At the second follow-up, it was 75.0 percent and 35.2%. Continuous ECG without interfering with other exams is feasible in complex ER circumstances. But the accuracy of single-patch ECG has not been verified. The device also had some minor difficulties. The use of SP-ECG may help alleviate overcrowding or staffing issues in EDs, although more research is required.
Telemedicine for Diabetes in Norway
Authors: Larbi Dillys , Lauvhaug Nybakke Henriette , Lind Karianne F. , Rødseth Eirin , Gabarron Elia
Published: May 2022

Telemedicine can be useful for diabetes patients living remotely, especially during pandemic times. We aimed to identify current knowledge of the use of telemedicine for diabetes in Norway by conducting a review of the literature. Telemedicine is mostly beneficial, and it seems that it can be adopted into the usual diabetes care in Norway as a low-cost alternative.
IoT in Elementary School for Everyone – A Research Plan
Authors: Ahlin Karin , Wästlund Erik , Ahmad Awais , Nöjd Sture , Mozelius Peter , Sodhro Ali Hassan
Published: May 2022

We propose a tentative research plan to increase students’ mental health in elementary schools by implementing Internet of Things (IoT) technology. The research plan should answer how to support students’ mental health using IoT solutions and the critical factors influencing testbeds for IoT solutions with the previously mentioned purpose. Our intended research method is Design Science, which we plan to use stepwise.
Validation of Multiple Path Translation for SNOMED CT Localisation
Authors: Schulz Stefan , Boeker Martin , Prunotto Andrea
Published: May 2022

The MTP (multiple translation paths) approach supports human translators in clinical terminology localization. It exploits the results of web-based machine translation tools and generates, for a chosen target language, a scored output of translation candidates for each input terminology code. We present first results of a validation, using four SNOMED CT benchmarks and three translation engines. For German as target language, there was a significant advantage of MTP as a generator of plausible translation candidate lists, and a moderate advantage of the top-ranked MTP translation candidate over single best performing direct-translation approaches.