bluuebunny/crossref_metadata_2025 huggingface dataset:
PEMBELAJARAN BAHASA INGGRIS UNTUK SEKOLAH DASAR MELALUI CERITA RAKYAT ANAK PAPUA
Authors: Yektiningtyas Wigati , Rahayu Budi
Published: May 2022

Some Primary Schools in Jayapura, Papua teach English but some teachers still face difficulties in teaching it. Suitable materials and strategies are the obstacles. Inisiative of teaching English using Papua children folktales is used as a scaffolding to help better teaching-learning process. Emphirically, Papua children folktales can be used to teach (1) alphabet, (2) vocabularies, (3) phrases, (4) simple sentences, and (5) numbers. Short drama, songs, and games are inserted to strengthen what the students have learned. The initiative result shows that the teaching process is more interesting and unstressful for the students because the materials are adopted from their natural and socio-cultural environment. Unity of cognitive, affective and psychomotor domains are also involved in the learning process that encourage students to be more active and confident. Keywords: Teaching-learning process, alphabet, vocabularies, phrases
Pedoman Penulisan
Authors: Jurnal Pengelola
Published: May 2022

Pedoman Penulisan Jurnal
MISeval: A Metric Library for Medical Image Segmentation Evaluation
Authors: Müller Dominik , Hartmann Dennis , Meyer Philip , Auer Florian , Soto-Rey Iñaki , Kramer Frank
Published: May 2022

Correct performance assessment is crucial for evaluating modern artificial intelligence algorithms in medicine like deep-learning based medical image segmentation models. However, there is no universal metric library in Python for standardized and reproducible evaluation. Thus, we propose our open-source publicly available Python package MISeval: a metric library for Medical Image Segmentation Evaluation. The implemented metrics can be intuitively used and easily integrated into any performance assessment pipeline. The package utilizes modern DevOps strategies to ensure functionality and stability. MISeval is available from PyPI (miseval) and GitHub: https://github.com/frankkramer-lab/miseval.
Analysis of Stroke Assistance in Covid-19 Pandemic by Process Mining Techniques
Authors: Leandro Gabrielle dos Santos , Miura Daniella Yuri , Safanelli Juliana , Borges Rafaela Mantoan , Moro Cláudia
Published: May 2022

Medical assistance to stroke patients must start as early as possible; however, several changes have impacted healthcare services during the Covid-19 pandemic. This research aimed to identify the stroke onset-to-door time during the Covid-19 pandemic considering the different paths a patient can take until receiving specialized care. It is a retrospective study based on process mining (PM) techniques applied to 221 electronic healthcare records of stroke patients during the pandemic. The results are two process models representing the patient’s path and performance, from the onset of the first symptoms to admission to specialized care. PM techniques have discovered the patient journey in providing fast stroke assistance.
Automated Diagnosis of Autism Spectrum Disorder Condition Using Shape Based Features Extracted from Brainstem
Authors: Jain Vaibhav , Selvaraj Abirami , Mittal Rakshit , Rani Priya , Kilpattu Ramaniharan Anandh , Agastinose Ronickom Jac Fredo
Published: May 2022

Alterations to the brainstem can hamper cognitive functioning, including audiovisual and behavioral disintegration, leading to individuals with Autism Spectrum Disorder (ASD) face challenges in social interaction. In this study, a process pipeline for the diagnosis of ASD has been proposed, based on geometrical and Zernike moments features, extracted from the brainstem of ASD subjects. The subjects considered for this study are obtained from publicly available data base ABIDE (300 ASD and 300 typically developing (TD)). Distance regularized level set (DRLSE) method has been used to segment the brainstem region from the midsagittal view of MRI data. Similarity measures were used to validate the segmented images against the ground truth images. Geometrical and Zernike moments features were extracted from the segmented images. The significant features were used to train Support vector machine (SVM) classifier to perform classification between ASD and TD subjects. The similarity results show high matching between DRLSE segmented brainstem and ground truth with high similarity index scores of Pearson Heron-II (PH II) = 0.9740 and Sokal and Sneath-II (SS II) = 0.9727. The SVM classifier achieved 70.53% accuracy to classify ASD and TD subjects. Thus, the process pipeline proposed in this study is able to achieve good accuracy in the classification of ASD subjects.
Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases
Authors: Redjdal Akram , Bouaud Jacques , Gligorov Joseph , Seroussi Brigitte
Published: May 2022

In many countries, the management of cancer patients must be discussed in multidisciplinary tumor boards (MTBs). These meetings have been introduced to provide a collaborative and multidisciplinary approach to cancer care. However, the benefits of MTBs are now being challenged because there are a lot of cases and not enough time to discuss all the of them. During the evaluation of the guideline-based clinical decision support system (CDSS) of the DESIREE project, we found that for some clinical cases, the system did not produce recommendations. We assumed that these cases were complex clinical cases and needed deeper MTB discussions. In this work, we trained and tested several machine learning and deep learning algorithms on a labelled sample of 298 breast cancer patient summaries, to predict the complexity of a breast cancer clinical case. XGboost and multi-layer perceptron were the models with the best result, with an F1 score of 83%.
Utilizing a Non-Motor Symptoms Questionnaire and Machine Learning to Differentiate Movement Disorders
Authors: Brenner Alexander , Plagwitz Lucas , Fujarski Michael , Warnecke Tobias , Varghese Julian
Published: May 2022

Parkinson’s disease (PD) is a common neurodegenerative disorder that severely impacts quality of life as the condition progresses. Early diagnosis and treatment is important to reduce burden and costs. Here, we evaluate the diagnostic potential of the Non-Motor symptoms (NMS) questionnaire by the International Parkinson and Movement Disorder Society based on patient-completed answers from a large single-center prospective study. In this study data from 489 study participants consisting of a PD group, a healthy control (HC) group and patients with differential diagnosis (DD) have been recorded with a smartphone-based system. Evaluation of the study data has shown a significant difference in NMS between the representative groups. Cross-validation of Machine Learning based classification achieves balanced accuracy scores of 88.7% in PD vs. HC, 72.1% in PD vs. DD and 82.6% when discriminating between all movement disorders (PD + DD) and the HC group. The results indicate potentially high feature importance of a simple self-administered questionnaire that could support early diagnosis.
Instruments Segmentation in X-ray Fluoroscopic Images for Endoscopic Retrograde Cholangio Pancreatography
Authors: Martin Garance , El-Madafri Saad , Becq Aymeric , Szewczyk Jérôme , Bloch Isabelle
Published: May 2022

In this work, we propose a method to segment endoscope and guidewire from 2D X-ray fluoroscopic images of an endoscopic retrograde cholangiopancreatography (ERCP). We used an improved U-Net model. We obtained a Dice score of 0.94±0.05 for endoscope segmentation and a Hausdorff distance of 24.26 pixels for the guidewire segmentation. These preliminary results pave the way for further applications aiming at aiding the medical procedure.
Conversational Agent to Address COVID-19 Infodemic: A Design-Based Research Approach
Authors: Zidoun Youness , Kaladhara Sreelekshmi , Kotp Yehia , Powell Leigh , Nour Radwa , Al Suwaidi Hanan , Zary Nabil
Published: May 2022

Since the beginning of the year 2020, we have been suffering from the COVID-19 pandemic and are daily exposed to misinformation, leading to myths around vaccination and COVID-19. This study focuses on creating and distributing a Conversational Agent (CA), named VWise, for a health intervention using Design-Based Research (DBR), to help profile, guide, and inform the public about COVID-19 and COVID-19 vaccination in the EMRO (Eastern Mediterranean Region of Operations) region.
Artificial Intelligence in Kidney Transplantation: A Scoping Review
Authors: Alamgir Asma , Hussein Hagar , Abdelaal Yasmin , Abd-Alrazaq Alaa , Househ Mowafa
Published: May 2022

Artificial Intelligence (AI) technologies are increasingly being used to enhance kidney transplant outcomes. In this review, we explore the use of AI in kidney transplantation (KT) in the existing literature. Four databases were searched to identify a total of 33 eligible studies. AI technologies were used to help in diagnostic, predictive and medication management purposes for kidney transplant patients. AI is an emerging tool in KT, however, there is a research gap exploring the limitations associated with implementing AI technologies in the field. Research is also needed to recognize clinical educational needs and other barriers to promote adoption and standardization of care for KT patients amongst clinicians.
Evaluating Unintended Consequences in Health Information Systems
Authors: Rohani Nurkhadija , Yusof Maryati Mohd
Published: May 2022

The use of health information systems (HIS) and complex sociotechnical interactions can generate dangerous unintended consequences (UC). The evaluation of such interactions can provide an understanding of the root causes of UC. This paper reviews the interactions that lead to UC and its contributing factors.
Utilizing Intensive Care Alarms for Machine Learning
Authors: Flint Anne Rike , Klopfenstein Sophie A.I. , Heeren Patrick , Balzer Felix , Poncette Akira-Sebastian
Published: May 2022

Alarms help to detect medical conditions in intensive care units and improve patient safety. However, up to 99% of alarms are non-actionable, i.e. alarm that did not trigger a medical intervention in a defined time frame. Reducing their amount through machine learning (ML) is hypothesized to be a promising approach to improve patient monitoring and alarm management. This retrospective study presents the technical and medical pre-processing steps to annotate alarms into actionable and non-actionable, creating a basis for ML applications.
SOIN – MI Data Lab: Personalized Ophthalmology Through Collaborative Data Collection and Dynamic Patient Consent
Authors: Bergin Ciara , Mantel Irmela , Schlingemann Reinier O. , Tomasoni Mattia , Wolfensberger Thomas J.
Published: May 2022

The Swiss Ophthalmic Image Network (SOIN) is part of the Swiss Personalized Health Network (SPHN). SOIN contains a collaborative, clinical research environment, MI Data Lab, which allows privacy-preserving, data-driven, research. Personalized care of chronic ocular disease, based on Machine Learning (ML) and medical imaging, can dramatically improve quality of life and reduce the burden on health and social care systems. MI Data Lab allows research partners to consolidate their data in a space where doctors and data scientists cooperate to design novel ML algorithms, on curated datasets. To date, we have created several algorithms to detect ocular biomarkers automatically, and applied such tools to 100k+ retinal images. MI Data Lab enables the development of predictive models, the extraction novel traits to be explored in terms of -omic associations, treatment outcome, and priors for disease progression.
Applying Goal-Oriented Modelling for Machine Learning Based Rehabilitation Care
Authors: Gißke Carola , Liu Jin , Gand Kai
Published: May 2022

Virtual coaches can support patients who need continuous rehabilitation due to an acute illness in the home environment. These coaching systems have to give medically correct instructions on the one hand and on the other hand respond individually to the patient. Hereby, machine learning algorithms could enable the adaptation and personalization of the rehabilitation process. In order to capture the necessary medical knowledge in a structured form and let the system technically make use of it, approaches of conceptual modelling have proved to be effective. On the basis of a virtual coaching scenario, we demonstrate how such a coaching application could be conceptually structured with the help of the goal-oriented modeling language i* in comparison to BPMN as process modelling approach and how machine learning algorithms could be implemented.
Thyroid Ultrasound-Image Dataset
Authors: Sharifi Yasaman , Shafiei Susan , Ashgzari Morteza Danai , Zakavi Seyed Rasoul , Eslami Saeid
Published: May 2022

Thyroid Computer-Aided Diagnosis (CAD) systems have been developed to assist radiologists in improving efficiency, reliability, and diagnosis performance. Often the performance of these CAD systems is evaluated with different datasets that make it incomparable. A valuable thyroid ultrasound (US) dataset is presented in this work. This dataset consists of 2450 thyroid US images from 2018 to 2020 in Prospective Epidemiological Research Studies in Mashhad, Iran (PERSIAN), a large national cohort study. These US images have the ROI of thyroid nodules and the associated American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TIRADS) features by expert physicians provided in XML format. Dataset’s images are categorized into five groups based on the ACR-TIRADS (Tirads1-Tirads5). The presented dataset is expected to be a valuable resource to develop and assess thyroid CAD systems to help radiologists better diagnose.
Speak-PIM, Towards a Framework for the Automatic Detection of Potentially Inappropriate Prescriptions
Authors: Mouazer Abdelmalek , Sedki Karima , Tsopra Rosy , Lamy Jean-Baptiste
Published: May 2022

Potentially inappropriate medications (PIMs) have adverse health consequences, particularly in elderly patients. Various explicit criteria have been developed to detect PIMs. However, it is difficult to apply these criteria without the help of an electronic decision support tool. Programming these tools can be very complex. Indeed, for computer scientists it is difficult to understand medical issues and for clinicians it is difficult to program in a computer programming language. In this work we present Speak-PIM, a framework for formalizing the PIM’s rules. Speak-PIM is based on a very simple semantics which is suitable for the declaration of PIMs without embarking on all the complexity of description logic or computer languages. It aims to offer an efficient collaboration between the computer scientists and clinicians.
A Federated and Distributed Data Management Infrastructure to Enable Public Health Surveillance from Intensive Care Unit Data
Authors: Bienzeisler Jonas ,   AKTIN and SPoCK Research Group, Triefenbach Lucas , Kombeiz Alexander , Lottes Matthäus , Vogel Christopher , Grabenhenrich Linus , Fischer Martina , Kocher Theresa , Niekrenz Lukas , Dreher Michael , Müller Christoph , Röhrig Rainer , Majeed Raphael W.
Published: May 2022

The Robert Koch Institute (RKI) monitors the actual number of COVID-19 patients requiring intensive care from aggregated data reported by hospitals in Germany. So far, there is no infrastructure to make use of individual patient-level data from intensive care units for public health surveillance. Adopting concepts and components of the already established AKTIN Emergency Department Data registry, we implemented the prototype of a federated and distributed research infrastructure giving the RKI access to patient-level intensive care data.
Human Factors of EHR Adoption in Saudi Primary Healthcare
Authors: Alanazi Bander Dowahi , Alhijji Mohammed Hijji , Alanazi Ibtisam Dhuwayhi
Published: May 2022

Electronic Health Records are rapidly gaining traction in healthcare with increased acceptance and adoption. However, there is limited understanding of factors influencing adoption in primary care. This paper investigates the human factors of EHR adoption in primary healthcare in Saudi Arabia. An online survey questionnaire was sent to all primary healthcare professionals in Riyadh city, Saudi Arabia. A 65.9% (1127/1710) response rate was obtained. The respondents demonstrated positive perceptions of EHRs in relation to the systems’ benefits. The perceptions were influenced by sociodemographic variables; hence, need consideration when implementing EHRs in primary care.
Goupile: A New Paradigm for the Development and Implementation of Clinical Report Forms
Authors: Martignene Niels , Amad Ali , Bellet Julie , Tabareau Julien , D’Hondt Fabien , Fovet Thomas , Lamer Antoine
Published: May 2022

Despite the increasing computerization of hospital information systems, segments of patient care are still in paper format. Data extracted automatically from the hospital databases for one specific project are thus supplemented by data collected manually. Data collection tools are usually developed entirely, which requires computer knowledge and is tedious, or automatically from metadata or drag and drop controls, which is limiting in terms of functionality. To facilitate this manual collection, we developed a free and open-source tool for creating forms that does not require advanced computer skills, offers rich features, and is quickly implemented, tested and deployed. It was implemented for 15 projects and supported thousands of daily users for a complex interactive study at the national level.
Local Explanation-Based Method for Healthcare Risk Stratification
Authors: Excoffier Jean-Baptiste , Escriva Elodie , Aligon Julien , Ortala Matthieu
Published: May 2022

Decision support tools in healthcare require a strong confidence in the developed Machine Learning (ML) models both in terms of performances and in their ability to provide users a deeper understanding of the underlying situation. This study presents a novel method to construct a risk stratification based on ML and local explanations. An open-source dataset was used to demonstrate the efficiency of this method that well identified the main subgroups of patients. Therefore, this method could help practitioners adjust and build protocols to improve care deliveries that would better reflect patient’s risk level and profile.
Extending the Austrian National EHR System with Patient-Reported Outcome Data
Authors: Katsch Florian , Hussein Rada , Sabutsch Stefan , Prenner Helene , Prasser Fabian , Stamm Tanja , Duftschmid Georg
Published: May 2022

The Austrian national Electronic Health Record system ELGA is a population-based infrastructure for health data. However, to date, it does not include patient-reported outcomes. In this paper, we describe on-going work on extending ELGA with patient-reported outcome data. This will be done by linking ELGA with the infrastructure of the Health Outcomes Observatory (H2O) initiative. The focus will be on using ELGA’s identifier registry for H2O patients and making H2O outcome data accessible in ELGA via an existing ELGA document type for telemonitoring.
Building a Comprehensive Clinical Data Repository Using FHIR, LOINC and SNOMED
Authors: Michel-Backofen Achim , Pellizzari Thomas , Zohner Jochen , Blasini Romina , Marquardt Kurt
Published: May 2022

In 2018 the University Hospital of Giessen (UHG) moved its hospital information system from an in-house solution to commercial software. The introduction of MEONA and Synedra-AIM allowed for the successful migration of clinical documents. The large pool of structured clinical data has been addressed in a second step and is now consolidated in a HAPI-FHIR server and mapped to LOINC and SNOMED for semantic interoperability in multicenter research projects, especially the German Medical Informatics Initiative (MII) and the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium.
How Does Triage by an Electronic Symptom Checker Match with Triage by a Nurse?
Authors: Koskela Tuomas , Liu Ville , Kaila Minna
Published: May 2022

Omaolo© electronic symptom checkers (ESCs) have been developed to make triage for primary health care patients in Finland. Based on the analysis of the patient’s responses to a set of questions, the ESC classifies him/her as emergent, urgent, not urgent, or advices on self-care. In this study the user answered the questions posed by the electronic symptom checker, after which a nurse assessed the urgency of the same user’s symptom. The triage nurse was not allowed to know the result of the electronic symptom assessment until he or she had assessed the patient’s condition. The level of triage was compared between ESC and nurse in each individual case. Findings from 825 individual cases were analyzed. The mean “exactly matched” for all symptom estimates was 52.6%. The mean “exactly matched” or “overconservative but suitable” for all symptom assessments was 66.6%. Safe assessments of electronic symptom checkers accounted for 98.6% of all assessments. A case was defined as “safe” if the recommendation for action given by the symptom assessment was at most one level less urgent than the nurse’s triage assessment of the same case. The findings show that electronic symptom assessments are safe compared to the assessment of an experienced nurse
The Prediction of Fall Circumstances Among Patients in Clinical Care – A Retrospective Observational Study
Authors: Rehfeld Sven , Schulte-Althoff Matthias , Schreiber Fabian , Fürstenau Daniel , Näher Anatol-Fiete , Hauss Armin , Köhler Charlotte , Balzer Felix
Published: May 2022

Standardized fall risk scores have not proven to reliably predict falls in clinical settings. Machine Learning offers the potential to increase the accuracy of such predictions, possibly vastly improving care for patients at high fall risks. We developed a boosting algorithm to predict both recurrent falls and the severity of fall injuries. The model was trained on a dataset including extensive information on fall events of patients who had been admitted to Charité – Universitätsmedizin Berlin between August 2016 and July 2020. The data were recorded according to the German expert standard for fall documentation. Predictive power scores were calculated to define optimal feature sets. With an accuracy of 74% for recurrent falls and 86% for injury severity, boosting demonstrated the best overall predictive performance of all models assessed. Given that our data contain initially rated risk scores, our results demonstrate that well trained ML algorithms possibly provide tools to substantially reduce fall risks in clinical care settings.
A Log Analysis Exploring the Predictors of Electronic Health Record Access by Clinicians for Consumers Aged ≥65 Who Present to the Emergency Department
Authors: Mullins Alexandra , Skouteris Helen , Morris Heather , Enticott Joanne
Published: May 2022

Electronic health records are widely implemented, yet little is understood around adoption and use in the ED setting. Older patients (≥65) are a cohort likely to benefit from use. The primary outcome (MHR access) was explored using logistic regression of 9 independent variables. 28.33% of patients had their MHR accessed within 3 days of presenting. Access is more likely when patients arrive via urgent ambulance and/or are triaged as critical.
Disparities in Regional Publication Trends on the Topic of Artificial Intelligence in Biomedical Science Over the Last Five Years: A Bibliometric Analysis
Authors: Shiferaw Kirubel Biruk , Waltemath Dagmar , Zeleke Atinkut
Published: May 2022

Bibliometric analysis is a scientific method that allows researchers to explore the current trend in a certain research area using citation information. This study aims to provide a meta-view of artificial intelligence studies focused on biomedicine in the last five years, which will provide an insight into current trends and future research directions. Besides the observation of increased publication rates in the area of AI in biomedicine, the results indicate a lower contribution from and a sparser network connectivity of countries with limited resources. Thus, working toward collaboration in terms of infrastructure and implementing alternative solutions such as FAIR (Findable, Accessible, Interoperable and Reproducible) and open access platforms could improve the collaborative nature of international health projects.
Dynamic Prediction of Non-Neutral SARS-Cov-2 Variants Using Incremental Machine Learning
Authors: Nicora Giovanna , Marini Simone , Salemi Marco , Bellazzi Riccardo
Published: May 2022

In this work we show that Incremental Machine Learning can be used to predict the classification of emerging SARS-CoV-2 lineages, dynamically distinguishing between neutral variants and non-neutral ones, i.e. variants of interest or variants of concerns. Starting from the Spike protein primary sequences collected in the GISAID db, we have derived a set of k-mers features, i.e., aminoacid subsequences with fixed length k. We have then implemented a Logistic Regression Incremental Learner that was monthly tested on the variants collected since February 2020 until October 2021. The average value of balanced accuracy of the classifier is 0.72 ± 0.2, which increased to 0.78 ± 0.16 in the last 12 months. The alpha, beta, gamma, eta, kappa and delta variants were recognized as non-neutral variants with mean recall ∼90%. In summary, incremental learning proved to be a useful instrument for pandemic surveillance, given its capability to update the model on new data over time
Global Good Open Source Software Development in Response to the COVID-19 Pandemic – Perspectives from SORMAS Implementation in Europe
Authors: Louw Candice , Paffenholz Rafael , Verset Carolin , Krause Gérard
Published: May 2022

In recent years, software has evolved from being static, closed source, proprietary products to being dynamic, open source, ecosystems contributing to the global good. To this end, the open source software (OSS) solution and global good, Surveillance Outbreak Response Management and Analysis System (SORMAS), rapidly adjusted to the demands of the Coronavirus disease 2019 (COVID-19) outbreak by introducing a COVID-19 module. This allowed countries that were already making use of the software as part of their public health surveillance infrastructure to make use of the new module in order to respond to the pandemic. New countries in continental Europe, most notably Germany, Switzerland, Liechtenstein and France subsequently chose to adopt the software for public health surveillance purposes for the first time during 2020, requiring additional adaptations to meet local needs. As a result, in this paper, we aim to gain a better understanding of how rapidly SORMAS was adapted to meet global needs by analyzing the SORMAS COVID-19 module introduction timeline, as well as the overall development activity of the software during 2020 and 2021 in response to the pandemic. Favorable initial feature response times in combination with development scale-up possibilities speak to some of the potential advantages of implementing global good OSS tools such as SORMAS for public health surveillance, in response to an emergency. Overall, SORMAS serves as proof of concept for developing a global good OSS solution on an international scale.
Preliminary Validation of a Rule-Based System for Mortality Coding Using ICD-11
Authors: Popescu Mihai Horia , Celik Can , Della Mea Vincenzo , Jakob Robert
Published: May 2022

A crucial process for world-level mortality statistics is the capability to identify the underlying cause of death from death certificates. Currently such certificates are coded using ICD-10. The selection of the underlying cause is done by means of semi-automated rule-based systems. However, starting from 2022, countries should begin to adopt ICD-11, for which no system is already available. The present paper describes the architecture of a novel system for automated UC selection, with classification-independent rules, and its preliminary validation on two sets of death certificates coded with ICD-10 and ICD-11.
Using FHIR to Support COVID-19 Vaccine Safety Electronic Case Reports in America
Authors: Rizzato Lede Daniel A. , Molina Helvert F. , Bertoglia Maria Paz , Otzoy Daniel , Benavides Luis A. , Donis José A. , Osorio Víctor B. , Aguilar Carlos , Bustos José L. , Diaz Martín , Revirol Karina , Galindo César , Mansilla Jorge , Campos Fernando , Kaminker Diego , D’agostino Marcelo , Pastor Desiree
Published: May 2022

During the COVID-19 pandemic, the Pan American Health Organization (PAHO) promoted several activities to strengthen the countries’ emergency response. Vaccines represented a breakthrough in the pandemic evolution, even though they have not been equitably distributed. As most vaccines have received emergency authorizations for their timely delivery, vaccine safety surveillance has been highlighted for detecting early signals of potential adverse events following immunization (AEFI, also known as ESAVI). The objective of this article is to share the different steps, methodologies, and preliminary results of a regional policy to strengthen the ESAVI surveillance system in the Americas, including the adoption of HL7 FHIR for health information exchange between countries and PAHO.
Automated Twitter Extraction and Visual Analytics with Dashboards: Development and First Experimentations
Authors: Patel Haris , Patel Riyâz , Zitouni Djamel , Guinhouya Benjamin , Fruchart Mathilde , Lamer Antoine
Published: May 2022

Information found in the social media may help to set up infoveillance and track epidemics, identify high-risk behaviours, or assess trends or feelings about a subject or event. We developed a dashboard to enable novice users to easily and autonomously extract and analyze data from Twitter. Eleven users tested the dashboard and considered the tool to be highly usable and useful. They were able to conduct the research they wanted and appreciated being able to use this tool without having to program.
Mindful Workarounds in Bar Code Medication Administration
Authors: Lichtner Valentina , Dowding Dawn
Published: May 2022

Bar-Coded Medication Administration systems (BCMA) are often used with workarounds. These workarounds are usually judged against standard operating procedures or the use of the technology as ‘designers’ intended’. However, some workarounds may be reasonable and justified to prevent safety errors. In this conceptual paper, we clarify BCMA safety mechanisms and provide a framework to identify workarounds with BCMA that nullify the error prevention mechanisms inherent in the technology design and process. We also highlight the importance of understanding the purpose behind a nurse’s workaround in BCMA, focusing on the notion of mindful (thoughtful) workarounds that have the potential to improve patient safety.
How Does Mental Workload Influence the Adoption of Clinical Information Systems: An Exploratory Study
Authors: Kremer Lisanne , Schwarz Ann-Kathrin , Röhrig Rainer , Breil Bernhard
Published: May 2022

Mental workload and technology acceptance are relevant factors that relate to use behavior and performance. Studies show a potential moderating effect of mental workload on predictors of technology acceptance. Aim of this study was the investigation of predictors of technology acceptance (UTAUT) related to clinical information systems and their relation to mental workload. This quasi-experimental study with 48 participants used the following measures: NASA TLX and UTAUT questionnaire. Participants had to perform three tasks on a clinical information system as well as four task-levels of the n-back task with increasing difficulty. Analyses show a high level of technology acceptance (M=3.82, SD=.76) and confirm performance expectancy as the most relevant predictor of behavioral intention (β=.48, p<.001). A linear regression showed that a high level of mental workload has an influence on performance expectancy (F1,46=8.438, p<.05). The study shows an influence of mental workload on acceptance, the strength and role of which (e.g. moderation) needs to be further investigated, especially in the context of other determinants.
SNIK Quiz: A Multiple Choice Game About Information Management in Hospitals
Authors: Höffner Konrad , Roszeitis Arne , Wächtler Max Niclas , Jahn Franziska , Winter Alfred
Published: May 2022

SNIK is a knowledge base about the management of health information systems generated by extracting Linked Data from textbooks and other sources. SNIK describes functions, roles executing these functions, and entity types, the information used or updated by these functions. We present SNIK Quiz, a browser game in which students answer multiple-choice questions about information management in hospitals based on SNIK. The questions are semi-automatically generated using templates in order to train basic facts, more complex patterns, and connections between textbooks encoded in SNIK.
Artificial Intelligence in Undergraduate Medical Education
Authors: Mosch Lina , Agha-Mir-Salim Louis , Sarica Merve M. , Balzer Felix , Poncette Akira-Sebastian
Published: May 2022

AI will take on an increasingly important role in medicine. Therefore, AI competencies should be taught in medical school. We investigated the inventory of AI-related courses at German medical schools. The majority of faculty offer courses on AI, but mainly at the elective and introductory levels. Regarding the topic of AI, there is a gap in German medical education that should be closed.
Deep Learning Methods for Detecting Side Effects of Cancer Chemotherapies Reported in a Remote Monitoring Web Application
Authors: Metzger Marie-Hélène , Gadji Ahmath , Haj Salah Nada , Kane Wedji , Boue François
Published: May 2022

The objective of our work was to develop deep learning methods for extracting and normalizing patient-reported free-text side effects in a cancer chemotherapy side effect remote monitoring web application. The F-measure was 0.79 for the medical concept extraction model and 0.85 for the negation extraction model (Bi-LSTM-CRF). The next step was the normalization. Of the 1040 unique concepts in the dataset, 62, 3% scored 1 (corresponding to a perfect match with an UMLS CUI). These methods need to be improved to allow their integration into home telemonitoring devices for automatic notification of the hospital oncologists.
Key Elements for the Evaluation of mHealth Applications: Results from a Delphi Survey
Authors: Holl Felix , Swoboda Walter
Published: May 2022

Mobile Health (mHealth) applications have seen strong growth in recent years, but they are often not systematically evaluated. A Delphi survey was conducted to identify key elements for the evaluation of mHealth applications. Sixteen experts participated in the study, and the study yielded a list of 79 key elements with expert consensus. Thirty-two elements were in the category of structure quality, 29 in process quality, and 18 in outcome quality. The number of key elements highlights the complexity of conducting systematic evaluations of mHealth applications.
Differentiation of Cell Painted Organelles Using Non Local Texture Descriptor and Random Forest Approach
Authors: Palliyil Sreekumar Sreelekshmi , Palanisamy Rohini , Swaminathan Ramakrishnan
Published: May 2022

Discriminating the cell organelles from microscopic images is a challenging task due to their high similarity in image appearance. In this work, an attempt has been made to differentiate nuclei, Endoplasmic Reticulum (ER) and cytoplasm using a texture pattern descriptor and Random Forest classifier. For this, Cell Painted public dataset from Broad Bioimage Benchmark collection are considered. Texture features are extracted from each image using Non Local Binary Pattern (NLBP) that captures the relationship between global pixels and sampling instances in a local neighborhood. Non local central pixels called anchors are derived from central pixels of image patches and compared with sampling instances. Binary string generated from this is encoded into 29 patterns. Statistical one-way analysis of variance (ANOVA) is performed to select significant features and are validated using Random Forest classifier. The dependency of classifier performance on the local patch radius (R) and the number of anchors (K) are also evaluated. The results indicate that 8 patterns out of 29 are showing strong inter class variability with high F value. Classification accuracy of 84% is achieved with R=3 and K=5. Experimental results demonstrate that the proposed work captures complex patterns in cell structure useful for differentiating cell components which can be employed for evaluating the cytotoxic effects in cell lines.
Pharmacological optimization of antihistamine profile
Authors:   I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russian Federation, Kareva E.N. ,   Pirogov Russian National Research Medical University, Moscow, Russian Federation
Published: May 2022

Antihistamines are the first-line treatment for allergic diseases. The optimization of pharmacological properties of these pharmacotherapeutics is provided by the synthesis of new molecules with better safety profile (no penetration via the blood-brain barrier or less risk of drug interactions) or more selective affinity to H1-receptors (active optical isomer). In real clinical practice, drugs with extrahepatic metabolism have an advantage, the effectiveness of which does not depend on concomitant therapy, and the maximum concentration is reached in the shortest possible time, which ensures a rapid onset of action. Comfort use and, therefore, adherence to treatment is also an essential aspect of pharmacotherapy. Current orodispersible H1-receptor antagonists have a clinically significant antihistamine effect and agree with patient expectations due to comfort use and no need to take them with water. As a result, these medications are suitable for specific categories of individuals, e.g., children, patients with difficulty of swallowing, and people practicing an active lifestyle. KEYWORDS: histamine, antihistamines, levocetirizine, pharmacokinetics, pharmacodynamics, orodispersible forms, compliance. FOR CITATION: Kareva E.N. Pharmacological optimization of antihistamine profile. Russian Medical Inquiry. 2022;6(2):92–97 (in Russ.). DOI: 10.32364/2587-6821-2022-6-2-92
Distribution of Zakat as a Solution Of Proverty Alleviation in Langsa City
Authors: Agustinar Agustinar , Laila Mufida Laila Mufida , Siti Mu’allimah Siti Mu’allimah
Published: May 2022

The focus of this research was the implementation of distribution of zakat as a solution of poverty alleviation in Langsa City. The problem in this research was, first, how was the concept of distribution in the Islamic Economic System? Second, how was the implementation of distribution of zakat in the Islamic Economic System in alleviating poverty? The purpose of this research was to describe the concept of distribution in the Islamic Economic System, and to analyze the implementation of distribution of zakat in the Islamic Economic System in alleviating poverty. The type of this research was a qualitative research by using descriptive analysis. It was a field research. The objects of this research were the poor people in Langsa City and the Baitul Mal Langsa. Based on the results and the analysis of discussion, it can be concluded that zakat had been distributed evenly. Therefore, zakat in Langsa City should be distributed with a larger budget and a wider scope, so that the benefits desired by Islam can be achieved and Langsa becomes one of the cities of baldatun thayyibatun wa rabbun ghafur in this beloved country of Indonesia.
Діяльність міліції Української РСР щодо протидії злочинності в кінці 1950-х років
Authors: Grechenko V. A.
Published: May 2022

Досліджено головні аспекти діяльності міліції Української РСР, пов’язані з боротьбою проти кримінальної злочинності у кінці 1950-х років. Висвітлено сутність кримінально-правової політики держави у перші роки десталінізації та загальний стан кримінальної злочинності у цей же період, проаналізовано форми і методи керівництва протидією злочинності з боку МВС республіки, зокрема зміст і спрямованість директив МВС та орієнтувань МВС, їх відмінності, особливості перевірок оперативно-службової діяльності окремих УВС областей з подальшим розглядом їх результатів на колегії МВС та значення всіх цих заходів у поліпшенні роботи місцевих органів міліції.
Особливості нормативно-правової регламентації діяльності закладів вищої освіти зі специфічними умовами навчання, які здійснюють підготовку кадрів для Національної поліції України
Authors: Shvets D. V.
Published: May 2022

Проаналізовано особливості нормативно-правової регламентації діяльності закладів вищої освіти зі специфічними умовами навчання, які здійснюють підготовку кадрів для поліції. Установлено, що така нормативно-правова регламентація здійснюється значною кількістю актів законодавства, які у своїй сукупності можна уявити як систему. З’ясовано загальне значення нормативно-правової регламентації діяльності закладів вищої освіти зі специфічними умовами навчання, а також установлено роль окремо взятого акта законодавства в цій сфері. Доведено, що систематизація національного законодавства в цій сфері матиме позитивний ефект для діяльності як поліції в цілому, так і закладів вищої освіти зі специфічними умовами навчання, які здійснюють підготовку кадрів для неї.
Interactive working techniques in an EFL class
Authors: Peñafiel Arcos Edison
Published: May 2022

Las falencias del método didáctico en el proceso de enseñanza-aprendizaje del idioma inglés inciden en el desarrollo limitado de las destrezas lingüísticas de este idioma. Además, aún existen vestigios de la aplicación del método denominado gramática-traducción, ubicado en el contexto del paradigma lingüístico tradicional, cuyo principal objetivo fue el aprendizaje de reglas gramaticales y listas de vocabulario, el cual no es efectivo para el desarrollo de las destrezas lingüísticas. Frente a esta problemática, este libro sugiere fomentar permanentemente la participación de los estudiantes para promover su interés en un ambiente didáctico distendido, dinámico y agradable, que incite a que cada estudiante hable en el idioma extranjero y participe en las distintas actividades señaladas por el docente.
Manual para el manejo de las quemaduras químicas
Authors: Poma Ángel Leonardo
Published: May 2022

En la vida doméstica, ya sea en el transporte, la recreación o en la actividad laboral, el hombre está permanentemente expuesto al contacto con compuestos tóxicos, irritantes, corrosivos, inflamables, cancerígenos o explosivos, cuyo efecto en el organismo humano puede llegar a producir lesiones de grados variables, desde simples inflamaciones tisulares hasta lesiones tan graves que pueden llegar a producir la muerte. El propósito de este libro es abordar todo lo referente a las quemaduras con productos químicos, para generar una conciencia de prevención.
Analysis of the Effect of Pavement Conditions on Vehicle Speed
Authors: Kusumawardhani Sekar Ayu , Prihutomo Nuzul Barkah , Saputro Danang Eko
Published: May 2022

The Jakarta Outer Ring Road (JORR) Toll Road is a logistics route that acts as an essential transportation artery to facilitate logistics distribution. This toll road connects strategic locations to support logistics distribution. It can lead to the repetition of traffic loads, causing a decrease in the pavement quality marked by road damage. Road damage that occurs can reduce the level of safety and comfort of road users. To minimize accidents due to road damage, road users will drive their vehicles at lower speeds in locations/segments that have damage. In the study, the level of road damage was analyzed using the Pavement Condition Index (PCI) method. Vehicle speed using the time mean speed method and then statistically analyzed using linear regression using SPSS v.26 software to determine how much influence road pavement conditions have on vehicle speed. Based on the results of the analysis on the Jakarta Outer Ring Road (JORR) KM 35+000 – 45+000 toll road, the pavement conditions of the toll road are in the good category with various vehicle speeds for each group with a range of 44.80 km/hour to 79,98 km/hour and the effect of pavement conditions on vehicle speed is 51.6% for class 1 vehicle, 58,1% for class 2 vehicle, 42,2% for class 3 vehicle, 42,9% for class 4 vehicle, 44,8% for class 5 vehicle.
Kinematic Hardening Model Comparison of Square Hollow Section Under Cyclic Bending
Authors: Amalia Aniendhita Rizki , Ochi Kenshi
Published: May 2022

This study compares the different linearity of the kinematic hardening model of the Square Hollow Section (SHS) under cyclic bending loading. Four specimens of a simple support beam cyclically tested in previous research are listed as hot-rolled, hot-finished, and two cold-formed. Using the bilinear, multilinear, and Chaboche models, each specimen is modeled in kinematic hardening. The variables or node sets for each linearity model are estimated using tensile test data, and Chaboche variables are obtained using the least-square fitting method. Each linearity model for each specimen is built-in FEA using a shell model. The numerical model applied the same cyclic loading history as the previous test. The numerical analysis comparison concluded that Chaboche and the multilinear kinematic model generate the expected result fitted to test hysteresis of cold-formed one and cold-formed 2 SHS, but the bilinear models are not fitted. Moreover, all kinematic models are not fit for the hot-rolled and hot-finished SHS compared to the test hysteresis. So, for hot-rolled and hot-finished SHS, the combined hardening is suggested; there is a possibility it is because of the lower yield ratio that both sections have. Overall, during a cyclic bending analysis of cold-formed SHS, multilinear or Chaboche models are preferable if the data is limited.
PERBANDINGAN NILAI ISPU HARIAN DI 5 TITIK PEMANTAUAN UDARA DKI JAKARTA PADA SAAT LOCKDOWN MARET 2020
Authors: Nelvidawati Nelvidawati , Roza Angelalia
Published: May 2022

One of the air quality conditions in the Special Capital City Region (DKI) Jakarta is the transportation sector. The air quality index is dominated by moderate conditions, even at dangerous levels. The COVID-19 pandemic caused the DKI Jakarta Provincial Government to lock down in March 2020. Research in several countries has shown that air quality has improved because people's mobility has been stopped. This study aims to determine the effect of the lockdown imposed in DKI Jakarta on its air quality and see a comparison at each point. The method used is descriptive method. The data analyzed in this study were collected from 5 points of the Air Quality Monitoring System (AQMS) monitoring tool. Based on the research, it was concluded that the lockdown imposed in DKI Jakarta only affected the location of point 1 at the Hotel Indonesia (HI) roundabout. The Air Pollution Status Index (ISPU) is dominated by good air quality conditions. Other areas such as West Jakarta, East Jakarta, North Jakarta and South Jakarta, the lockdown imposed in March 2020 did not have any effect on increasing the ISPU value. In 2021 the air quality condition in DKI Jakarta is worse than in previous years at all monitoring points since the activities have returned to normal and traffic jams frequently occur.
Keywords: Air Quality Index, DKI Jakarta, Lockdown, Transportation.
Responsabilidad social y análisis comparativo entre dos entidades financieras
Authors: Potas Alberto Veiga
Published: May 2022

En esta investigación, el tema de la responsabilidad social tiene como objetivo presentar y saber cómo las empresas deben ser analizadas en los informes anuales de balance social. El objetivo es investigar la importancia y la utilidad de la responsabilidad social para las empresas ubicadas en Brasil, mostrando las acciones de las organizaciones con el fin de identificar y verificar datos, informaciones, proyectos realizados y problemas existentes. Para ello, será necesario realizar una investigación bibliográfica teórica, investigando la relación de las empresas con la comunidad local, siguiendo el concepto de responsabilidad social dentro de unos límites éticos y morales. Es necesario conocer qué instituciones realizan proyectos y cómo lo hacen a través de la observación de los resultados, también se analizará si este procedimiento brinda un desarrollo sustentable eficiente dentro de la región.