bluuebunny/crossref_metadata_2025 huggingface dataset:
STUDI PEMAHAMAN VISI MISI INSTITUSI DAN PENELURUSAN KESESUAIAN KOMPETENSI PADA ALUMNI JURUSAN KEPERAWATAN GIGI POLTEKKES KEMENKES SEMARANG TAHUN 2010 – 2016
Authors: Sukendro Sulur Joyo , Sariyem Sariyem , Ningtyas Endah Aryati Eko
Published: January 2022

ABSTRAKPoliteknikKesehatanKemenkes Semarang (Poltekkes Semarang)adalah unit pelaksana teknis Kementerian Kesehatan RI yang berada di bawah dan bertanggungjawab kepada Kepala Badan Pengembangan dan Pemberdayaan Sumber Daya Manusia (PPSDM) Kesehatan. Jurusan Keperawaan Gigi Semarang mempunyai program studi D III Keperawatan Gigi dan program studi D IV Keperawatan Gigi danmenghasilkantenagakesehatan yang berdayasaingtinggi. Dokumentasiuntukmelacak alumni Jurusan Keperawatan Gigi Poltekkes Semarang belumdilaksanakansecara optimal. Hal ini terjadi karena pelacakan alumni membutuhkan sumberdaya dan sumberdana yang cukup besar, oleh sebab itu perlu dilakukan kegiatan pelacakan alumni dan pemahaman visi misi institusi dan kesesuaian kompetensi pada alumni.Tujuandaripenelitian iniadalahmemetakanposisilulusandalam dunia kerja, proses dan riwayat pekerjaan, pemahaman visi dan misi lembaga, peta kesenjangankompetensilulusandengantuntutan dunia kerja dan dayaserap, sertakesesuaiankompetensibidangilmudenganbidangpekerjaan. Tahapankegiatan yang dilakukandalampelaksanaan pemetaan karakterisik alumni meliputi 4 (empat) tahapyaitutahappersiapan, tahappelaksanaan, tahapanalisa dan tahapevaluasi/tindaklanjut.Metode yang digunakandalampelacakanadalahmelaluikuesioneronlineberbantu SMS Gateway, jejaring media social (Whatsapp Group, Facebook Group, BBM group, Line, email). Diharapkanseorang alumni perwakilanangkatan 2010-2016 sebagaipioneer yang akanmengajaktemannyadalam group untukikutberpartisipasi. Pemberian reward berupapaket pulsadigunakanuntukmeningkatkanresponse rate.  Data yang didapatkanakandianalisasesuaidenganindikator yang telahditentukansehinggadiharapkanmampumenghasilkanlaporan dan kesimpulanterkait data lulusan.Hasil penelitian yaitu Visi Misi Jurusan Keperawatan Gigi Poltekkes Kemenkes Semarang perlu diberikan pemahaman kepada alumni pada kegiatan-kegiatan lembaga atau pada saat seminar yang dihadiri alumni. Rata-rata lama waktu tunggu alumni dalam mendapatkan pekerjaan pertama pada tahun 2010-2016 yaitu kurang dari 3 bulan dengan persentase sebesar 59% dan yang menunggu 12 bulan dengan persentase sebesar 1,9%.  Jenis pekerjaan alumni yang sesuai yaitu 85% bekerja terapis gigi dan mulut. Tempat kerja alumni 52% di Instansi pemerintah dan rumah sakit, 29%bekerja di Klinik Gigi Bersama atau Pribadi. Rata-rata penghasilan atau gaji alumni yaitu 78,7% masih dalam kategori rendah dan sangat rendah.Kesimpulanpenelitian ini adalah perlu lembaga memperhatikan masukan alumni terhadap kemampuan pada saat lulus maupun upaya peningkatan kemampuan sesuai kebutuhan di lapangan kerja.
PERBEDAAN DEBRIS INDEX DAN pH SALIVA SEBELUM DAN SESUDAH MENGKONSUMSI BUAH SEMANGKA (CITRULLUS LANATUS) PADA MURID KELAS IV MI DARUSSALAM KOTA JAMBI TAHUN 2019
Authors: Junaidi Junaidi , Idham Idham
Published: January 2022

Debris adalah lapisan lunak dari sisa-sisa makanan, melekat tidak erat pada permukaan gigi, dapat dilihat beberapa menit setelah selesai makan.Jika kita tidak menyikat gigi dalam 1 hari saja tentu debris menumpuk pada permukaan gigi. Buah semangka m emiliki kadar air, serat yang tinggi dan vitamin C sehingga diharapkan dengan mengkonsumsi buah semangka dapat mengurangi penumpukan debris dan mempengaruhi pH saliva.Tujuan penelitian ini adalah diketahuinya perbedaan debris index dan ph saliva  sebelum dan sesudah mengkonsumsi buah semangka.                             Hasil penelitian menunjukkan bahwa Terdapat perbedaan yang bermakna pada  debris index sebelum dan sesudah  mengkonsumsi buah semangka. Tidak terdapat perbedaan yang bermakna pada pH Saliva sebelum dan sesudah mengkonsumsi buah semangka. Tidak terdapat perbedaan yang bermakna  pada debris index pada kelompok perlakuan dengan debris index kelompok kontrol setelah mengkonsumsi buah semangka. Tidak terdapat perbedaan yang bermakna pada pH Saliva pada kelompok perlakuan dengan pH Saliva kelompok kontrol setelah mengkonsumsi buah semangka pada murid kela IV MI Darussalam Kota Jambi Tahun 2019Kata Kunci : Debris index, Saliva, Semangka
GAMBARAN PENGETAHUAN, SIKAP, DAN TINDAKAN SISWA-SISWI TENTANG PEMELIHARAAN KESEHATAN GIGI DAN MULUT BERDASARKAN UMUR DAN JENIS KELAMIN DI SD NEGERI 101896 KIRI HULU- I TANJUNG MORAWA PROPINSI SUMATERA UTARA
Authors: Sihombing Kirana Patrolina , Simare-mare Rosdiana T. , Tobing Antika Nabila
Published: January 2022

Kesehatan gigi dan mulut merupakan bagian yang terintegrasi dari kesehatan secara keseluruhan, sehingga perihal kesehatan gigi dan mulut perlu dibudayakan di seluruh lingkungan keluarga dan masyarakat. Salah satu faktor yang mempengaruhi kesehatan gigi dan mulut adalah tingkat pengetahuan, sikap dan tindakan pemeliharaan kesehatan gigi dan mulut. Tujuan penelitian ini adalah untuk mengetahui korelasi antara tingkat pengetahuan, sikap, dan tindakan terhadap kesehatan gigi dan mulut pelajar di SDN 101896 Kiri Hulu-I Tanjung Morawa.Jenis penelitian ini adalah penelitian deskriptif dengan metode survey pada 36 orang menggunakan teknik “proportional stratified random sampling” penelitian ini dilakukan dengan pembagian kuesioner pengetahuan,sikap dan tindakan tentang kesehatan gigi pada siswa/I umur 10-12 tahun SD Negeri 101896 Kiri Hulu-I Tanjung Morawa. Dari hasil penelitian 36 orang responden, sebanyak 32 orang (88,9%) berpengetahuan baik, 4 orang (11,1%) sedang, untuk sikap 18 orang (50%) baik dan 18 orang (50%) sedang,Tindakan 14 orang (38,9%) baik,21orang (58,3%) sedang dan 1 orang (2,7%) buruk.Tingkat pengetahuan responden tentang kesehatan gigi dan mulut dikategorikan baik,interpertasi sikap terhadap kesehatan gigi dan mulut kategori sedang dan interpertasi tindakan terhadap kesehatan gigi dan mulut dikategorikan sedang. Diharapkan responden meningkatkan pengetahuan,sikap dan tindakanya terhadap pemeliharaan kesehatan gigi danmulutnya. Kata Kunci                  : Pengetahuan, Sikap, Tindakan
ANALISA KONSUMSI MAKANAN KARIOGENIK TERHADAP ANGKA KEJADIAN KARIES PADA ANAK SEKOLAH DASAR DI KECAMATAN TEMBALANG KOTA SEMARANG
Authors: Subekti Ani , Mardiati Erni , Putri Rizky Amalia , ASri Lutfi Asri , Prahesti Anggi Ratna , Nadyatin Nisrina Nadyatin
Published: January 2022

ABSTRACT Caries cases are mostly found at the age of development, namely elementary school children. The daily food consumed by school children at home and outside the home is a cariogenic food which is one of the causes of caries. The purpose of this study was to determine the incidence of caries rates that occur in elementary school age children and the dietary pattern of cariogenic food consumed by elementary school aged children in Semarang City.This type of research is analytic observational with a case control study approach. The sample was elementary school children in Tembalang Subdistrict, Semarang City as a group of cases with caries and non-caries as a control group. Cariogenic dietary variables were measured from the sugar content, frequency and consumption method using the Kariogenic Diet Survey. The data analysis used was the Chi-Square Test. Then proceed to test the causal factor analysis using the Odd Ratio.The results of the Odd Ratio showed that children who ate cariogenic foods with a high frequency had a higher caries rate than children who ate cariogenic foods less frequently. Based on the results of data analysis (correlation), it is known that caries and food frequency have a significance value of 0.004 0.05, which means that there is a significant correlation. Furthermore, between caries and food content the significance value is 0.030 0.05, which means that there is a significant correlation.The conclusion of this study is that there is a relationship between the frequency of cariogenic foods and the content of cariogenic foods on the incidence of caries. The higher the frequency of eating cariogenic foods, the higher the incidence of caries. Keywords : cariogenic food,, caries rate
Differences in Behavior After Dental Health Counseling with Zoom Meetings for Middle School Students
Authors: Sukini Sukini , Rinawati Ni Kade , Wiradona Irmanita
Published: January 2022

RISKESDAS data shows that the number of dental health problems is about 55.6% in junior high school students in Bali Province in 2018. The reason is because of low oral health behaviour, therefore dental health counseling is expected to overcome these problems. The aim of this study was to know the differences of behavior after being given oral health counseling to junior high school students.This type of research is quasi experimental with a one-to-one design pre-test and post-test groups. The sample consisted of 60 junior high school students with a purposive sampling technique. Data analysis with Wilcoxon test.The results of statistical tests in the study showed that there were significant differences related to knowledge (p = 0.000) 0.05, attitudes (p = 0.000) 0.05, and actions (p = 0.000) 0.05, after being given counseling with zoom meeting. Most of the students during pretest had moderate knowledge, attitudes, and actions, after being given counseling with a zoom meeting, the posttest scores increased to good.Keyword : Counseling; behaviour; zoom meeting
Blueberries and health
Authors: Dunford Nurhan Turgut
Published: January 2022

Blueberry is a high value crop globally. Both wild and cultivated blueberries are commercially available to consumers. Although mostly consumed as fresh or frozen, dried blueberries are also offered in the market. Yogurts, beverages, jams, and jellies made with blueberries are some of the products popular with consumers.Blueberries are rich in several health-beneficial phytochemicals including phenolic compounds and vitamins. This article reviews the health implications of blueberry consumption. The review was limited to the research studies published in peer-reviewed scientific journals. The impact of blueberry intake on cardiovascular functions, obesity, cancer, diabetes, cognitive performance, and gut microbiota was the focus of this review.This review clearly revealed very broad health benefits of blueberry intake. However, it is evident that scientific studies on this topic are scarce. Indeed, there are only a few studies on the health effects of blueberries published in scientific journals. The majority of the investigations on this topic were carried out using animal models or cell cultures. Scarcity and limited size of the clinical studies in this field leave many questions about the effect of blueberry consumption on human health unanswered. Undoubtedly, there is a need for large-scale controlled and randomized clinical and epidemiological studies and meta-analyses of the data from such research. This would allow for an in-depth understanding of the effect of blueberries on health and the biological and metabolic pathways involved in disease mitigation and treatment.Keywords: Blueberry, cancer, cardiovascular health, chemical composition, cognitive performance, diabetes, gut microbiota, obesity, phytochemicals
A Knowledge-Model for AI-Driven Tutoring Systems
Authors: Baumgart Andreas , Madany Mamlouk Amir
Published: January 2022

A powerful new complement to traditional synchronous teaching is emerging: intelligent tutoring systems. The narrative: A learner interacts with a digital agent. The agent reviews, selects and proposes individually tailored educational resources and processes – i.e. a meaningful succession of instructions, tests or groupwork. The aim is to make personal tutored learning the new norm in higher education – especially in groups with heterogeneous educational backgrounds. The challenge: Today, there are no suitable data that allow computer-agents to learn how to take reasonable decisions. Available educational resources cannot be addressed by a computer logic because up to now they have not been tagged with machine-readable information at all or these have not been provided uniformly. And what’s worse: there are no agreed conceptual and structured models of what we understand by “learning”, how this model-to-be could be implemented in a computer algorithm and what those explicit decisions are that a tutoring system could take. So, a prerequisite for any future digital agent is to have a structured, computer-accessible model of “knowledge”. This model is required to qualify and quantify individual learning, to allow the association of resources as learning objects and to provide a base to operationalize learning for AI-based agents. We will suggest a conceptual model of “knowledge” based on a variant of Bloom’s taxonomy, transfer this concept of cognitive learning objectives into an ontology and describe an implementation into a web-based database application. The approach has been employed to model the basics of abstract knowledge in engineering mechanics at university-level. This paper addresses interdisciplinary aspects ranging from a teaching methodology, the taxonomy of knowledge in cognitive science, over a database-application for ontologies to an implementation of this model in a Grails service. We aim to deliver this web-based ontology, its user-interfaces and APIs into a research network that qualifies AI-based agents for competence-based tutoring.
Deep Learning for Knowledge Extraction from UAV Images1
Authors: Brezani S. , Hrasko R. , Vanco D. , Vojtas J. , Vojtas P.
Published: January 2022

We study possibilities and ways to increase automation, efficiency, and digitization of industrial processes by integrating knowledge gained from UAV (unmanned aerial vehicle) images with systems to support managerial decision-making. Here we present our results in the secondary wood processing industry. First, we present a deployed solution for repeated area and volume estimated calculations of wood stock areas from our UAV images in the customer’s warehouse. Processing with the commercial software we use is time-consuming and requires annotation by humans (each time aerial images are processed). Second, we present a partial solution where for computing areas of woodpiles, the only human activity is annotating training images for deep neural networks’ supervised learning (only once in a while). Third, we discuss a multicriterial evaluation of possible improvements concerning the precision, frequency, and processing time. The method uses UAVs to take images of woodpiles, deep neural networks for semantic segmentation, and an algorithm to improve results. (semantic segmentation as image classification at a pixel level). Our experiments compare several architectures, backbones, and hyperparameters on real-world data. To calculate also volumes, the feasibility of our approach and to verify it will function as envisioned is verified by a proof of concept. The exchange of knowledge with industrial processes is mediated by ontological comparison and translation of OWL into UML. Furthermore, it shows the possibility of establishing communication between knowledge extractors from images taken by UAVs and managerial decision systems.
Visual Programming for Artificial Intelligent and Robotic Application (VPAR) Framework
Authors: Pongthanisorn Goragod , Viriyavit Waranrach , Charoenporn Thatsanee , Sornlertlamvanich Virach
Published: January 2022

Computer programming is popularized in 21st century education in terms of allowing intensive logical thinking for students. Artificial Intelligent and robotic field is considered to be the most attractive for programming today. However, for the first-time learners and novice programmers, they may encounter a difficulty in understanding the text-based style programming language with its special syntax, sematic, libraries, and the structure of the program itself. In this work, we proposed a visual programming environment for artificial intelligent and robotic application using Google Blockly. The development framework is a web application which is capable of using Google Blockly to create a program and translate the result of visual programming style to conventional text-based programming. This allows almost instant programming capability for learners of programming in such a complex system.
A Proposal for a Method of Determining Contextual Semantic Frames by Understanding the Mutual Objectives and Situations Between Speech Recognition and Interlocutors
Authors: Konishi Ryosuke , Nakamura Fumito , Kiyoki Yasushi
Published: January 2022

In this study, we will examine how the speech sounds generated by one-to-one or one-to-n human communication are not only simple exchanges of intentions and opinions, but are also clearly divided into linguistic expression and linguistic understanding. In this course, we will discuss the problems of interaction between interlocutors and the complex interplay of phenomena that occur in individual interlocutors. We propose a method to determine the semantic frame of a dialogue contextually by integrating and calculating the features of speech and the features of the meaning of words.
Improving Maximum Entropy Model by GIS
Authors: Kawami Junichi , Miura Takao
Published: January 2022

Maximum Entropy Model (MEM) [1] [4] estimates probability distribution functions, by which current state of knowledge is described in the context of prior data. Here we examine Generalized Iterative Scaling (GIS) [1] algorithm to determine optimum feature weights with feature selection during learning. Maximum Entropy principle [1] provides us with all the characteristics of the data given in advance and we could expect robust distribution against outlier. However it takes much time until convergence because the computation depends heavily on the number of classes. We introduce a novel approach random sampling of Monte Carlo method into GIS for improved computation.
Quality Characteristics for User-Generated Content
Authors: Musto Jiri , Dahanayake Ajantha
Published: January 2022

Today, vast amounts of data are collected from the internet, and the general public generates most data using social networks. There is a need to have a comprehensive approach to characterize the quality of such user-generated data collection from the internet. The data quality characteristics accepted among database and computer science communities have definitions that are not domain-specific. Therefore, there is no clear understanding of the data quality characteristics specific to user-generated content. This research examines different user-generated content platforms against the general data quality characteristics to determine which quality characteristics are essential for user-generated content. The research contributes to a list of definitions of those data quality characteristics specific to user-generated content. These definitions help identify quality characteristics useful for user-generated content platforms and their implementations. The quality of the content of Atlas of Living Australia, Twitter, YouTube, Wikipedia, and WalkingPaths is evaluated to assess the essence of the quality characteristics defined in this research.
Modeling the Variety of Trip Opportunities in Recreational Route Networks
Authors: Molnár András J.
Published: January 2022

In this paper, we deal with the question of how the variety of trip opportunities can be modeled in – possibly complex – recreational trail networks (such as hiking paths or cycling ways). In order to quantify the variety of possible loop trips starting from specific trailheads (starting nodes accessible from outside the network) and the variety of connecting trips between specific origin-destination pairs, two novel measures of Loop Trip Variety Index (LTVI) and Connecting Trip Variety Index (CTVI) are proposed preliminarily and informally in [12], respectively, in the frame of assessing the impacts of some recent trail network developments. This paper establishes the formal definitions of improved variants of these measures, shows their well-definedness, presents the algorithms of their computation, investigates on their properties and benefits, and gives reasons of how and to what extent they can be treated as models of trip variety. Possible uses, application areas and future improvements are sketched especially for visitor management planning and profile-based trip recommendation systems.
The Geo CPS Platform for Designing Smart Cities
Authors: Yan Wanglin , Kiyoki Yasushi , Murakami Yoshifumi
Published: January 2022

The authors have developed the Geo CPS platform, which incorporates the advantages of cyber-physical systems, geographic information systems, and tangible user interfaces, and provides a platform to connect the three components for interactive sensing, processing, and actuation in smart city development. It can be also applied for education in environmental and disaster management, as a tool for technical training, or as a testbed for business solutions.
A Comparison of Word Embeddings to Study Complications in Neurosurgery
Authors: Danilov Gleb , Kotik Konstantin , Shifrin Michael , Strunina Yulia , Pronkina Tatyana , Tsukanova Tatyana , Ishankulov Timur , Shults Maria , Makashova Elizaveta , Latyshev Yaroslav , Sufianov Rinat , Sharipov Oleg , Nazarenko Anton , Konovalov Nikolay , Potapov Alexander
Published: January 2022

Our study aimed to compare the capability of different word embeddings to capture the semantic similarity of clinical concepts related to complications in neurosurgery at the level of medical experts. Eighty-four sets of word embeddings (based on Word2vec, GloVe, FastText, PMI, and BERT algorithms) were benchmarked in a clustering task. FastText model showed the best close to the medical expertise capability to group medical terms by their meaning (adjusted Rand index = 0.682). Word embedding models can accurately reflect clinical concepts’ semantic and linguistic similarities, promising their robust usage in medical domain-specific NLP tasks.
Prediction of Postoperative Speech Dysfunction Based on Cortico-Cortical Evoked Potentials and Machine Learning
Authors: Ishankulov Timur , Danilov Gleb , Pitskhelauri David , Titov Oleg , Ogurtsova Anna , Buklina Svetlana , Gulaev Evgeniy , Konakova Tatyana , Bykanov Andrey
Published: January 2022

The possibility of postoperative speech dysfunction prediction in neurosurgery based on intraoperative cortico-cortical evoked potentials (CCEP) might provide a new basis to refine the criteria for the extent of intracerebral tumor resection and preserve patients’ quality of life. In this study, we aimed to test the quality of predicting postoperative speech dysfunction with machine learning based on the initial intraoperative CCEP before tumor removal. CCEP data were reported for 26 patients. We used several machine learning models to predict speech deterioration following neurosurgery: a random forest of decision trees, logistic regression, support vector machine with different types of the kernel (linear, radial, and polynomial). The best result with F1-score = 0.638 was obtained by a support vector machine with a polynomial kernel. Most models showed low specificity and high sensitivity (reached 0.993 for the best model). Our pilot study demonstrated the insufficient quality of speech dysfunction prediction by solely intraoperative CCEP recorded before glial tumor resection, grounding our further research of CCEP postresectional dynamics.
Medical Informatics in a Tension Between Black-Box AI and Trust
Authors: Sariyar Murat , Holm Jürgen
Published: January 2022

For medical informaticians, it became more and more crucial to assess the benefits and disadvantages of AI-based solutions as promising alternatives for many traditional tools. Besides quantitative criteria such as accuracy and processing time, healthcare providers are often interested in qualitative explanations of the solutions. Explainable AI provides methods and tools, which are interpretable enough that it affords different stakeholders a qualitative understanding of its solutions. Its main purpose is to provide insights into the black-box mechanism of machine learning programs. Our goal here is to advance the problem of qualitatively assessing AI from the perspective of medical informaticians by providing insights into the central notions, namely: explainability, interpretability, understanding, trust, and confidence.
Machine Learning Approaches for Early Prostate Cancer Prediction Based on Healthcare Utilization Patterns
Authors: Finkelstein Joseph , Cui Wanting , Martin Tiphaine C. , Parsons Ramon
Published: January 2022

The goal of this study was to build a machine learning model for early prostate cancer prediction based on healthcare utilization patterns. We examined the frequency and pattern changes of healthcare utilization in 2916 prostate cancer patients 3 years prior to their prostate cancer diagnoses and explored several supervised machine learning techniques to predict possible prostate cancer diagnosis. Analysis of patients’ medical activities between 1 year and 2 years prior to their prostate cancer diagnoses using XGBoost model provided the best prediction accuracy with high F1 score (0.9) and AUC score (0.73). These pilot results indicated that application of machine learning to healthcare utilization patterns may result in early identification of prostate cancer diagnosis.
A Framework for User-Configurable Data Quality Assurance of Electronic Patient Records
Authors: Romanchikova Marina , Hippolyte Jean-Laurent
Published: January 2022

Electronic Patient Records (EPRs) are valuable data resources for clinical and operational research. The heterogeneity of medical software coupled with the changing data formats and long lifespan of the patient datasets stored in EPRs results in data inconsistencies that hinder operational activities and increase personnel efforts for data lookup and cleaning. This study presents an approach for automated data quality reporting that was developed and tested within a real-world hospital setting at Royal Surrey County Hospital NHS Foundation Trust in 2020. 81 data quality tests configurable via spreadsheets were defined and executed to yield standardised human-readable reports in comma-separated value format. The data evaluation and reporting routines provided manyfold improvement over existing data quality reporting mechanisms.
A Deep Learning Program to Predict Acute Kidney Injury
Authors: Li Xiaoqiang
Published: January 2022

Acute kidney injury is a dangerous and sometime fatal clinical situation, which can cause irreversible damage. If we can predict it earlier and make appropriate prevention before its outbreak, kidney injury could be avoided. One challenge of early recognition of AKI is that the most e-alerts have focused on creatinine-based algorithms, but the elevation of serum creatinine lags behind renal injury. We use recurrent neural network (RNN) to make data mining on laboratory results of MIMIC-III Database. At first, we transfer the case data into Pandas DataFrame of series framed for supervised learning. Then we can use RNN predicts the next serum creatinine values (SCr) based on the last laboratory test results after emergency admissions. We train the RNN on whole dataset (i.e. multi-cases prediction) with LSTM. As the result shown, this prototype can predict criteria (SCr) of AKI with a RMSE (Root Mean Square Error) of 0.017mg/dL.
Introduction of the EU Digital COVID Certificate in Slovenia: Technological and Process Aspects
Authors: Stanimirovic Dalibor , Tepej Jocic Lucija
Published: January 2022

In early 2021, the European Commission presented a proposal to introduce an EU Digital COVID Certificate, which should enable safe border crossings for citizens within the EU during the COVID-19 pandemic. Subsequently, all EU Member States successfully introduced the EU Digital COVID Certificate by 1 July 2021. This article focuses on a review of the technological and process aspects identified in the introduction of the EU Digital COVID Certificate in Slovenia. The research applies a case study framework, including focus group discussions, as the primary data collection method. The research findings expose the technological and process complexities related to the dispersed data sources and fairly intricate and copious business rules used for the creation of the EU Digital COVID Certificate. Moreover, the study implies that the ad hoc introduction of such demanding and sensitive digital solutions in the future will not be possible without the establishment of effective national health information infrastructures across the EU.
Smartphone-Based Healthcare Apps for Older Adults in the COVID-19 Era: Heuristic Evaluation
Authors: Saeidnia Hamid Reza , Ghorbi Ali , Kozak Marcin , Herteliu Claudiu
Published: January 2022

Many national governments have attempted to prevent and combat COVID-19 using mobile health (mHealth) technologies during the epidemic. During this time, governments began developing smartphone-based apps for prevention, call tracking, and monitoring people with COVID-19. An important question is, does everyone benefit from these technologies equally and fairly? To answer this question, we evaluated the user interface of smartphone-based apps developed during the COVID-19 era by considering their design for older adults, in order to determine whether social justice has been considered in the development of these apps.
A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods
Authors: Gharehbaghi Arash , Babic Ankica
Published: January 2022

This paper presents an original method for studying the performance of the supervised Machine Learning (ML) methods, the A-Test method. The method offers the possibility of investigating the structural risk as well as the learning capacity of ML methods in a quantitating manner. A-Test provides a powerful validation method for the learning methods with small or medium size of the learning data, where overfitting is regarded as a common problem of learning. Such a condition can occur in many applications of bioinformatics and biomedical engineering in which access to a large dataset is a challengeable task. Performance of the A-Test method is explored by validation of two ML methods, using real datasets of heart sound signals. The datasets comprise of children cases with a normal heart condition as well as 4 pathological cases: aortic stenosis, ventricular septal defect, mitral regurgitation, and pulmonary stenosis. It is observed that the A-Test method provides further comprehensive and more realistic information about the performance of the classification methods as compared to the existing alternatives, the K-fold validation and repeated random sub-sampling.
Intellicor: Mobile Design for Monitoring Phonographic Signals
Authors: Wist Jakobsen Malin , Babic Ankica
Published: January 2022

A mobile and web-based prototype was developed to explore utility of heart sound data in the context of patient self-monitoring. There are not many applications available despite measurement equipment that can be purchased. This research aimed at developing an application that could help patients understand and use phonocardiography. The resulting prototype Intellicor enables easy-to-use web and mobile solutions such as registering heart sound, review of previous heart signal recordings, summaries of terms related to patient condition, and medication taken. These functions can be utilized by both patients and physicians to create understanding of heart signals and build communication as a part of treatment. Three development iterations included several expert evaluators who gave positive feedback on the concept of the application. It was appreciated that patients could monitor heart signals and better understand the results. The medical experts would welcome such a system into their work domain if developed correctly and in accordance with the formal expectations, both legal and clinical. The prototype has shown the advantage of gathering data otherwise impossible to obtain. The Intellicor prototype presents the foundation that ought to be further developed in close cooperation of clinical and biomedical experts. The self-monitoring of this kind could benefit patients and the healthcare sector as demonstrated by the Intellicor prototype.
Implementation of a Monitoring System for Pregnancy by Tele-Ultrasound in an African Context (Burkina Faso)
Authors: Barro Seydou Golo , Zampaligre Idrissa , Staccini Pascal
Published: January 2022

Prenatal ultrasound is a radiological examination that allows optimal follow-up of pregnancies. However, its implementation remains limited in poor countries due to a lack of equipment and trained health workers, such as in Burkina Faso. The aim of this work is to set up an ultrasound tele-expertise system. To achieve this objective, we mobilized human, material and IT resources. The design of the tele-expertise application was based on a generic open source software called “MedShakeEHR” that we have adapted to our context. The application runs in a network on a Linux system. It enables ultrasound data exchange and sharing with a remote expert for interpretation using the DICOM protocol. This device thus offers the possibility to pregnant women to carry out their prenatal ultrasound locally. It also allows the constitution of prenatal ultrasound database according security and confidentiality standards.
Tracking Activities of Daily Living in the Home Office
Authors: Wang Ju , Aguirre Mehlhorn Marcel , Ditler Katharina , Effner Benjamin , Deserno Thomas M.
Published: January 2022

Due to the COVID-19 pandemic, home-office has turned to be a common practice in many companies to limit physical contact to reduce the rate of infections in the workplace. To quantify office workers’ ADLs, this work demonstrates unobtrusive monitoring of activities of daily living (ADLs) of an office worker in a home-office environment with three low-cost sensors: an accelerometer and two light sensors. We extract four elementary events: distinct and fain chair movement, monitor, and fridge usage, from which we derived seven ADLs using predefined rules. This simple system can support the quantification of ADLs of home-office workers.
Developing an Ontology for Documenting Adverse Events While Avoiding Pitfalls
Authors: Neuenschwander Stefanie , Romao Patricia , Holm Jürgen , Sariyar Murat
Published: January 2022

Ontologies promise more benefits than terminologies in terms of data annotation and computer-assisted reasoning, by defining a hierarchy of terms and their relations within a domain. Here, we present central insights related to the development of an ontology for documenting events during interoperative neuromonitoring (IOM), for which we used the Basic Formal Ontology (BFO) as an upper-level ontology. This work has the following two goals: to describe the development of the IOM ontology and to guide the practice with respect to documenting of biomedical events, as available ontologies pose difficulties on certain issues. We address the following issues: (i) differentiate between the sets documentation, identification, continuant and explanation, understanding, occurrent as we had problems in applying the available ontology of adverse events, (ii) covering diseases and injuries in a consistent way, and (iii) deciding on which level to define relations.
Identification of COVID-19 Vaccines Concerns in Health-Related French Web Forums: A Topic Modelling Approach
Authors: Karapetiantz Pierre , Audeh Bissan , Bousquet Cédric
Published: January 2022

Since December 2019 and the first reported cases of COVID-19 in Wuhan, China, there have been 199,466,211 confirmed cases of COVID-19 in the World. The WHO defined vaccination hesitancy as one of the top ten threats to global health in 2019. Our objective was thus to identify topics and trends about COVID-19 vaccines from French web forums to understand the perception of the French population on these vaccines before the vaccination campaign started. We performed a topic model analysis on 485 web forums’ posts. 10 topics were found. We reviewed 120 posts from 6 of these 10 topics. One topic was about vaccine hesitancy, refusal, and mistrust, and two topics were related to what the users think about the government, the political and economic choices made towards this epidemic.
MDA Framework for FAIR Principles
Authors: Mohammadzadeh Zahra , Saeidnia Hamid Reza , Kozak Marcin , Ghorbi Ali
Published: January 2022

This paper shows that the MDA framework can be helpful for designing and implementing FAIR principles. We reached this conclusion based on a focus group interview with six experts, during which we focused on the three MDA components: mechanics, dynamics and aesthetics.
Artificial Intelligence in Primary Care: An Overview
Authors: Turcian Darius , Stoicu-Tivadar Vasile
Published: January 2022

With the increase in computing power and the development of numerous technological devices that facilitate remote work, the involvement of artificial intelligence in medicine has seen a significant increase to help the doctor make decisions and intervene in the medical process and telemedicine. In this paper, we gave an overview of the practical involvement of artificial intelligence through different support systems used in primary medicine or telemedicine and also identified the possibilities and opportunities for the development of new support systems for family medicine. Thus, we identified systems used for primary diagnosis, diagnosis of hypertension, early detection of heart abnormalities, detection of diabetes, support in the prescription process, helping clinicians in the daily workflow by providing certain answers to questions, treatment guidance, determining patient priority for treatment for SARS-CoV-2 infection or early detection of disease, support of artificial ventilation in medical emergency centers, remote support for treatments and medication.
Usability of Venous Thromboembolism Prophylaxis Recommender System: A Pilot Study
Authors: Karajizadeh Mehrdad , Zand Farid , Vazin Afsaneh , Sharifian Roxana , Bayati Najmeh
Published: January 2022

Poor usability of clinical decision support system impact negative on healthcare professionals, decrease usage and quality of clinical decision support system and result in a negative effect on patient outcome. Therefore, the objective of this study was the usability evaluation of the venous thromboembolism prophylaxis recommendation system. This study design is a pilot study. Totally seven individuals participate in the study that 4 out of 7 were ICU attending and 3 out of 7 were Residents in ICUs setting. System Usability Scale (SUS) was used to assess the usability of the clinical decision support system (venous thromboembolism prophylaxis recommendation system) integrated into the medication order entry system in the ICU setting. This study has shown that the mean System Usability Scale (SUS) score was 74.64. Summing up the results, it can be concluded that the usability quality of the venous thromboembolism prophylaxis recommendations system is good. Further research requires to evaluate the usability of the venous thromboembolism prophylaxis recommendation system by quantitative and qualitative methods in large scale.
Towards Harmonized Data Quality in the Medical Informatics Initiative – Current State and Future Directions
Authors: Löbe Matthias , Kamdje-Wabo Gaetan , Sinza Adriana Carina , Spengler Helmut , Strobel Marcus , Tute Erik
Published: January 2022

Health data from hospital information systems are valuable sources for medical research but have known issues in terms of data quality. In a nationwide data integration project in Germany, health care data from all participating university hospitals are being pooled and refined in local centers. As there is currently no overarching agreement on how to deal with errors and implausibilities, meetings were held to discuss the current status and the need to develop consensual measures at the organizational and technical levels. This paper analyzes the discovered similarities and differences. The result shows that although data quality checks are carried out at all sites, there is a lack of both centrally coordinated data quality indicators and a formalization of plausibility rules as well as a repository for automatic querying of the rules, for example in ETL processes.
eHealth Literacy Among Health Sciences Students in Greece
Authors: Trantali Trisevgeni , Athanasopoulou Christina , Lagiou Areti , Sakellari Evanthia
Published: January 2022

eÎŮealth literacy is a necessary skill to find and make good use of online health information. However, the general public lacks this skill; it is essential for future health professionals to be able to guide and facilitate the public. The study aimed to examine the perceived eHealth literacy level of health sciences students in Greece. A cross-sectional, online survey was conducted (N=113 students). The questionnaire included socio-demographic data and the eHealth Literacy Scale (eHEALS). Participants’ mean eHEALS score was 31.9 with medicine and dentistry students having the highest score (33.7) and other health and caring sciences students the lowest (29.8). There was no statistically significant difference at eHEALS score among participants of different academic year. However, there was a statistically significant difference at eHEALS score among University Departments (p=0.009). Further research in representative samples is required to assess specific needs and improve current educational curricula.
Trust in E-Health System and Willingness to Share Personal Health Data
Authors: Bosanac Dasa , Stevanovic Aleksandra
Published: January 2022

Electronic personal health records (ePHR) are web-based tools that enable patients to access their personal health data. Since the data in PHR are systematized, they can be used in scientific research with the patient’s consent. Despite the potential benefits of using ePHR, their adoption in Croatia remains low. Multiple factors are influencing the use of PHR and willingness to share personal health data (PHD). The purpose of this paper was to determine familiarity with the e-health system among Croatian citizens, trustfulness in the system itself, and willingness to share PHD with physicians or researchers. Results showed that 34% of respondents use ePHR, and have less confidence in the electronic system than doctors’ confidentiality. However, health professionals have lover trust in doctors’ confidentiality compared to non-health workers. Respondents rated mental health data and STD data as most sensitive but are overall willing to share that data with doctors and researchers.
Assisting Data Retrieval with a Drug Knowledge Graph
Authors: Lelong Romain , Dahamna Badisse , Leguillon Romain , Grosjean Julien , Letord Catherine , Darmoni Stéfan J. , Soualmia Lina F.
Published: January 2022

The Normandy health data warehouse EDSaN integrates the medication orders from the University Hospital of Rouen (France). This study aims at describing the design and the evaluation of an information retrieval system founded on a complex and semantically augmented knowledge graph dedicated to EDSaN drugs’ prescriptions. The system is intended to help the selection of drugs in the search process by health professionals. The manual evaluation of the relevance of the returned drugs showed encouraging results as expected. A deeper analysis in order to improve the ranking method is needed and will be performed in a future work.
CARE REGIO – Digital Transformation and Technology in Nursing Care
Authors: Fuchs Dominik , Waibel Ann-Kathrin , Karthan Maximilian , Seifert Nadine , Örtl Michael , Bethge Sabrina , Fischer Gina , Waschbüsch Luca , Netzband Steffen , Kramer Frank , Mahler Andreas , Teynor Alexandra , Merli Dominik , Mess Elisabeth Veronica , Öttinger Jennifer , Schmid Stefanie , Scheu Amelie , Swoboda Walter , Friedrich Petra
Published: January 2022

Digital technologies have the potential to improve the quality of nursing care. CARE REGIO is a Bavarian joint research project for digital transformation and technology in nursing care. The project goals are supporting the nursing staff, saving time, improving the quality of care as well as increasing the quality of life and safety of those in need of care. In Phase 1 of the project, literature and stakeholder analyses, and qualitative surveys were carried out. Subsequently, central fields of action were defined for Phase 2 of the project. CARE REGIO can make a significant contribution to evaluating existing digital solutions, developing new solutions, and accelerating their implementation into practice.
Clinical Notes De-Identification: Scoping Recent Benchmarks for n2c2 Datasets
Authors: Chomutare Taridzo
Published: January 2022

Publicly shared repositories play an important role in advancing performance benchmarks for some of the most important tasks in natural language processing (NLP) and healthcare in general. This study reviews most recent benchmarks based on the 2014 n2c2 de-identification dataset. Pre-processing challenges were uncovered, and attention brought to the discrepancies in reported number of Protected Health Information (PHI) entities among the studies. Improved reporting is required for greater transparency and reproducibility.
Effects of Computer-Aided Decision Support Systems on Appropriate Antibiotic Prescribing by Medical Interns: A Quasi-Experimental Study
Authors: Rangraz Jeddi Fatemeh , Momen-Heravi Mansooreh , Nabovati Ehsan , Holl Felix , Akbari Hossein , Farrahi Razieh
Published: January 2022

Literature suggests that the adoption of guidelines for antibiotic prescribing has a significant impact on improving prescription practices of physicians; thus, this study aimed to assess the effectiveness of computer-aided decision support systems (CA-DSS) on antibiotic prescribing among medical interns. A prospective before-and-after interventional study was conducted on 40 medical interns. The interns were asked to use the CA-DSS during a one-month internship course at the infectious disease department. The main outcome measure was the knowledge of medical interns regarding the type, name, volume, usual dosages, and administration route of antibiotics prescribed. Paired t-test was applied to assess the change of medical interns’ knowledge before and after the study. There was a statistically significant difference between the mean score of interns’ medical knowledge before 5.4±2 and after 9.1±2.8 using the CA-DSS (p = 0.000). CA-DSS as an IT-based training intervention was effective for the knowledge of medical interns to prescribe the right antibiotics for acute respiratory infections.
Remote AI Supported E-Multidisciplinary Oncology Conference in Breast Cancer as a Technology and Method to Optimize Outcomes in the Peripheries
Authors: Mammas Constantinos S. , Mamma Adamantia S. , Papaxoinis George , Georgiou Irene
Published: January 2022

Aim: Feasibility-reliability control of Telemedicine Systems (TS) integrated with Multimedia Systems (MS) and Artificial intelligence (AI) for remote e-Multidisciplinary Oncology Conference in Breast Cancer. Material and Methods: Forty (n1=40) patients suffering from breast surgical oncology malignant (n2=32) and non-malignant (n3=8) diseases classified to seven categories: Nipple Discharge, Dominant Breast Mass, Occult Breast Lesion, Early Breast Carcinoma, Advanced Breast Carcinoma, Recurrent Breast Carcinoma) and treated clinically with the standard diagnostic (Mammography, US, MRI, Cytology, Pathology, BRCA1/2 Mutation Predisposition and Breast Cancer Risk Analysis) surgical, auxiliary therapeutic methods. Then clinical decisions compared to those proposed remotely by the virtual AI supported e-Oncology Conference for each patient. Results: In four (n4=4) out of forty patients (TS, MS and AI) supported decision making and surgical treatment proposal including postoperative Radiotherapy proposal was not as clear as expected. Non-output answer for non-malignant breast pathologies (n3=8) was accurately indicated by (MS and AI). Mean accuracy of (TS, MS and AI) for: 1.Surgical Operative Planning including Rad=94.1%, 2.Chem=96.8%, 3.Horm=96.7% [In 95%, (Confidence interval: 85–99%)]. Conclusion: High feasibility-reliability of the virtual AI supported e-Multidisciplinary Oncology Conference for remote decision making and surgical planning and for optimum outcomes in Breast Cancer treatment makes it a clinical necessity especially for the periphery of Hellas.