bluuebunny/crossref_metadata_2025 huggingface dataset:
Factors Influencing Islamic Education: Government Policy, Principals and Teachers
Authors: Syukur Taufik Abdillah , Anhary Tania Putri , Rieuwpassa Habel S.J. , Megan Goldwag , Limei Sun
Published: June 2023

Education is something that is familiar in life, and cannot be separated from the human soul, education is a big benchmark for every ability possessed by a person, where everyone takes education in order to achieve their goals in the future. Likewise with Islamic education, Islamic education also contains various matters concerning the religion of Islam, then Islamic education also becomes a reference for every Muslim in the world to assist them in solving every subject matter regarding religion that occurs in everyday life. Education is also an extraordinary thing to elevate one's level of life. This research also discusses the factors that will influence the policies of school principals, teachers and also the government. The purpose of this research is to find out what are the factors in Islamic education that can influence government policies, school principals, and teachers. The research method used is a method with descriptive qualitative understanding by interviewing one of the elementary school teachers in the area where the researcher lives, by asking several questions. Researchers ask several questions related to the subject matter raised. Interviews were conducted continuously until the answers were sufficient to describe the actual situation. The results of this study are that there are many influences of Islamic education that arise in government policies, principals and teachers.
Signature Identification using Digital Image Processing and Machine Learning Methods
Authors: Putra I Kadek Nurcahyo , Dewi Ni Putu Dita Ariani Sukma , Pusparani Diah Ayu , Mupu Dibi Ngabe
Published: June 2023

Signature is used to legally approve an agreement, treaty, and state administrative activities. Identification of the signature is required to ensure ownership of a signature and to prevent things like forgery from happening to the owner of the signature. In this study, data signatures were obtained from 25 people over the age of 50. The signers provided 20 signatures and were free to choose the stationery used to write the signature on white paper. The total data obtained in this study was 500 signature data. The obtained signature was scanned to create a signature image, which was then pre-processed to prepare it for feature extraction, which can characterize the signature images. The HOG method was used to extract features, resulting in a dataset with 4,536 feature vectors for each signature image. To identify the signature image, the classification methods SVM, Decision Tree, Nave Bayes, and K-NN were compared. SVM achieved the highest accuracy, which is 100%. When K=5, the K-NN method achieved a fairly good accuracy of 97.3%. Meanwhile, Naive Bayes and Decision Tree achieved accuracy significantly lower than K-NN (61%). Because the HOG method produced a large feature vector for each signature, it is recommended that important features that represent signatures be optimized or extracted to produce smaller features to speed up computation without sacrificing accuracy, and that the HOG method be compared to other extraction feature methods to obtain a better model in future research.
A Systematic Literature Review on Blockchain Technology in Software Engineering
Authors: Dzhalila Dzhillan , Siahaan Daniel , Fauzan Reza , Asyrofi Raka , Karimi Muhammad Ihsan
Published: June 2023

Blockchain technology is gaining increasing interest among software developers as a distributed and decentralized ledger for tracking the origin of digital assets. However, the application of blockchain in software engineering requires further attention. In this study, we aim to address the current challenges and explore the need for specialized blockchain practices in software engineering. Through a systematic literature review, we identify the various applications of blockchain technology in software engineering. Additionally, we conduct a thorough analysis of existing obstacles and propose potential solutions. Gathering and evaluating requirements using blockchain-based requirements engineering approaches will enhance the quality and reliability of data in software development projects. This, in turn, will improve the overall quality and dependability of software, as well as increase user interest and productivity.
Perception of Risk of Exposure to Covid-19 and Quality of Life for Nurses in the Emergency Room after Booster Vaccination
Authors: Ardiansyah Diki , Fashila Balqis , Rudiyana Wisdom , Kurnia Theresia Avila , Jauhar Muhamad
Published: June 2023

Background: nurses' perceptions of risk have an impact on the process of dealing with a pandemic as well as affecting their quality of life in terms of physical health, psychology, social relations, and the environment.Purpose: this study analyzed the relationship between perceptions of the risk of exposure to Covid-19 and the quality of life of nurses in the Emergency Room after booster vaccination.Methods: research using studies cross-sectional by sampling technique total sampling for all emergency room nurses at hospitals in Sukabumi City there were 49 people. The data collection instrument uses a risk perception questionnaire and a questionnaireWorld Health Organization Quality of Life (WHOQOL-BREF).Results: the result revealed that51% had high-risk perception and 51% had poor physical health, 63.3% had bad psychological, 57.1% had a bad environment, and 77.6% with good social relations. After doing the testChi-Square significance (α = 0.05), there is a relationship between perceptions of the risk of exposure to Covid-19 with 3 domains of quality of life including physical health (0.032), psychological (0.029), and the environment (0.029). But there is no relationship with the domain of social relations (0.196).Conclusion: Health workers, especially nurses, always have a good coping mechanism during a pandemic. Apart from that, it is hoped that nurses can improve their quality of life, such as maintaining their physical and psychological health to improve the quality of service.
The effects of DyglomeraⓇ (dichrostachys glomerata extract) on body fat percentage and body weight: a randomized, double-blind, placebo-controlled clinical trial
Authors: Youovop Janvier , Takuissu Guy , Mbopda Christelle , Nwang Felix , Ntentié Raissa , Mbong Mary-Ann , Azantsa Boris , Singh Harinder , Oben Julius
Published: June 2023

Introduction: Previous studies have shown the beneficial effects of Dyglomera®, a hydroethanolic extract of Dichrostachys glomerata, on inflammation as well as parameters linked to metabolic syndrome. Its effect on body fat was however not verified. The present study was therefore carried out to evaluate the anti-obesity effect of the Dyglomera®, on overweight and obese subjects.Methods: This 12-week randomized, double-blind placebo-controlled trial had percentage body fat measured by dual-energy X-ray absorptiometry, body weight and body mass index set as efficacy endpoints.  On the other hand, key biochemical parameters were measured as secondary endpoints.  The values of these parameters at T12 compared to T0 for the placebo and Dyglomera® groups were used as a measure of the efficacy. Results: Subjects treated with Dyglomera® for 12 weeks showed significant differences, with 6.73 kg (p<0.05) decrease in the body weight and 22.85% (p<0.05) reduction in the percentage body fat. In addition, the markers of lipid profile, adipocytokines, glycemia and transaminases plasmatic activities were also improved by the intake of Dyglomera®.Conclusions: This study shows that Dyglomera® effectively decreases the body weight in obese subjects after 12 weeks of treatment, which was accompanied by a reduction in the body fat and related disorders. Dyglomera®is a good agent for overweight and obesity management.Keywords: Dichrostachys glomerata, Dyglomera®, body fat percentage, body weight, clinical trials.
Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0
Authors: Bordel Borja , Alcarria Ramón , Chung Joaquin , Kettimuthu Rajkumar
Published: June 2023

Future Industry 4.0 scenarios are characterized by seamless integration between computational and physical processes. To achieve this objective, dense platforms made of small sensing nodes and other resource constraint devices are ubiquitously deployed. All these devices have a limited number of computational resources, just enough to perform the simple operation they are in charge of. The remaining operations are delegated to powerful gateways that manage sensing nodes, but resources are never unlimited, and as more and more devices are deployed on Industry 4.0 platforms, gateways present more problems to handle massive machine-type communications. Although the problems are diverse, those related to security are especially critical. To enable sensing nodes to establish secure communications, several semiconductor companies are currently promoting a new generation of devices based on Physical Unclonable Functions, whose usage grows every year in many real industrial scenarios. Those hardware devices do not consume any computational resource but force the gateway to keep large key-value catalogues for each individual node. In this context, memory usage is not scalable and processing delays increase exponentially with each new node on the platform. In this paper, we address this challenge through predictor-corrector models, representing the key-value catalogues. Models are mathematically complex, but we argue that they consume less computational resources than current approaches. The lightweight models are based on complex functions managed as Laurent series, cubic spline interpolations, and Boolean functions also developed as series. Unknown parameters in these models are predicted, and eventually corrected to calculate the output value for each given key. The initial parameters are based on the Kane Yee formula. An experimental analysis and a performance evaluation are provided in the experimental section, showing that the proposed approach causes a significant reduction in the resource consumption.
Reform of college students’ teaching management informatization under the background of big data and IoT
Authors: Wu Lei
Published: June 2023

With the continuous layout of intelligent processing technology in the teaching field, it has become an important trend to apply big data and Internet of Things technology to the teaching management of college students. Big data technology can comprehensively analyze the teaching situation of students through massive data, and then provide the best solution, the current big data technology already has a strong technical foundation, which can provide assistance for students’ teaching. The Internet of Things technology can collect personal information of students through sensors and miniature portable devices, and provide the data to the background server for big data analysis. In order to analyze the current status of the informationization of teaching management for college students, this paper first surveys some colleges and institutions through questionnaire surveys, and conducts data analysis on the collected questionnaires, and puts forward a solution based on big data and the Internet of Things based on the data analysis results. Finally, the combination of big data and Internet of Things technology for student teaching data collection, effect evaluation scheme, intelligent arrangement of integrated courses, intelligent recommendation scheme for students’ teaching needs, and teaching management information visualization scheme are analyzed, and it is found that the combination of big data and Internet of Things related technologies It can effectively improve the efficiency of teaching management of college students.
A MapReduce-based approach to social network big data mining
Authors: Qi Fuli
Published: June 2023

The rapid development of social networks has facilitated the convenience of users to receive information. As a network communication platform for people’s daily use, microblog has countless information data. In view of the low efficiency and poor clustering effect of K-means algorithm, a parallel K-means clustering algorithm based on MapReduce model is studied; In order to alleviate the difficulty in calculating the similarity of microblog topic text, the space vector model and semantic similarity are used to calculate the similarity between texts to improve the quality of microblog text classification. The data expansion rate of corresponding nodes under different data sets shows that the average expansion rate of the parallel K-means algorithm reaches 0.89, and the running rate is the highest. The results show that the parallel K-means algorithm has good clustering stability and the highest clustering quality, reaching 1.24; The clustering time of the algorithm is the shortest, the average clustering time is 1.27 minutes, and the clustering effect and efficiency of the algorithm are the best. In the quality analysis of Weibo topic recommendation, the accuracy of P-K-means recommendation is 95.64%, user satisfaction is 98.64%, and the recommendation effect is also the best. It shows that the research on the parallel K-means clustering algorithm based on MapReduce has the best performance in microblogging topic mining and recommendation, which can efficiently recommend topics of interest to users and enhance users’ microblogging experience.
Integrated framework for feature extraction and weighting in coal and gas outburst classification
Authors: Hu Hongqiang , Zhai Ce , Chu Yunxia , Feng Jiu , Shi Jianfeng , Liu Xuning , Zhang Genshan
Published: June 2023

The prediction of coal and gas outburst is very necessary for the prevention of gas disaster, so an outburst prediction model coupled with feature extraction and feature weighting using optimized classifier is proposed. First, Pearson correlation coefficient(PCC) and symmetric uncertainty(SU) are employed to measure the effective information in outburst sample data. Second, Kernel principal component analysis(KPCA) and linear discriminant analysis(LDA) methods are used to extract the exiting discriminate information, and the extracted linear and nonlinear feature information can effectively reflect significant information of outburst influencing factors. Third, the combination of gradient boost decision tree(GBDT) and grey relation analysis(GRA) is used to weight and fuse the extracted linear and nonlinear feature components, then form a new feature set as important discriminant information. Forth, the weighted and fused features of the coal and gas outburst influencing factors are used as the input of support vector machine(SVM) classifier with optimized parameters, it can classify outburst states, and the achieved classification accuracy can obtain 95%. Finally, the proposed model and the existing outburst classification models in literatures are used to predict outburst, then the experiment results verify the effectiveness of the proposed model and conclude that the performance of the proposed predication model are significant than present outburst prediction models.
A new characterization of the product of (L, M)-fuzzy convex structures1
Authors: Zhao Hu , Hu Xia , Chen Gui-Xiu
Published: June 2023

In order to give a characterization of the product of (L, M)-fuzzy convex structures, the notion of convex (L, M)-fuzzy hull operators is presented, it is proved that the category of (L, M)-fuzzy convex structures and the category of convex (L, M)-fuzzy hull operators are isomorphic. In particular, the lattices structure of convex (L, M)-fuzzy hull operators and a new characterization of the product of (L, M)-fuzzy convex structures are given.
MADM framework based on the triangular Pythagorean fuzzy sets and applications to college public English teaching quality evaluation
Authors: Dou Weiwei
Published: June 2023

The so-called “college English” teaching quality evaluation is to provide a basic, comprehensive, and realistic evaluation of the relevant aspects and management of teaching implementation on the basis of following the general laws of higher education; It is a comprehensive inspection of “College English” teaching and an important means of quality monitoring and policy adjustment for “College English”. As mentioned earlier, teaching evaluation is a comprehensive evaluation of teaching. Therefore, our evaluation of the quality of university public education is actually an examination of our specific measures in evaluating teaching, teaching methods and methods, teaching literature, and other aspects. The college public English teaching quality evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional dual generalized weighted Bonferroni mean (DGWBM) operator and dual generalized weighted geometric Bonferroni mean (DGWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy DGWBM (TPFDGWBM) operator and triangular Pythagorean fuzzy DGWGBM (TPFDGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for college public English teaching quality evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity.
Dimensionality reduction technique under picture fuzzy environment and its application in decision making
Authors: Devi Preeti , Kizielewicz Bartłomiej , Guleria Abhishek , Shekhovtsov Andrii , Gandotra Neeraj , Saini Namita , Sałabun Wojciech
Published: June 2023

The notion of soft matrix plays a vital role in many engineering applications and socio-economic and financial problems. A picture fuzzy set has been used to handle uncertainty data in modeling human opinion. In this work, we recall the picture fuzzy soft matrix concept and its different subsequent classes. Also, different kinds of binary operations over the proposed matrices have been provided. The main contribution of this paper is that using the concept of choice matrix and its weighted form and the score matrix, a new algorithm for decision-making has been outlined by considering the picture of fuzzy soft matrices. The current challenge In the decision-making problems is that many qualitative and quantitative criteria are involved. Hence, the dimensionality reduction technique plays an essential role in simplicity and broader applicability in the decision-making processes. We present an algorithm for the reduction process using the proposed definitions of the object and parameter-oriented picture fuzzy soft matrix and the technique to find the threshold value for the provided information. Then, illustrative numerical examples have also been provided for each proposed algorithm. A detailed comparative study of the proposed techniques has also been carried out in contrast with other existing techniques.
Solving barrier ranking in clean energy adoption: An MCDM approach with q-rung orthopair fuzzy preferences
Authors: Krishankumar Raghunathan , Pamucar Dragan
Published: June 2023

With a growing focus from the United Nations to eradicate the ill effects of climate change, countries around the world are transforming to green and sustainable habits/practices. Adoption of clean energy for demand satisfaction is a prime focus of many countries as it reduces carbon trace and promotes global development. In developing countries like India, there is an urge for sustainable global development. Literature shows that direct and complete adoption of clean energy incurs some barriers, which impede the sustainable development of the nation. Grading such barriers supports policymakers to effectively plan strategies, which motivates authors to put forward a novel decision model with integrated approaches. First, qualitative rating data on barriers and circular economy (CE) factors are collected from experts via questionnaires, which are transformed into q-rung orthopair fuzzy information (qRFI). Second, the weights of experts and CE factors are determined by the proposed variance measure and CRITIC. Third, barriers are graded by the proposed ranking algorithm that considers modified WAPAS formulation. Finally, these approaches are integrated into a model that is testified for practicality by using a case example from India. Sensitivity and comparative analyses are performed to realize the merits and limitations of the model for extant works.
Restorative Neurology and Neuroscience: Celebrating the 40th volume of an academic journal
Authors: Sabel Bernhard A.
Published: June 2023

Since the first issue of the academic journal Restorative Neurology and Neuroscience (RNN) was published in 1989, 40 volumes with a total of 1,550 SCI publications have helped advance basic and clinical sciences in the fields of central and peripheral nervous system rescue, regeneration, restoration and plasticity in experimental and clinical disorders. In this way RNN helped advance the development of a range of neuropsychiatric intervention across a broad spectrum of approaches such as drugs, training (rehabilitation), psychotherapy or neuromodulation with current stimulation. Today, RNN remains a focused, innovative and viable source of scientific information in the neurosciences with high visibility in an ever changing world of academic publishing.
A study on fracture behavior of SEN natural hybrid composite specimen under tensile load
Authors: Harsha H.M. , Satish K.G.
Published: June 2023

In this work, two reinforcements (natural jute fiber and glass fiber) hybridized with different volume fractions, are reinforced with a constant volume epoxy resin through hand layup method. The ASTM standard tensile specimen of jute natural epoxy composite of different volume fractions and orientations of fibers, with varied single edge notch (SEN) size were subjected to uniaxial tensile load in universal testing machine. Effect of jute volume fraction, notch size and fiber orientation on tensile strength and fracture toughness has been studied through experimental results. Increased percent of jute fibre showed decrease in tensile strength and fracture toughness. Also, with the increase in notch size, the tensile strength decreased and the fracture toughness increased. Further, the tensile strength and fracture toughness were superior in 0°/90° fiber orientation specimen than those with ±45° fiber orientation. Furthermore, experimental results were validated by conducting statistical and fractographic analysis. The jute fiber percentage was ranked as best level factor affecting the fracture behavior as per taguchi method. Morphological features of fractured surfaces were analyzed through scanning electron microscopic (SEM) images with respect to nature of jute fiber failure under uniaxial loading conditions.
A Hybrid AI-Based Method for ICD Classification of Medical Documents
Authors: Bruness Daniel , Bay Matthias , Schulze Christian , Guckert Michael , Minor Mirjam
Published: June 2023

Automatic document classification is a common problem that has successfully been addressed with machine learning methods. However, these methods require extensive training data, which is not always readily available. Additionally, in privacy-sensitive settings, transfer and reuse of trained machine learning models is not an option because sensitive information could potentially be reconstructed from the model. Therefore, we propose a transfer learning method that uses ontologies to normalize the feature space of text classifiers to create a controlled vocabulary. This ensures that the trained models do not contain personal data, and can be widely reused without violating the GDPR. Furthermore, the ontologies can be enriched so that the classifiers can be transferred to contexts with different terminology without additional training. Applying classifiers trained on medical documents to medical texts written in colloquial language shows promising results and highlights the potential of the approach. The compliance with GDPR by design opens many further application domains for transfer learning based solutions.
Increasing Trust in AI Using Explainable Artificial Intelligence for Histopathology – An Overview
Authors: Pesecan Cătălin-Mihai , Stoicu-Tivadar Lăcrămioara
Published: June 2023

Digital Pathology is an area that could benefit a lot from the automatic classification of scanned microscopic slides. One of the main problems with this is that the experts need to understand and trust the decisions of the system. This paper is an overview of the current state of the art methods used in histopathological practice for explaining CNN classification useful for histopathological experts and ML engineers that work with histopathological images. This paper is an overview of the current state of the art methods used in the histopathological practice for explain. The search was performed using SCOPUS database and revealed that there are few applications of CNNs for digital pathology. The 4-term search yielded 99 results. This research sheds light on the main methods that can be used for histopathology classification and offers a good starting point for future works.
Prediction of Waiting Times in A&E
Authors: Arias-Gómez Luis F. , Lovegrove Thomas , Kunz Holger
Published: June 2023

Predicting waiting times in A&E is a critical tool for controlling the flow of patients in the department. The most used method (rolling average) does not account for the complex context of the A&E. Using retrospective data of patients visiting an A&E service from 2017 to 2019 (pre-pandemic). An AI-enabled method is used to predict waiting times in this study. A random forest and XGBoost regression methods were trained and tested to predict the time to discharge before the patient arrived at the hospital. When applying the final models to the 68,321 observations and using the complete set of features, the random forest algorithm’s performance measurements are RMSE=85.31 and MAE=66.71. The XGBoost model obtained a performance of RMSE=82.66 and MAE=64.31. The approach might be a more dynamic method to predict waiting times.
Facilitating Interprofessional Collaboration on Electronic Medicines Management
Authors: Lundvoll Warth Line , Dyb Kari
Published: June 2023

Medicines are important for well-being. Thus, medication errors can have severe consequences, even death. Transfers between professionals and levels of care are a challenge in terms of medicines management. Norwegian governmental strategies encourage communication and collaboration between levels of care, and several initiatives are invested in to improve digital medicine management. In the project Electronic Medicines Management (eMM), we established an arena for interprofessional discussions about medicines management. This paper provides an example of how the eMM arena contributed to knowledge sharing and development in current medicines management practices at a nursing home. Building on communities of practice as a method we carried out the first of several sessions, with nine interprofessional participants. The results illustrate how discussion and agreement were reached on a common practice across different levels of care, and how the knowledge required bringing this knowledge back to the local practices.
Multi-Class Seizure Type Classification Using Features Extracted from the EEG
Authors: Selvaraj Abirami , PJ Swarubini , Thomas John , Rajamanickam Yuvaraj , Menon Ramshekhar N. , Agastinose Ronickom Jac Fredo
Published: June 2023

In this study, we classify the seizure types using feature extraction and machine learning algorithms. Initially, we pre-processed the electroencephalogram (EEG) of focal non-specific seizure (FNSZ), generalized seizure (GNSZ), tonic-clonic seizure (TCSZ), complex partial seizure (CPSZ) and absence seizure (ABSZ). Further, 21 features from time (9) and frequency (12) domain were computed from the EEG signals of different seizure types. XGBoost classifier model was built for individual domain features and combination of time and frequency features and validated the results using 10-fold cross-validation. Our results revealed that the classifier model with combination of time and frequency features performed well followed by the time and frequency domain features. We obtained a highest multi-class accuracy of 79.72% for the classification of five types of seizure while using all the 21 features. The band power between 11-13 Hz was found to be the top feature in our study. The proposed study can be used for the seizure type classification in clinical applications.
Leadership and Intention to Leave Among Public Health Sector Physicians in Cyprus: A National Cross-Sectional Study
Authors: Gregoriou Ioanna , Economidou Eleftheria Ch. , Avraam Demetris , Soteriades Elpidoforos S. , Papastavrou Evridiki , Charalambous Andreas , Stylianides Antonis , Merkouris Anastasios
Published: June 2023

Physician shortage is a major concern in many health care systems globally, while healthcare leadership constitutes one of the most vital factors within human resource management. Our study examined the relationship between managers’ leadership styles and physicians’ intent to leave their current position. In this cross-sectional national survey, questionnaires were distributed to all physicians working in the public health sector of Cyprus. Most demographic characteristics evaluated by chi-square or Mann-Whitney test, were statistically significantly different between those who intended to leave their job and those who did not. The results of our study demonstrated that transformational leadership has a positive influence on retention of physicians in public hospitals, while non leadership infers a negative influence. Developing leadership skills in physician supervisors is of a great importance for organizations to make a large impact on health professionals’ retention and overall performance.
Effect of Clinical and Demographic Variables on the Hospital Stay of Patients Undergoing Total Knee Arthroplasty
Authors: Marino Marta Rosaria , Bottino Vincenzo , Negri Giacomo , Stingone Maria Anna , Russo Mario Alessandro , Triassi Maria
Published: June 2023

The knee is the joint most affected by osteoarthritis and in its severe form can significantly affect people’s physical and functional abilities. The increased demand for surgery leads to greater attention by health care management to be able to keep costs down. A major expense item for this procedure is Length of Stay (LOS). In this study, several Machine Learning algorithms were tested in order to construct not only a valid predictor of LOS but also to know among the selected variables the main risk factors. To do so, activity data from the Evangelical Hospital “Betania” in Naples, Italy, from 2019–2020 were used. Among the algorithms, the best are the classification algorithms with accuracy values exceeding 90%. Finally, the results are in line with those shown by two other comparison hospitals in the area.
COVID-19 Mobile Apps Trends Derived from Long-Term Google Play Analysis
Authors: Pustozerov Evgenii , Von Jan Ute , Albrecht Urs-Vito
Published: June 2023

In this paper, we describe the 5-year trends of COVID-related mobile apps in the Google Play platform obtained by retrospectively analyzing app descriptions. Out of 21764 and 48750 unique apps available free of charge in the “medical” and “health and fitness”, there were 161 and 143 COVID-related apps, respectively. The prominentrise in apps’ prevalence occurred in January 2021.
A Conceptual Framework to Predict Disease Progressions in Patients with Chronic Kidney Disease, Using Machine Learning and Process Mining
Authors: Kandasamy Nichalini , Chaussalet Thierry , Basukoski Artie
Published: June 2023

Process Mining is a technique looking into the analysis and mining of existing process flow. On the other hand, Machine Learning is a data science field and a sub-branch of Artificial Intelligence with the main purpose of replicating human behavior through algorithms. The separate application of Process Mining and Machine Learning for healthcare purposes has been widely explored with a various number of published works discussing their use. However, the simultaneous application of Process Mining and Machine Learning algorithms is still a growing field with ongoing studies on its application. This paper proposes a feasible framework where Process Mining and Machine Learning can be used in combination within the healthcare environment.
Towards FAIR Data Standardization Using FHIR Genomics Resources Integration in Obstetrics-Gynecology Department Systems
Authors: Niţulescu Adina , Crişan-Vida Mihaela , Stoicu-Tivadar Lăcrămioara
Published: June 2023

The paper presents a current situation of the FHIR Genomics resource and an assessment of FAIR data usage and possible future directions. FHIR Genomics forges a path towards data interoperability. By integrating both the FAIR principles and the FHIR resources, we can achieve a higher standardization across healthcare data collection and a smoother data exchange. By exemplifying on the FHIR Genomics resource, we want to pave the way towards the integration of genomic data into an Obstetrics-Gynecology Information system as a future direction to be able to identify possible disease predisposition in fetus.
Use of Real-World Data to Support Adverse Drug Reactions Prevention During ePrescription
Authors: Dimitriadis Vlasios , Chytas Achilleas , Grammatikopoulou Margarita , Nikolaidis George , Pliatsika Jenny , Zachariadou Martha , Nikolopoulos Spiros , Natsiavas Pantelis
Published: June 2023

Adverse Drug Reactions (ADRs) are a crucial public health issue due to the significant health and monetary burden that they can impose. Real-World Data (RWD), e.g., Electronic Health Records, claims data, etc., can support the identification of potentially unknown ADRs and thus, they could provide raw data to mine ADR prevention rules. The PrescIT project aims to create a Clinical Decision Support System (CDSS) for ADR prevention during ePrescription and uses OMOP-CDM as the main data model to mine ADR prevention rules, based on the software stack provided by the OHDSI initiative. This paper presents the deployment of OMOP-CDM infrastructure using the MIMIC-III as a testbed.
Response Time Improvement in Medical Emergency Departments Through Evolutionary Optimization
Authors: Kyriakopoulos Constantine , Gialampoukidis Ilias , Kintzios Spyridon , Vrochidis Stefanos , Kompatsiaris Ioannis
Published: June 2023

Modern Internet connectivity provides the ability to perform efficient communications between the control centre of a healthcare system and the internal management processes of the emergency departments in clinics. Based on this, resource management is improved when exploiting the available efficient connectivity for adapting to the operating state of the system. An efficient order of patient treatment tasks inside the emergency department can reduce in real-time the average treatment time per patient. The motivation to use adaptive methods and specifically evolutionary metaheuristics for this time-sensitive task, is the exploitation of the runtime conditions which may vary according to the patient incoming flow and the severity of each specific case. In this work, an evolutionary method improves the efficiency in the emergency department, according to the dynamically structured treatment task order. Specifically, the average time inside the ED is reduced at a small expense of the execution time. This renders similar methods as candidates for resource-allocating tasks.
Pandemics and Health Crisis in ICU of Two Greek Public Hospitals
Authors: Gkolfinopoulou Anna , Kallergi Maria
Published: June 2023

The purpose of this study was to collect and analyze information on the operating conditions of intensive care units of Greek Public Hospital during the COVID-19 pandemic. The need to improve the Greek healthcare sector was widely recognized prior to and was clearly demonstrated during the pandemic when the Greek medical and nursing staff dealt with numerous problems on a daily basis. Two questionnaires were developed for data collection. One addressed issues faced by the ICUs Head Nurses and the other by the hospitals’ Biomedical Engineers. The questionnaires aimed at identifying needs and weaknesses in workflow, ergonomics, care delivery protocols, system maintenance and repair. The findings from the ICUs of two reference COVID-19 Greek hospitals are reported here. Significant differences were observed in the biomedical engineering services but both hospitals encountered similar ergonomic issues. Data collection from other Greek hospitals is in progress. Final results will be used as a guide to develop novel, time and cost-efficient strategies in ICU care delivery.
Prediction of Hospital Length Stay for Patients Undergoing Mastectomy
Authors: D’Onofrio Gaetano , D’Amore Antonio , Onofaro Filippina , Caputi Emanuele , Napoli Annalisa , Triassi Maria , Marino Marta Rosaria
Published: June 2023

The prolonged length of stay is an important aspect to be considered for the healthcare management since this affect both the health-related expenditure of the hospital and the quality of the offered service. In the light of these consideration is important for hospitals to be able to predict the LOS of patients and to work on the principal aspect affecting it in order to reduce LOS as much as possible. In this work we focus on patients undergoing mastectomy. The data were collected form 989 patients who underwent mastectomy surgery in the Surgery Department of the AORN “A. Cardarelli” of Naples. Different models have been tested and characterized and the one with the best performance was identified.
Machine Learning for Diagnosis and Screening of Chronic Lymphocytic Leukemia Using Routine Complete Blood Count (CBC) Results
Authors: Padmanabhan Regina , El Alaoui Yousra , Elomri Adel , Qaraqe Marwa K. , El Omri Halima , Yasin Taha Ruba
Published: June 2023

The comprehensive epidemiology and global disease burdens reported recently suggest that chronic lymphocytic leukemia (CLL) constitutes 25–30% of leukemias thus being the most common leukemia subtype. However, there is an insufficient presence of artificial intelligence (AI)-based techniques for CLL diagnosis. The novelty of this study is in the investigation of data-driven techniques to leverage the intricate CLL-related immune dysfunctions reflected in routine complete blood count (CBC) alone. We used statistical inferences, four feature selection methods, and multistage hyperparameter tuning to build robust classifiers. With respective accuracies of 97.05%, 97.63%, and 98.62% for Quadratic Discriminant Analysis (QDA), Logistic Regression (LR), and XGboost (XGb)-based models, CBC-driven AI methods promise timely medical care and improved patient outcome with lesser resource usage and related cost.
Using Technology to Enhance Community Health and Territorial Resources Access
Authors: Portela Diana S. , Webber Thais , Bowles Juliana
Published: June 2023

Improving access to health information and territorial resources can be a way to help strengthen health-related quality of life perception in disadvantaged communities. We argue that, in countries facing diverse economic and social disparities and challenges, technology can be leveraged to improve communities’ access to evidence-based, up-to-date, comprehensive, and culturally appropriate supportive territorial resources, thereby improving individuals’ quality of life, especially for those living with one or more chronic diseases. This paper discusses the benefits of introducing and designing a user-friendly, relevant, and effective web-based technology to broaden patients’ access to healthcare resources and tailored health information, supporting community-based quality of life transformation.
Did We Have Sex? Proposal for an eHealth Solution to Warning Sexual Contacts of an STI Risk
Authors: Bosmans Joren , Buyl Ronald
Published: June 2023

Partner notificationis an important part of reaching people at risk for aSTI and getting them screened. There are however several barriersreducing the effectiveness of traditional partner notification. To overcome these barriers an eHealth appis proposed which helps people trace their sexual contacts in an anonymous way and find reliable information about safety and testing. This should allow people to warn their at risk contacts more easily when they test. As this is a rather new way of tracing sexual contacts, a multi-disciplinary approach is needed to study the possibilities of this technique.
From Raw Data to FAIR Data: The FAIRification Workflow for Brazilian Tuberculosis Research
Authors: Bernardi Filipe , Lima Vinicius , Sartoretto Gabriel , Baiochi João , Cassão Victor , Kritski Afrânio , Rijo Rui , Alves Domingos
Published: June 2023

Among the main factors that negatively influence the decision-making process, it is possible to highlight the low quality, availability, and integration of population health data. This study aims to highlight the difficulty of research based on tuberculosis data available in Brazil. The FAIR methodology is a solution for standardizing data and sharing information about the disease. All the main actors involved, including those who generate data and administrators of information systems, should be encouraged to know their strengths and weaknesses. Continuously fostering strategies to promote data quality is, therefore, a strong stimulus for strengthening national health information systems and can potentially benefit from recommendations on how to overcome the inherent limitations of these information systems. Data quality management in Brazilian tuberculosis information systems is still not carried out organized and systematically. According to the FAIR principles, the evaluation demonstrates only 37.75% of compliance.
ClinApp: A Microservices-Based Platform for Efficient Medical Visit Scheduling
Authors: Lagakis Paraskevas , Logaras Evangelos , Billis Antonis , Stamkopoulos Evangelos , Dimitriadis Ilias , Vakali Athena , Bamidis Panagiotis D.
Published: June 2023

Clin App is a platform streamlining medical appointment management and patient data collection using a conversational agent. Focused on healthcare professionals and patients, it offers appointment automation, questionnaire creation, and medical data management. This work showcases ClinApp’s microservices-based architecture and its user-centered design.
eHealth4U: A DEMO of a Prototype National Electronic Health Record for Cyprus
Authors: Neocleous Andreas , Papaioannou Maria , Savva Panayiotis , Miguel Francisco , Yiasemi Constantinos , Panayides Andreas , Antoniou Zinonas , Neofytou Marios , Michael Christos , Melios Panayiotis , Constantinou Ioannis , Cânciu Ionuţ-Cristian , Adamides Giorgos , Christodoulou Marios , Pattichis Constantinos
Published: June 2023

In this paper we present a demonstration of a prototype national Electronic Health Record platform for Cyprus. This prototype is developed using the HL7 FHIR interoperability standard in combination with terminologies widely adopted by the clinical community such as the SNOMED CT and the LOINC. The system is organized in such a way to be user-friendly for its users, being the doctors and the citizens. The health-related data of this EHR are separated into three main sections, being the “Medical History”, the “Clinical Examination” and the “Laboratory results”. Business requirements include the Patient Summary as defined by the guidelines of the eHealth network and the International Patient Summary which are used as the base for all the sections of our EHR, together with additional medical information and functionality such as the organization of medical teams or the history of medical visits and episodes of care. From the doctor’s point of view, one can search for patients who have granted the doctor with a consent and read or add/edit their EHR data by initiating a new visit as defined in the Cyprus National Law for eHealth. At the same time, doctors can organize their medical teams by managing the locations of each team and the members that belong to each team.
Is Medical Informatics a Scientific Discipline or Just Applied Computer Science?
Authors: Sariyar Murat
Published: June 2023

The aim of this paper is to investigate whether and how medical informatics can claim to have a sound scientific basis. Why is such clarification fruitful? First, it provides a common ground for the core principles, theories and methods used to gain knowledge and to guide the practice. Without such a ground, medical informatics might be subsumed to medical engineering at one institution and to life sciences at another institution or might be just regarded as an application domain within computer science. We will provide a succinct outline of the philosophy of science, after which we provide an application of the related notions in order to decide the scientific status of medical informatics. We justify viewing medical informatics as an interdisciplinary field with a paradigm that can be formulated as “user-centered process-orientation in the healthcare setting”. Even if MI is not merely applied computer science, it still remains uncertain whether it will attain the status of a mature science, especially without comprehensive theories.
Implementation of 3D Printing in Various Healthcare Settings: A Scoping Review
Authors: Baig Mansoor Ali , Norah Albedah , Haifa AlDakhil , Nouf AlTuraiki , Baig Saniyah M.
Published: June 2023

3D printing has been one of the recent buzzwords, along with Machine learning and AI. The combination of these three provides a great deal of improvisation in health education and healthcare management techniques. This paper studies various implementations of 3D printing solutions. Shortly, AI combined with 3D printing would revolutionize the healthcare industry in most areas, not just limited to human implants, pharmaceuticals, tissue engineering/regenerative medicine, education, and other evidence-based decision support systems. 3D printing is a manufacturing method in which objects are made by fusion or depositing materials such as plastic, metals, ceramics, powder, liquids, or even living cells in layers to produce a desired 3D-Object.
Comparing Emotional Valence Scores of Twitter Messages from Human Coding and Machine Learning Algorithms Among Hispanic and African American Family Caregivers of Persons with Dementia
Authors: Yoon Sunmoo , Broadwell Peter , Tipiani Dante , Bristol Amanda , Moon Soyoung , Yoon Brian , Liu Jianfang , Huang Niya , Davis Nicole
Published: June 2023

We compared emotional valence scores as determined via machine learning approaches to human-coded scores of direct messages on Twitter from our 2,301 followers during a Twitter-based clinical trial screening for Hispanic and African American family caregivers of persons with dementia. We manually assigned emotional valence scores to 249 randomly selected direct Twitter messages from our followers (N=2,301), then we applied three machine learning sentiment analysis algorithms to extract emotional valence scores for each message and compared their mean scores to the human coding results. The aggregated mean emotional scores from the natural language processing were slightly positive, while the mean score from human coding as a gold standard was negative. Clusters of strongly negative sentiments were observed in followers’ responses to being found non-eligible for the study, indicating a significant need for alternative strategies to provide similar research opportunities to non-eligible family caregivers.