bluuebunny/crossref_metadata_2025 huggingface dataset:
Industrial technology development features of the stamped coal blend coking
Authors: Miroshnichenko Denis , Maryna Cherkashyna , Alla Sokolova , Igor Shulga , Kravchenko Sergiy , Gorbunov Kostiantyn , Sakun Antonina , Zhuha Oleh , Lebedev Vladimir
Published: August 2024

The technology of stamping is one of the main special methods of preparing coal blends to increase the coke production economic indicators. The legal regulation peculiarities to ensuring environmental industrial enterprises’ safety through the development of stamped blends coking technologies in Ukraine are considered. The effective industrial technologies development for the coking stamped blends coking, taking into account the environmental requirements for industrial production, using the coke production waste - float concentrates and sludges from coal processing plants, is very important. A comprehensive study of the coking raw material base components properties was carried out. Based on this, several options for coal blends composition were developed and experimentally tested. It is justified that in order to increase the height of the furnace for coking the stamped blend, a proportional increase in the strength of the stamped cake is necessary. This ensures the stability of the cake when it is loaded into the coking chamber and prevents complications in the furnace’s operation.
Urban space optimization using TOPSIS: A case study of Casablanca's Ghandi underpass
Authors: Bouchaqour Mariame , Ouadif Latifa , Bahi Lahcen
Published: August 2024

This study delves into the optimization of urban underground space by applying the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, focusing on the Ghandi underpass in Casablanca as a case study. Addressing the pressing need for efficient traffic management and urban planning in densely populated areas, this research evaluates the effectiveness of the TOPSIS method in selecting optimal solutions for underground infrastructure projects. By analyzing various criteria including cost, environmental impact, construction feasibility, and social benefits, the study offers a comprehensive evaluation of the Ghandi underpass project. The research methodology involves a detailed assessment of factors influencing urban underground infrastructure success. Cost analysis covers initial construction expenses and long-term maintenance, while environmental impact considers carbon footprint and disruption to local ecosystems. Construction feasibility examines technical challenges and the project timeline, and social benefits are evaluated to understand broader community impacts, including traffic flow improvements, congestion reduction, and enhanced urban space utilization. Findings reveal significant improvements in traffic flow and urban space utilization postimplementation, underscoring the TOPSIS method's potential as a robust decision-making tool for urban planners and policymakers. TOPSIS provides a structured approach to urban infrastructure development by prioritizing projects that maximize benefits across multiple criteria. This research contributes to urban planning by offering valuable insights into decision-making models for urban environment enhancement, demonstrating that TOPSIS can effectively guide urban infrastructure development to ensure feasibility and economic, environmental, and social benefits. The Ghandi underpass case study highlights the broader applicability of TOPSIS in optimizing urban underground spaces for growing urban populations.
Gender disparities in rural teacher education and empowerment in Western China
Authors: Yang Qianli , Biswas Rakheebrita
Published: August 2024

Although everybody accepts that gender imbalance existed in China's educational framework during the 1980s, there is clashing proof in the country's financial matters of education writing about changes in gender disparity in educational accomplishment over time. A few investigations guarantee there is, as yet, a critical gender imbalance in the 30 years, while others report progress. The purpose of this study is to examine the progress China has made in eliminating gender imbalance. This article uses a method of meta-examination. A procedure that offers a new quantitative examination of a sizable sum of data observational exploration on contrasts in gender and education. The exposition inspects various education models. It spreads over numerous grade levels, races, and both time and geology. The results show that there is still a gender imbalance in educational accomplishment, but it has been getting more modest over the long run. This theory is a brief synopsis of a review that investigated gender contrasts in Western China's rural teacher empowerment and education frameworks. The review tried to reveal insight into the gender disparities in teacher empowerment software engineers, preparing open doors and admittance to superior grade education in this locale's rural networks.
Emotional design in contemporary interior spaces: User experience and emerging trends
Authors: Rui Liang , Firzan Muhammad
Published: August 2024

Interior designs are well known for their contemporary aesthetic beauty, pleasurable aspects, and emotional elements. It requires not only knowledge of civil engineering, but also psychological theory and artistic skill. A creative interior space takes into consideration the physical surroundings, personal emotions, and cultural context. It is an unsustainable if the interior designs without considering the emotional needs of users. However, the interior designers often neglect the emotional elements. They are more focused on physiological needs. In addition, the traditional Chinese style cultural artistic skill has become incapable of meeting the requirements of contemporary interior designs. All the processes, such as manufacturing, use of raw materials, and the functionality of furniture, are no more according to the emotional needs of users. There are concerns regarding how interior design can actually meet user s’ needs while enhancing cultural characteristics. This study explored emotional design in current interior spaces and highlighted current trends in interior design. Furthermore, the current work examined the influence of emotional design and cultural design in the New Chinese Decorative Style. A qualitative methodology was used to fulfill the study objectives and draw reliable conclusions. The data were collected from walking interviews conducted with the users of emotional designs in different showrooms. A useful technique thematic analysis for analyzing qualitative data. The findings, derived from 15 users, indicate that three levels of emotional design - visual aesthetic, functional use, and individual life - are evident in the experience of end-users within showrooms featuring the New Chinese Decorative Style. A cozy atmosphere, color patterns, minimalistic furniture, and visual harmony and unity in items were significantly linked to users’ emotions and pleasure in interior design. Thus, the findings indicated that integrating aesthetics and pleasure with emotions in interior design can significantly enhance customers’ experiences.
Chemical composition, antioxidant and antifungal activities of Lavandula dentata against onychomycosis due to Cladosporium cladosporioides
Authors: Maouni Safae , Ettakifi Hajar , Mahiou Noureddine , Jaouani Moussa , Taheri Fatima Zahra , Kaoutar Abbassi , Erbiai El Hadi , Lamrani Zouhaire , Esteves da Silva Joaquim C.G. , Maouni Abdelfettah , Saidi Rabah
Published: August 2024

The chemical composition and the antioxidant and antifungal activities of extracts and essential oils (EOs) of Lavandula dentata (Ld) harvested in north of Morocco has been analyzed by team of researchers. The extraction of EOs from the dried leaves with stems was carried out by hydrodistillation and analyzed by GC‒MS. The essential components identified among 27 compounds in EOs were Lavandulyl isobutyrate (22.35%), 1,3-Cyclohexadiene (15.03%), 5-butyl-, Pinocarveol (8.21%), L-Fenchone (7.65%), Myrtenal (6.57%), Camphor (6.00%) and α-Pinene (4.73%). The extraction of biomolecule was carried out using different solvent and analyzed by Spectrophotometry and LC‒MS. The maximum total phenolic and flavonoid content were obtained in flowers when extraction with aqueous acetone or methanol was used. The analysis and quantification of phenolic compounds of L. dentata leaves with stems in methanolic extracts has shown 21 elements; mainly, Rutin (2133.00 µg/g of dry weight) , Apigenin 7-glucoside (837.40 µg/g of dw) and Luteolin 7-glucoside (823.30 40 µg/g of dw). The antioxidant activity showed that the IC50 of different solvent and plant parts are overall less than 1 mg/mL, with the exception of acetonic extract and EOs of flowers. The smaller IC50 was observed in the aqueous methanolic extract for flowers, leaves with stems and EOs, respectively 0.14 mg/mL, 0.2 mg/mL and 0.23  μL/mL). The antifungal test results against Cladosporium demonstrated that the MIC of essential oils extracted from Ld leaves with stems was 1 mg/mL, which is lower than that of extracts from Ld leaves with stems and flowers, which ranged from 5 to 6 mg/mL. Meanwhile, the MFC of the essential oils was 9 mg/mL, lower than that of the extracts, which was above 12 mg/mL. Two pharmaceutical fungicides have also been tested and have shown their effectiveness at very significant values of MIC, but for MFC, terbinafine has proven to be more efficient than fluconazole. The results highlight the superior antifongic efficacy of essential oils compared to organic extracts. This suggests that these oils could be considered for in vivo therapeutic use, either topically or systemically, alone or combined with terbinafine, which outperforms fluconazole.
Pengaruh Penambahan Tepung Wortel (Daucus carota L.) dengan Konsentrasi yang Berbeda dalam Pakan Komersial terhadap Intensitas Warna Ikan Cupang Halfmoon (Betta splendens)
Authors: Al Arif Sofriyadi  , Sarmin  , Denny Indra Yudistira  , Aryanti Indah Setyastuti  , RR. Nurina Ayu  , Any Kurniawati  , Mardiyana
Published: August 2024

Ornamental fish is a type of fish that lives in fresh water or in the sea that has a beautiful and attractive body shape or color. One type of freshwater ornamental fish that has a unique difference from the others is Betta ornamental fish. Betta fish is one type of ornamental fish that is in demand by the public, but the breeders of this betta fish are mostly confused about how to increase the brightness of the color of betta fish. Based on this background, this study aims to determine the effect of adding carrot flour with different concentrations in commercial feed on the color intensity of halfmoon betta fish (Betta splendens).This study used an experimental method with a Complete Randomized Design (RAL) of 4 treatments and 3 repeats. Then the variables observed include, fish brightness, fish weight, fish length, fish daily length, fish survival, and water quality. Furthermore, the observed data and data processing is carried out with SPSS statistical software 25.The addition of carrot flour in commercial feed can affect markedly (P>0.05). The addition of P4 treatment carrot flour at a dose of 95% pellets and 15% carrot flour in commercial feed, gave good results on the brightness of the color of Betta fish with an average value of 5.0 compared to other treatments that were given the addition of less carrot flour.
Hubungan Kepatuhan Diet, Asupan Vitamin C, Aktivitas Fisik dan Edukasi dengan Kadar Gula Darah Pasien Diabetes Melitus Tipe 2 pada Masa Pandemi
Authors: Manalu Asdiniyati , Abdurrachim Rijanti , Magdalena
Published: August 2024

Diabetes melitus tipe 2 adalah penyakit gangguan metabolik yang ditandai oleh kenaikan gula darah. Penyakit ini berpengaruh terhadap kualitas sumber daya manusia dan berdampak pada peningkatan biaya kesehatan. DM juga merupakan salah satu penyakit penyerta COVID-19. Penelitian ini menganalisis hubungan antara kepatuhan diet, asupan vitamin C, aktivitas fisik dan edukasi dengan kadar gula darah pasien diabetes melitus tipe 2 pada masa pandemi COVID-19 di RSD Idaman Kota Banjarbaru. Jenis penelitian berupa observasional analitik dengan rancang penelitian cross sectional. Sampel diambil dengan teknik non probability sampling berdasarkan kriteria inklusi dan eksklusi sebanyak 40 orang. Data primer diperoleh melalui wawancara. Data sekunder diperoleh melalui arsip dan dokumen di rekam medis RSD Idaman Banjarbaru. Analisa data menggunakan uji korelasi Rank Spearman dengan tingkat kepercayaan 95%. Hasil karakteristik responden berusia 46-55 tahun 40%; jenis kelamin perempuan 80%, pendidikan SMA/Sederajat 35%, pekerjaan ibu rumah tangga 52,5%; tidak menerapkan diet 60%; asupan vitamin C kurang 47,5%, aktivitas fisik ringan 37,5%, telah mendapatkan edukasi 55% dan kadar gula darah tidak terkontrol 67,5%. Sehingga didapatkan bahwa ada hubungan bermakna antara kepatuhan diet, asupan vitamin C, aktivitas fisik, dan edukasi dengan kadar gula darah pasien DM tipe 2 di RSD Idaman Banjarbaru.
Hubungan Persepsi, Aktivitas Fisik, Pola Makan, dan Indeks Massa Tubuh (IMT) dengan Kejadian Diabetes Melitus Tipe 2
Authors: Hikmah Nur , Mahpolah  , Widyastuti Hariati Niken
Published: August 2024

Kota Banjarbaru menempati peringkat ke-3 pada kasus dan peringkat ke-2 pada kematian penderita DM di seluruh wilayah Kalimantan Selatan dari tahun 2017-2018. Berdasarkan data Puskesmas Landasan Ulin menunjukan angka peningkatan kasus pada tahun 2016 ada 266 penderita dan pada tahun 2018 meningkat menjadi505 penderita. DM tipe 2 yang tidak terkontrol dapat menyebabkan komplikasi seperti kerusakan mata, ginjal, pembuluh darah, syaraf dan jantung. Tujuan untuk mengetahui hubungan persepsi, aktivitas fisik, pola makan, dan indeks massa tubuh (IMT) dengan kejadian diabetes melitus tipe 2 pada masyarakat di wilayah Puskesmas Landasan Ulin. Jenis penelitian observasional analitik, dengan desain cross sectional. Sampel berjumlah 110 orang diambil dengan cara purposive sampling. Analisis data menggunakan uji Korelasi Rank Spearman pada α 0,05. Tidak ada hubungan bermakna antara persepsi (p=0,083) dan pola konsumsi protein (p=0,215) dengan kejadian diabetes melitus tipe 2, ada hubungan bermakna antara aktivitas fisik (p=0,002), pola makan (p=0,004), pola konsumsi karbohidrat (p=0,049),  lemak  (p=0,003),  serat  (p=0,004),  dan  IMT (p=0,000) dengan kejadian diabetes melitus tipe 2. Disarankan masyarakat dapat menerapakan gaya hidup sehat dengan menjaga pola makan dengan makanan bergizi seimbang dan memperbanyak aktivitas fisik seperti berolahraga.
Assessing the Potential of EEG in Early Detection of Alzheimer’s Disease: A Systematic Comprehensive Review (2000–2023)
Authors: Ehteshamzad Sharareh
Published: August 2024

Background:

As the prevalence of Alzheimer’s disease (AD) grows with an aging population, the need for early diagnosis has led to increased focus on electroencephalography (EEG) as a non-invasive diagnostic tool.

Objective:

This review assesses advancements in EEG analysis, including the application of machine learning, for detecting AD from 2000 to 2023.

Methods:

Following PRISMA guidelines, a search across major databases resulted in 25 studies that met the inclusion criteria, focusing on EEG’s application in AD diagnosis and the use of novel signal processing and machine learning techniques.

Results:

Progress in EEG analysis has shown promise for early AD identification, with techniques like Hjorth parameters and signal compressibility enhancing detection capabilities. Machine learning has improved the precision of differential diagnosis between AD and mild cognitive impairment. However, challenges in standardizing EEG methodologies and data privacy remain.

Conclusions:

EEG stands out as a valuable tool for early AD detection, with the potential to integrate into multimodal diagnostic approaches. Future research should aim to standardize EEG procedures and explore collaborative, privacy-preserving research methods.
A trust-aware model based on reliability-based friendly relationship method in IoT networks
Authors: Yang Jinsong , Hu Yuanchao , Xiao Xing , Meng Chenxu , Zeng Lingcheng , Li Xinhai
Published: August 2024

The Internet of Things (IoT) necessitates secure communication and high availability among objects at the network edge to ensure reliable object-to-object transactions. In the IoT networks, despite resource limitations, especially at the edge of the network, the potential for error is high. Therefore, a mechanism to increase the reliability, lifetime, and stability of the network is necessary. In this paper, we introduce a trust evaluation framework based on a reliability-based friendly relationship method in IoT networks. We present a conceptual trust model that captures the overall performance of the IoT social network based on parameters such as nodes’ communication history experiences. Trust in the IoT network is built upon a harmonious communication environment that aligns with the trustworthiness of each object and its ability to maintain continuous interactions. We propose an empirical Trust Indicator (TI) that captures individual agents’ experiences in IoT groups, considering the results of system executions, current experience values, and timestamps of interactions. Mathematical models are developed to analyze the dynamics of trust, including trust increase through increased reliability and collaborative interactions and trust decay due to non-cooperative interactions and lack of communication. The model parameters in IoT groups through simulation show that in this system based on the level of reliability and its increase or decrease, its direct effect can be evaluated by quantitative measurement of mean time to failure (MTTF), which is a measure of devices trust and the network itself.
From Theory to Practice: Practical Applications of the MOLD-US Framework
Authors: Engelsma Thomas , Jaspers Monique W. , Peute Linda W.
Published: August 2024

The MOLD-US framework has been developed to synthesize knowledge for (usability) researchers on aging- and disease-related barriers that can hamper the use of health information technology (HIT). However, dissemination in terms of practical applications of the framework is currently unknown and could inform industry and researchers for applying MOLD-US in practice, but also provide insights on the use of theoretical frameworks in HIT research. Therefore, a citation analysis was conducted on the paper presenting the MOLD-US framework. Nine of the 241 citations were found to report practical application(s) of the MOLD-US framework in their methods section: (1) qualitative research input (n=3), (2) research design (n=3), (3) design approaches (n=2), and (4) conceptual framework development (n=2). Future work aims to explore MOLDUS-US practical applications in the industry, through for example grey literature, but also continuously monitor novel applications to enhance the development of HIT for the aging population.
Standardizing X-Ray and Angiography Procedure Coding in Estonia: Validating TermX Through the Implementation of SNOMED CT
Authors: Marquis Merit , Bossenko Igor , Ross Peeter
Published: August 2024

Background: The existence of multiple code systems and standards has highlighted the necessity for innovative solutions to bridge these discrepancies. Objectives: This research investigates the utilisation of TermX to tackle the challenges of interoperability in radiology procedures, with a specific emphasis on angiography and X-ray modalities. Results: The study produced a revised RadLex data model and mapping guide, designed to classify radiology services using TermX. In total, 380 concepts were required to comprehensively describe all 622 procedures examined. Conclusions: Our study demonstrates the effectiveness of TermX in simplifying the process of mapping between code systems, thus enabling more efficient analysis, and reporting of data.
Linking Dutch Intensive Care Registry Data to Socioeconomic Status Scores
Authors: Koornneef Daniëlle J.M. , Brinkman Sylvia , Termorshuizen Fabian , de Keizer Nicolette F.
Published: August 2024

This study aimed to gain insight into the success rate of linking the NICE registry with SES data from CBS and to examine whether the characteristics of linked and non-linked patients differ. Although clinically relevant differences were found, in total 93,4% of the admissions were successfully linked.
Insights from the Implementation of Open Notes in Sweden
Authors: Cajander Åsa , Corneliussen Hilde G. , Hertzum Morten , Ellingsen Gunnar
Published: August 2024

The implementation of Open Notes in Sweden, granting patients access to their clinical records, has been a complex and nuanced endeavor, marked by regional variations in strategy and challenges arising from the diverging needs of healthcare providers and patients. This paper presents an interview study with managers about the implementation process in five of the 21 regions in Sweden. The aim of this study is to explore the experiences and strategies of these managers in navigating the implementation challenges. The study sheds light on the prevalent theme of uncertainty throughout the implementation journey and the strategies used to balance conflicting perspectives. The findings contribute to our understanding of Open Notes implementation and offer policymakers and healthcare organizations insights about enhancing the implementation process to optimize patient care.
A Greek Conversational Agent for Hematologic Malignancies: Usability and User Experience Assessment
Authors: Chatzimina Maria E. , Papadaki Helen A. , Pontikoglou Charalampos , Tsiknakis Manolis
Published: August 2024

Enabling patients to actively document their health information significantly improves understanding of how therapies work, disease progression, and overall life quality affects for those living with chronic disorders such as hematologic malignancies. Advancements in artificial intelligence, particularly in areas such as natural language processing and speech recognition, have resulted in the development of interactive tools tailored for healthcare. This paper introduces an innovative conversational agent tailored to the Greek language. The design and deployment of this tool, which incorporates sentiment analysis, aims at gathering detailed family histories and symptom data from individuals diagnosed with hematologic malignancies. Furthermore, we discuss the preliminary findings from a feasibility study assessing the tool’s effectiveness. Initial feedback on the user experience suggests a positive reception towards the agent’s usability, highlighting its potential to enhance patient engagement in a clinical setting.
OMOP-on-FHIR: A FHIR Server Development to Facilitate Data Interaction with the OMOP-CDM and FHIR for PGHD
Authors: Jathissa Prabath , Rohatsch Lukas , Sauermann Stefan , Hussein Rada
Published: August 2024

This paper introduces the concept of the implementation of a FHIR server for bidirectional data exchange with the OMOP-CDM. Leveraging FHIR as a metamodel, the implementation aims to promote a more interconnected and patient-centric healthcare ecosystem. The methodology involves utilizing the Java HAPI FHIR API for server architecture and validating the solution through patient data exchange with a FHIR reference implementation server. This initiative signifies a significant advancement in healthcare data interoperability, promising improved patient care quality and clinical research vigor.
FoPraNet-BW: An Infrastructure for Clinical Studies in Practice-Based Research Networks in the German Health System
Authors: Schmutz Patrick , Krauss Arthur , Dörflinger Sven , Becker Arndt , Koch Roland , Polanc Andreas , Feil Elke , Salm Claudia , Scheeser Karin , Peters-Klimm Frank , Thies Christian
Published: August 2024

General practice-based research networks have become an integral tool to gain medical knowledge from primary care in many countries. For this purpose, a scalable IT-infrastructure is presented considering the limiting peculiarities in the German health system and enabling GPs to participate in clinical studies based on their patient population. The infrastructure consists of a central study management server and local clients for each practice. It adopts to the currently limited digital connectivity of GP practices, data protection regulations for clinical data and the needs of the medical staff to manage a clinical study. The infrastructure is in production at the four university hospitals in the state of Baden-Württemberg. Until now three clinical studies with over 70 GPs and 350 Participants are successfully conducted or have been finished. Further clinical studies are in the planning stages.
Improving Healthcare Quality with a LHS: From Patient-Generated Health Data to Evidence-Based Recommendations
Authors: Santos Ricardo L. , Cruz-Correia Ricardo
Published: August 2024

One approach to enriching the Learning Health System (LHS) is leveraging vital signs and data from wearable technologies. Blood oxygen, heart rate, respiration rates, and other data collected by wearables (like sleep and exercise patterns) can be used to monitor and predict health conditions. This data is already being collected and could be used to improve healthcare in several ways. Our approach will be health data interoperability with HL7 FHIR (for data exchange between different systems), openEHR (to store researchable data separated from software but connected to ontologies, external terminologies and code sets) and maintain the semantics of data. OpenEHR is a standard that has an important role in modelling processes and clinical decisions. The six pillars of Lifestyle Medicine can be a first attempt to change how patients see their daily decisions, affecting the mid to long-term evolution of their health. Our objective is to develop the first stage of the LHS based on a co-produced personal health recording (CoPHR) built on top of a local LLM that interoperates health data through HL7 FHIR, openEHR, OHDSI and terminologies that can ingest external evidence and produces clinical and personal decision support and, when combined with many other patients, can produce or confirm evidence.
Dementia and Alzheimer’s Disease Prevalence in Bulgaria During 2018: Nationally Representative Study
Authors: Krastev Evgeniy , Krasteva Ralitsa , Fazli Mario , Tcharaktchiev Dimitar
Published: August 2024

This paper presents data analysis of a large dataset of outpatient records for the purpose of establishing accurate prevalence rates of dementia and Alzheimer’s disease in Bulgaria. The research is motivated by the lack of accurate prevalence data as well as statistics about the actual number of people affected by dementia at national level. Health data from pseudonymized outpatient records (1,378,355) of 642,013 unique patients (Male 39.88%, Female 61.67%) with visits to neurologists and psychiatrists in 2018 was mapped to an OMOP CDM relational database. The size of this dataset is one of largest in the EU context. Prevalence of dementia for all age groups [30,100+] years is 1.61% (Male 0.62%, Female 0.99%) and Alzheimer’ disease prevalence is 0.39% (Male 0.15%, Female 0.24%), where 24.34% of all the patients with dementia suffer Alzheimer’s disease. The mean prevalence rates of dementia and Alzheimer’s disease for ages above 64 are 9.52% (CI 95%, [6.98%, 12.04%]) and 1.04 % (CI 95%, [0.84%, 1.27%,]). A comparison with EU statistics yields 0.07% difference for dementia prevalence, while data visualization confirms the known patterns of prevalence with aging and gender specifics These results stimulate further research and support the development of a national strategy for dementia and Alzheimer’s disease.
Video Consultations and Environmental Sustainability – Usability’s Impact on Long-Term Use
Authors: Muli Irene , Cajander Åsa , Rahimi Ardabili Hania , Taloyan Marina , Hägglund Maria
Published: August 2024

The rapid shift to digital healthcare, accelerated by the COVID-19 pandemic, holds promise for sustainable healthcare delivery and climate change mitigation. This study evaluates the Alltid öppet application through the lens of usability and patient satisfaction and their correlation with the intention for continued use. A cross-sectional analysis of primary care patients revealed that alignment with user needs significantly predicts long-term adoption, while frustration during use discourages it. Ease of use did not significantly affect the intention to continue, suggesting that usability alone is insufficient to drive sustained engagement. These findings highlight user-centred design’s importance in digital healthcare solutions, suggesting that enduring adoption hinges on value perception and reducing user frustration. As the healthcare sector navigates its digital future, this study offers crucial insights into the design and implementation strategies that could underpin the environmental sustainability and resilience of healthcare systems.
Opportunistic Screening for Osteoporosis Using Hand Radiographs – A Feature Augmentation Study Technique (FAST)
Authors: Mohammadi Farid Ghareh , Sebro Ronnie
Published: August 2024

Patients with low bone mineral density (BMD) face an increased risk of fractures, yet are frequently undiagnosed. Consequently, it is imperative to have opportunistically screen for low BMD in patients undergoing other medical evaluations. This retrospective study encompassed 422 patients aged ≥ 50 who underwent both dual-energy X-ray absorptiometry (DXA) and hand radiographs (modality of digital X-ray) from three different vendors within a 12-month period. The dataset was randomly divided into training/validation (n=338) and test (n=84) datasets. we sought to predict osteoporosis/osteopenia and establish correlations between bone textural analysis and DXA measurements. Our results demonstrate that the deep learning model achieved an accuracy of 77.38%, sensitivity of 77.38%, specificity of 73.63%, and an area under the curve (AUC) of 83% in detecting osteoporosis/osteopenia. These findings suggest that hand radiographs can serve as a viable screening tool for identifying individuals warranting formal DXA assessment for osteoporosis/osteopenia.
Towards Accountable E-Health Policies in the Nordic Countries
Authors: Faxvaag Arild , Reponen Jarmo , Hardardottir Gudrun Audur , Vehko Tuulikki , Viitanen Johanna , Eriksen Jeppe , Koch Sabine , Nøhr Christian
Published: August 2024

The Nordic Countries are seen as forerunners in the field of digital health technologies and national implementation has been guided by sector specific strategies for many years. In the context of new European legislation such as the European Health Data Space (EHDS), a review of the existing strategies is indicated. The objective of this policy analysis is to assess and compare the scope, ambitions and extent of accountability in national-level digital health policies in the Nordic countries. The scope of the policies from the five countries were largely centred around a) empowering and activating citizens; b) a shift towards prevention and digital first; c) supporting health operations; d) doing the groundwork; e) making health data more available in research and innovation workflows and f) supporting health personnel. Finland comes out as the most ambitious country with the aim to transform their health system by means of digitalisation. Both Finland and Iceland work towards prevention and the digital first ambition due to large populations in rural areas. These two countries also present the most accountable policies, meaning that their policy documents are the most transparent as to how they arrived at the conclusions and how they are to evaluate the achievements.
Montenegrin Digital Academic Innovation Hub: Experience in Supporting Digital Health Innovations
Authors: Ognjanović Ivana , Šendelj Ramo , Laković Luka , Reich Christoph , Madan Manav , Bokor Laszlo , Kara Peter A. , Mantas John , Zoulias Emmanouil , Golob Martina , Šimšić Nataša , Orović Irena , Terzić Jelena , Radusinović Tanja , Rakočević Nada , Eraković Jevto , Roganović Milovan , Žugić Nataša , Marijanović Marijan
Published: August 2024

Montenegrin Digital Academic Innovation Hub established within Erasmus+ project DigNEST is essential institutional support for developing innovations in the field of health in academic-business cooperation and partnership. Experience of 18 months in running Hub service provides preliminary results in analysis received innovation ideas, provided support and potentials/capacities in medical informatics advancements at national, regional and global level.
Evaluating ChatGPT 4.0’s User Satisfaction Among Doctors Across Different Medical Departments
Authors: Triantafyllopoulos Loukas , Feretzakis Georgios , Tzelves Lazaros , Sakagianni Aikaterini , Verykios Vassilios S. , Kalles Dimitris
Published: August 2024

In an era increasingly focused on integrating Artificial Intelligence (AI) into healthcare, the utility and user satisfaction of AI applications like ChatGPT have become pivotal research areas. This study, conducted in Greece, engaged 193 doctors from various medical departments who interacted with ChatGPT 4.0 through a custom web application. The participants, representing a diverse range of medical specialties, received responses from the specific chatbot tailored to their specific departmental inquiries. Their satisfaction was gauged using a validated form featuring a 1-to-5 rating scale. The results highlighted a possible correlation between the doctors’ medical departments and their satisfaction levels with ChatGPT 4.0. Significantly, doctors from certain departments (like General Surgery and Cardiology) reported lower satisfaction scores, ranging from 2.73 to 2.80 out of 5, in contrast to their colleagues from departments like Biopathology and Orthopedics, who scored between 4.00 and 4.46 out of 5. This variation in satisfaction levels underscores the diverse needs within different medical specialties and illuminates both the potential of ChatGPT and the areas needing improvement, especially in delivering department-specific medical information. Despite its limitations, ChatGPT version 4.0 is emerging as a valuable tool in the medical community, indicating potential future advancements and more extensive integration into healthcare practices. The study’s findings are crucial in understanding the distinct preferences and requirements of healthcare professionals across various medical departments, thereby guiding the future development of AI tools in healthcare.
Assessing the Proficiency of LLMs with Various Tasks and Evaluators
Authors: Kim Tong Min , Lee Youngrong , Kim Chansik , Ko Taehoon
Published: August 2024

Previous studies have been limited to giving one or two tasks to Large Language Models (LLMs) and involved a small number of evaluators within a single domain to evaluate the LLM’s answer. We assessed the proficiency of four LLMs by applying eight tasks and evaluating 32 results with 17 evaluators from diverse domains, demonstrating the significance of various tasks and evaluators on LLMs.
Visualizing Future Breast Cancer Prognosis by Generative AI
Authors: Enström Clara , Tidstam Anna , Åboge Eriksson Matilda
Published: August 2024

In Sweden, 30 percent of breast cancer cases are detected between screenings, leading to later staged cancer diagnoses. Aileen Health is preventing later staged cancers by making a breast cancer prognosis with generative AI. This study investigates how breast radiologists perceive AI-generated images and their usability as cancer prognosis. Through literature review and formative usability testing, the research study emphasizes the challenges when integrating AI-generated medical images into clinical decision-making. Furthermore, our findings stress the importance of avoiding cognitive overload and following mental models. Future research should focus on radiologists’ use of breast cancer prognosis at various urgency levels, as well as AI accuracy of generated images.
Multi-Objective Performance Optimization of Machine Learning Models in Healthcare
Authors: Moradpour Maryam , Ritter Zully , Haushild Anne-Christin
Published: August 2024

Multi-objective optimization holds particular significance for medical applications, wherein enhancing sensitivity is crucial to avoid costly missed diagnoses, and maintaining high specificity is imperative to prevent unnecessary procedures. In particular, when optimizing machine learning architectures for clinical diagnostics, it becomes essential to balance target quality measures such as accuracy, sensitivity, and specificity. Therefore, we developed MOOF, a multi-objective optimization framework that employs NSGA-II and TOPSIS to simultaneously optimize the model parameters of three selected ML algorithms: random forest, support vector machine, and multilayer perceptron. Finally, we evaluated the performance of the optimized MOOF models compared to gold standard methods such as multi-score grid search and single objective optimizations. Our results show that MOOF generally outperforms other approaches by inherently providing optimal solutions, representing the trade-offs between the target objectives. In conclusion, the study supports the importance of multi-objective optimization in medical informatics, with MOOF as a powerful tool for precise ML models, potentially improving patient care and clinical decision support systems.
Developing an AI-Assisted Platform to Support Tuberculosis Care Delivery
Authors: Iribarren Sarah , Yuwen Weichao , de Cock Martine
Published: August 2024

Artificial Intelligence (AI) has the potential to “bridge the gap” between healthcare provider and patient needs in low-resource settings to deliver timely, personalized, and empathetic care to individuals with active tuberculosis.
Optimizing ICU Care: Machine Learning and PCA for Early Prediction of Renal Replacement Therapy Requirement
Authors: Mahmoud Monira , Bader Mohamed , McNicholas James , Esmeli Ramazan
Published: August 2024

Forecasting the need for Renal Replacement Therapy (RRT) in intensive care units (ICUs) at an early stage can enhance patient outcomes and optimize resource allocation. The study aimed to develop a model for early prediction of Renal Replacement Therapy (RRT) requirement within 24 hours of ICU admission, utilizing machine learning techniques and SHapley Additive exPlanations (SHAP). It assessed various models including Random Forest (RF), Neural Network (NN), and XGBoost, using data from 34,000 ICU admissions. XGBoost showed superior performance in terms of AUPRC, while RF performed better in AUC-ROC. Results were consistent before and after Principal Component Analysis (PCA) and feature evaluation analysis. The top 10 feature models outperformed the PCA model while using fewer inputs. These findings suggest the potential utility of the developed models in accurately predicting RRT requirement within 24 hours of ICU admission, aiding in efficient critical care delivery.
Automated Assessment of Driver Distraction Using Multimodal Wearable Data and Squeeze-Excitation Networks
Authors: Pavan Kaveti , Ganapathy Nagarajan
Published: August 2024

Driver distraction, crucial for road safety, can benefit from multimodal physiological signals assessment. However, fusion of heterogeneous data is highly challenging. In this study, we address this challenge by exploring 1D convolution neural network (CNN) with squeeze and excitation networks (SEcNN) on multimodal data. For this, electrocardiogram (256Hz) and respiration (128Hz) are obtained from subjects (N=10) while using textile electrodes and driving in different scenarios namely normal, texting and calling. The obtained multimodal data is preprocessed and SEcNN to identify driver distraction. Experiments are performed using Leave-one-out-subject cross validation. The proposed approach is able to discriminate drivers distraction. It is observed that SEcNN yields average accuracy 57.03% and average F1 score 54.90% for shorter segments. Thus, the proposed approach using wearable shirts could be useful for non-intrusive monitoring in real world driver scenarios.
What Kind of Transformer Models to Use for the ICD-10 Codes Classification Task
Authors: Mansour Mariem , Yilmaz Fatma , Miletic Marko , Sariyar Murat
Published: August 2024

Coding according to the International Classification of Diseases (ICD)-10 and its clinical modifications (CM) is inherently complex and expensive. Natural Language Processing (NLP) assists by simplifying the analysis of unstructured data from electronic health records, thereby facilitating diagnosis coding. This study investigates the suitability of transformer models for ICD-10 classification, considering both encoder and encoder-decoder architectures. The analysis is performed on clinical discharge summaries from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset, which contains an extensive collection of electronic health records. Pre-trained models such as BioBERT, ClinicalBERT, ClinicalLongformer, and ClinicalBigBird are adapted for the coding task, incorporating specific preprocessing techniques to enhance performance. The findings indicate that increasing context length improves accuracy, and that the difference in accuracy between encoder and encoder-decoder models is negligible.
Evaluation of Machine Learning Algorithms for Pressure Injury Risk Assessment in a Hospital with Limited IT Resources
Authors: Abi Khalil Cynthia , Saab Antoine , Rahme Jihane , Abla Jana , Seroussi Brigitte
Published: August 2024

Clinical decision support systems for Nursing Process (NP-CDSSs) help resolve a critical challenge in nursing decision-making through automating the Nursing Process. NP-CDSSs are more effective when they are linked to Electronic Medical Record (EMR) Data allowing for the computation of Risk Assessment Scores. Braden scale (BS) is a well-known scale used to identify the risk of Hospital-Acquired Pressure Injuries (HAPIs). While BS is widely used, its specificity for identifying high-risk patients is limited. This study develops and evaluates a Machine Learning (ML) model to predict the HAPI risk, leveraging EMR readily available data. Various ML algorithms demonstrated superior performance compared to BS (pooled model AUC/F1-score of 0.85/0.8 vs. AUC of 0.63 for BS). Integrating ML into NP-CDSSs holds promise for enhancing nursing assessments and automating risk analyses even in hospitals with limited IT resources, aiming for better patient safety.
A Comparison of Websites Offering Laboratory Test Information Targeted to Citizens
Authors: Monkman Helen , Bailey Dana
Published: August 2024

Laboratory (lab) tests can assist diagnosis, treatment, and monitoring illness and health. Lab results are one of the most commonly accessible types of personal health information, yet they can be difficult for consumers (e.g., patients, laypeople, citizens) to understand. Consequently, many consumers turn to digital educational resources (e.g., websites, mobile applications) to make sense of their tests and results. In this study, we compared the understandability and readability of four different consumer targeted webpages with information about a commonly ordered blood test called the Complete Blood Count (CBC). The webpages varied in terms of understandability, and only one met the threshold. None of the web pages provided any information about how to respond to lab results. Although all four webpages were quite readable, some were much longer than others. The length of webpages may impact users’ attention, ability to locate information, and determine what is most important. Future work is warranted to better understand users’ information needs and the usability and user experience of these types of websites.
Cohort Builder: A Software Pipeline for Generating Patient Cohorts with Predetermined Baseline Characteristics from Medical Records and Raw Ophthalmic Imaging Data
Authors: Mousavi Sepehr , Garjani Ali , Elwakil Adham , Brock Laurent Pierre , Dherse Alexandre , Forestier Edwige , Palaz Marine , Seiler Emilien , Duriez Alexia , Martin Thibaud , Wolfensberger Thomas , Schlingemann Reinier , Meloni Ilenia , Bergin Ciara , Tomasoni Mattia
Published: August 2024

In clinical research, the analysis of patient cohorts is a widely employed method for investigating relevant healthcare questions. The ability to automatically extract large-scale patient cohorts from hospital systems is vital in order to unlock the potential of real-world clinical data, and answer pivotal medical questions through retrospective research studies. However, existing medical data is often dispersed across various systems and databases, preventing a systematic approach to access and interoperability. Even when the data are readily accessible, clinical researchers need to sift through Electronic Medical Records, confirm ethical approval, verify status of patient consent, check the availability of imaging data, and filter the data based on disease-specific image biomarkers. We present Cohort Builder, a software pipeline designed to facilitate the creation of patient cohorts with predefined baseline characteristics from real-world ophthalmic imaging data and electronic medical records. The applicability of our approach extends beyond ophthalmology to other medical domains with similar requirements such as neurology, cardiology and orthopedics.
Developing an Open-Source, User-Friendly, OWASP-Compliant Architecture for Healthcare Web Application Testing
Authors: Murgia Ylenia , Delgado Jaime , Giacomini Mauro
Published: August 2024

Nowadays, web applications are fundamental in the healthcare sector. However, with the widespread use of this technology, risks related to cybersecurity attacks also increase. To mitigate this phenomenon, every 3-4 years, the nonprofit foundation Open Worldwide Application Security Project (OWASP) compiles a top 10 ranking of the most critical web application security risks. Along with the top 10 Web Application Security Risks, OWASP also provides the Web Security Testing Guide, which offers comprehensive guidelines for conducting security tests. This guide includes suggestions for specific tools to use when performing different tests, among other valuable insights. However, the use of these recommended tools can be costly and can require advanced technical skills and a deep understanding of security best practices and web technologies. In addition, since the OWASP work on web security is generic, it would be useful to restrict and adapt it to the healthcare area. This would help in reducing the overhead when dealing with the needed tools. The goal of this study is to make web application security assessment in healthcare more accessible by developing tools that simplify the process and makes it user- friendly. Before developing such tools, an in-depth feasibility study must be conducted to verify the existence of open-source libraries to carry out the necessary testing procedures. It will be also necessary to identify how tools could be simplified and enhanced when focusing on healthcare.
A Data Pipeline for Enhancing Quality of MAUDE-Based Studies
Authors: Shi Yuheng , Yu Yue , Feng Yubo , Gong Yang
Published: August 2024

The increasing use of MAUDE reports in patient safety research highlights the importance of understanding the processing and dissemination of open-access MAUDE data. However, the absence of a structured data pipeline undermines the reproducibility and transparency of studies relying on MAUDE data. In response, we conducted a comprehensive analysis of a recent study on endoscopic clips, assessing methodologies and results. We advocate for implementing an extract, transform, and load (ETL) pipeline, utilizing openFDA and integrating keyword search strategies and data visualization techniques. This approach aims to enhance the quality of MAUDE-based studies, ensuring their reproducibility and transparency. Moreover, ETL serves as a cornerstone in data engineering, enabling real-time data management and quality assurance, thus promoting the sustainability and collaboration of MAUDE-based patient safety research.