bluuebunny/crossref_metadata_2025 huggingface dataset:
Reputation Management of Organisations in the Public Sector: Social Listening as a Method for Analysing Big Data
Authors: Forthmann Jörg , Homann Reimund , Krämer Alexandra , Westermann Arne
Published: October 2023

Reputation management in the public sector has scientifically been rather neglected in the past. Nevertheless, the literature suggests that the strategic management of reputation has become more important for public organisations in recent years. Therefore, big data analysis with the help of social listening, i.e. retrieving relevant communication from online sources via web-crawling and analysing the results using artificial intelligence, is applied here to three public sector organisations. The results show that social listening is at least in principle suitable for measuring reputation in the public sector. Also, the reputation of public companies and private companies is different. As a consequence, public companies should aim to improve their reputation and at least an awareness of the weaknesses of public company reputation should be built.
The effectiveness of resveratrol in the management of childhood obesity
Authors: Dutka Peyton , Martirosyan Danik
Published: October 2023

Our review is focused on examining whether or not resveratrol is effective in managing and treating childhood obesity based on its beneficial health effects, while also discussing the progress made in developing functional food products containing resveratrol. Resveratrol is a natural phenolic compound found in the Veratrum grandiflorum and is known for its antioxidant properties. Resveratrol can be found in various foods like berries, grapes, pistachios, dark chocolate, peanut skins, soybeans, and pomegranates. Studies evaluating the effectiveness of resveratrol in the management and treatment of childhood obesity have been conducted both in animals and humans. Through numerous mechanisms, such as activation of AMPK, increasing lipolysis, decreasing leptin levels and leptin/soluble leptin receptor (sOB-R) ratio, managing embryonic oxidative stress biomarkers, and reducing body weight and adiposity in offspring, resveratrol has been shown to be effective in ameliorating symptoms of obesity. Thus far, researchers have established both a goal to be accomplished (treatment and management of childhood obesity) and a bioactive compound (resveratrol) to accomplish that goal, as well as appropriate dosages and times of consumption for preclinical trials, mechanisms of action, and relevant biomarkers of resveratrol. These findings have been used to conduct various preclinical trials to test the efficacy and safety of resveratrol both in vivo and on animals. To our knowledge, the following steps have not yet been completed and are required for future research; 1) clinical trials involving resveratrol supplementation to establish safety, appropriate dosage, and time of consumption in humans, 2) creation of a specialized label for resveratrol functional food products, 3) publications submitted to open access, peer-reviewed journals, 4) approval by a reliable governmental agency, 5) official establishment of the product, and 6) epidemiological studies and after-market research conducted following release of the functional food product to the general public.Keywords: Resveratrol, Obesity, Childhood, Maternal, Cardiovascular, Diabetes, Inflammation.Graphical Abstract: Effects of resveratrol on managing childhood obesity
Prognostic value of inflammatory markers for mortality in hemodialysis patients: a retrospective study with over 3-year follow-up
Authors: YAZICI Raziye , GÜNEY İbrahim
Published: October 2023

Aims: In chronic kidney disease (CKD), chronic systemic inflammation contributes to premature ageing and morbidity; it is a predictor of overall mortality. In this study, we aimed to investigate prognostic value of inflamatory markers including systemic immune-inflammation index (SII), pan-immune-inflammation value (PIV), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) for mortality outcomes in hemodialysis patients.

Methods: In this retrospective study, CKD patients on maintenance hemodialysis between January 1, 2020 and January 31, 2020 were included. SII, PIV, NLR, PLR values of the patients were calculated. SII was calculated by (neutrophil count x platelet count)/lymphocyte count; PIV was calculated by (neutrophil count x platelet count x monocyte count)/lymphocyte count. Mortality rate of the study population during approximately 38-month follow-up period was calculated. The relationships of inflamatory markers and other variables with mortality were analysed.

Results: Of 162 patients, 53.1% were male and 46.9% were female (mean age: 61.6±13.5). During 38-month follow-up period, a total of 60 patients (37%) died. Compared with surviving group, NLR values, mean age and the rate of diabetes mellitus (DM) and coronary artery disease (CAD) comorbidities were higher (p=0.012, p
Impact of Ramadan fasting on eGFR in patients with late stage chronic kidney disease
Authors: İSLAM Mahmud
Published: October 2023

Aims: Ramadan fasting is a significant religious practice observed annually by approximately 1.9 billion adult Muslims worldwide. However, its potential impact on kidney health in individuals with chronic kidney disease (CKD) remains a subject of concern. This study aimed to investigate the effects of Ramadan fasting on renal function in patients with stage 3-5 CKD and to identify any associated risk factors.

Methods: A single-center, self-controlled longitudinal observational study was conducted on 192 stable patients with stage 3-5 CKD who observed Ramadan fasting. The fasting period was about 14-15 hours per day for one month Various clinical parameters, including eGFR, blood urea nitrogen (BUN), sodium (Na+), potassium (K+), calcium (Ca++), phosphorus, parathyroid hormone (PTH), albumin, uric acid, fasting glucose, total cholesterol, triglyceride, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and hemoglobin (Hgb), were measured before and after Ramadan fasting. P0.05). Furthermore, the use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEi/ARB) did not impact eGFR (0.084). Notably, Ramadan fasting led to improvements in metabolic parameters, such as fasting glucose and lipid profile, except for triglyceride levels (p
The impact of hypothyroidism and levothyroxine treatment on preeclampsia risk: unraveling the connection for improved maternal and neonatal outcomes
Authors: ARSLANCA Şeyma Banu
Published: October 2023

Aims: Preeclampsia, a pregnancy-related complication, may develop in women with hypothyroidism. Possible risk factors may include comorbidities, predisposition to diabetes, obesity, advanced maternal age, and prior infertility treatments. The study aims to investigate the relationship between hypothyroidism and the risk of preeclampsia in pregnant women receiving levothyroxine by examining its application period.

Methods: This is a retrospective cohort included pregnant women who gave birth between December 2022-April 2023. Women with 110 preeclampsia and those without preeclampsia (152 controls) were identified and compared in terms of hypothyroidism status, type of hypothyroidism, and levothyroxine treatment.

Results: The results showed a significant association between the severity of the preeclampsia and its onset that early onset cases were more likely to be severe, while late onset cases were predominantly mild (p
Can hematologic parameters predict isolated oligohydramnios and isolated polyhydramnios?
Authors: DEMİR Mustafa Bertan , TUTUŞ Şadan , CÜNDÜBEY Cevat Rifat
Published: October 2023

Aims: We fulfilled this study to anticipate the diagnosis of isolated oligohydramnios (IO) and isolated polyhydramnios (IP) by using the first trimester value of hematologic parameters.

Methods: We conducted a retrospective research 32 and 42 weeks of gestation women with IO and IP between in a single tertiary center in Turkey. In this cohort research three groups are composed of 65 IO patients and 56 IP patients and normal 97 patients that had normal volume of amniotic fluid.

Results: While PLR were significantly increased in the IO pregnants (p 0.05).

Conslusion: PLR values were independently associated with isolated oligohydramnios but not to isolated polyhydramnios. Hematologic parameters can be helpful in predicting isolated oligohydramnios.
The effects of behavioral therapy given to men with premature ejaculation on symptoms and their partners’ sexual functioning and sexual quality of life
Authors: DOĞAN Kazım
Published: October 2023

Aims: This research aimed to evaluate the efficacy of behavioral therapy administered to men diagnosed with premature ejaculation (PE) and its consequent effects on their partners' sexual function and overall sexual quality of life.

Methods: Using a quasi-experimental pre-test post-test study design, men diagnosed with premature ejaculation and their partners from a urology outpatient clinic underwent behavioral therapy. The therapy incorporated the "stop-start technique" over six bi-weekly sessions, each lasting 45 minutes. Post-therapy evaluations were conducted after the sixth session.

Results: Post-treatment results showed a significant decrease in men’s PEDT scores from 15.53±2.09 to 7.65±3.05 (p=0.012). Concurrently, their partners experienced an increase in FSFI scores from 13.90±11.1 to 21.70±7.86 (p=0.001) and SQOL-F scores from 37.82±8.50 to 84.01±9.68 (p=0.001). Significant improvements were also recorded in the FSFI subscales for desire (2.2±1.7 to 4.2±1.14), arousal (2.3±3.7 to 4.6±3.04), lubrication (2.5±2.7 to 3.7±1.7), orgasm (2.6±2.0 to 3.5±1.3), and satisfaction (2.0±2.2 to 4.4±1.8) for the female partners post-treatment, all with p
Examining the influence of sample rejection rates on the carbon footprint of clinical laboratories: a retrospective analysis
Authors: UÇAR Kamil Taha
Published: October 2023

Aims: Clinical laboratories play a vital role in healthcare, yet their operations contribute to resource consumption, waste generation, and greenhouse gas emissions. The need for sustainable practices in laboratories has led to guidelines for reducing their carbon footprint. This study aims to assess the impact of sample rejection rates (SRRs) on laboratory sustainability by calculating the carbon footprint and medical waste generated due to rejected samples.

Methods: This retrospective, single-center study obtained data from the Hospital Information Management System for two years (2021 and 2022). SRRs were calculated for different sample tube types. The carbon footprint caused by rejected samples was calculated using CO2 emission (CO2e) conversion factors. The weight of medical waste generated due to rejected samples was evaluated. Statistical analysis was performed using appropriate tests.

Results: In 2021 and 2022, SRRs for different sample tubes were calculated, with statistically significant differences observed. The total CO2e value resulting from rejected samples over two years was 12.3 tons, and the medical waste generated was 3.7 tons. The highest SRR was observed in Blue top tubes, while yellow top tubes showed a significant reduction in SRR in 2022.

Conclusion: This study highlights the impact of SRRs on laboratory sustainability. The calculated CO2e and medical waste values underscore the need to minimize sample rejections. While these values seem minor compared to global emissions, they reflect only a portion of the potential environmental impact. Reducing sample rejections not only improves patient safety and laboratory efficiency but also aligns with the larger goal of creating environmentally conscious and sustainable healthcare practices.
The effect of nasal septum deviation type on the systemic inflammatory index and blood markers of inflammation
Authors: KANDEMİR Süheyla , PAMUK Gözde
Published: October 2023

Aims: The aim of this study was to identify the changes in blood markers of inflammation and the systemic immune inflammatory index in patients with nasal septum deviation (NSD). An additional aim was to determine if there was a significant difference in the findings according to septum deviation type.

Methods: This retrospective study included 321 patients diagnosed with NSD via physical examination and CT (NSD group), and 101 healthy controls. Standard complete blood count was performed. Main blood parameters and mean platelet volume (MPV), the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), and the systemic immune inflammatory index (SII) were recorded. NSD was classified as 3 types according to Dreher classification, and the types were compared to each other and the control group.

Results: The hemoglobin level, and platelet and neutrophil counts were significantly higher in the NSD group than in the control group (P
The role of disease activity as a determinant of body awareness and central sensitization in patients with axial spondyloarthritis: a cross-sectional study
Authors: APAYDIN Hakan , BAZANCİR APAYDIN Zilan
Published: October 2023

Aims: The aim of this study was to investigate the effects of disease activity on body awareness and central sensitization in patients with axial spondyloarthritis (axSpA).

Methods: This cross-sectional study included patients diagnosed with axSpA. Disease activity was evaluated using the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), and a score of four or higher was considered high disease activity. Patients were divided into two groups according to BASDAI: high disease activity (BASDAI ≥ 4) group (HG) and the low disease activity (BASDAI0.05). The CSI-A score was higher in HG compared to LG (44 (31-54) vs. 31 (21-41), p=0.008). The HG had poorer BAQ scores than the LG (61 (52-85) vs. 85 (64-96), p=0.017). BASDAI was moderately associated with CSI-A (r=0.145, R2=0.172, p=0.001). No significant correlation was found between BASDAI and BAQ (p=0.167). The results of the simple linear regression analysis suggested that CSI-A explained 17.2% of the disease activity. BASDAI (β = 0.415, p=0.001) significantly predicted central sensitization. BASDAI was strongly correlated with VAS (r=0.665, R2= 0.442, p
The role of carbondioxide insufflation in preventing postoperative peritoneal adhesions in rats
Authors: KARABACAK Harun , AKIN Murat , YILMAZ Tonguç Utku , YILMAZ Güldal , GÜLBAHAR Özlem
Published: October 2023

Aims: Adhesion is the pathological connections that occur during the healing with scar formation of peritoneal surface defects. CO2 is used the most frequently in laparoscopic operations for insufflation. It is believed that it causes to changes in the inflammatory reply of the pneumo-peritoneum, defects in acid-base balance and decrease in peritoneal macrophage functions. CO2 is the only gas whose immunologic effects have been shown. It has been proven in experimental studies that the CO2 insufflation causes to local peritoneal acidosis without affecting the systemic status. Moreover, it has also been shown that it decreases the pneumo-peritoneum TNF-α and IL-6 production; however, increases the IL-10 production which is an anti-inflammatory cytokine. In the literature, the relation between the laparoscopy and the postoperative adhesions has always been explained by taking the suggestion of its causing to less tissue trauma as a basis when compared with the open surgery. The inflammatory reply of the CO2 has been less dealt with. In this study, we wanted to find the answer to the question whether the capno-peritoneum has a role in preventing the postoperative adhesion formation only by using CO2 without a a laparoscopic operation.

Methods: 30 female Wistar Albino type rats whose weights varied between 250±20 were used in the study. The rats were divided into 5 groups. Each group had 6 rats. Rats were placed in standard polycarbon cages in groups of 6. The room temperature was kept in 21°C. The rats were fed with standard pellet food during the study and tap water was provided to them. The operational anesthesia was performed by injecting intramuscular Ketamine Hydrochloride (Ketalar, Parke Davis and Eczacıbaşı, İstanbul) 50 mg/kg and Xylazine hydrochloride (Rompun, Bayer HealthCare) 5 mg/kg.

Results: A meaningful difference (p
Role of hemogram parameters as predictive markers for propofol injection pain in reproductive and postmenopausal women: a prospective study
Authors: TAŞARGÖL Ömer , KÖSE Halil Cihan
Published: October 2023

Aims: In clinical practice, propofol injection pain (PIP) is a frequent condition that clinicians could face every day. The aim of this prospective study was to investigate the correlation between PIP and hemogram parameters in reproductive and postmenopausal women.

Methods: In this prospective study, 40 reproductive and 40 postmenopausal female patients who underwent elective surgery were enrolled. Baseline data including age, weight, height, hemogram parameters, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune inflammatory score (SII) were recorded preoperatively. The pain was classified as 0: no pain, 1: mild pain, 2: moderate pain, 3: severe pain. Patients were previously informed about the questioning of pain scores during propofol.

Results: The proportion of patients experiencing PIP in postmenopausal (n=16;40%) group was significantly higher than those in the reproductive group (n=6;15%) (p=0.009). When reproductive and postmenopausal groups were compared according to the presence or absence of pain, no difference was found between the groups in terms of hemogram ratios and platelet indices (p>0.05). The NLR, PLR and SII ratios were not significant in predicting the presence of PIP.

Conclusion: The findings of our study showed that the occurrence of PIP in postmenopausal women was higher compared to women in reproductive age. Hemogram values, NLR platelet, PLR and SII ratios were not significantly associated with the presence of PIP.
The effects of frailty on quality of recovery and complications in older adults undergoing major abdominal surgery: a prospective cohort study
Authors: YÜCELER KAÇMAZ Hatice , KAHRAMAN Hilal , AKIN Sibel , SÖZÜER Erdoğan
Published: October 2023

Aims: There is an increase in the number of older people who have undergone surgical intervention in proportion to the aging of the global population. This situation creates the need to adapt surgical care according to the pathophysiological profile of older people. Recently, it has been argued that chronological age alone does not explain biological age, and frailty will be an appropriate parameter in organizing surgical care of older people. This study aimed to determine the effect of frailty on 30-day postoperative complications and quality of recovery in older people undergoing major abdominal surgery.

Methods: A prospective cohort study was conducted in the General Surgery Department of the Erciyes University Medical Center in Türkiye. Overall, 222 patients aged 65 and over underwent major abdominal surgery between February 2021 and 2023. Frailty was determined using Fried Frailty Index. 30-day postoperative complications were evaluated using the Clavien Dindo Classification. Quality of recovery- 40 (QoR-40) was filled three days after surgery to determine the quality of recovery of the patients. Receiver-operating characteristic curves analysis was used to evaluate the ability to predict 30-day complications of frailty. Univariate linear regression analysis was performed to determine frailty to be an independent predictor of the QoR-40.

Results: The majority of the participants were male (56.8%), the mean age was 71.41±5.29 years, and 50% of patients were frail. Frail patients (28.1%) showed a higher rate of major complications compared to non-frail patients (9.3%). The Fried frailty index score significantly predicted 30-day postoperative complications (AUC=0.653, 95%=0.565-0.741). The total mean score of the QoR-40 scale was 147.09±15.82. Univariate linear regression analysis found frailty (OR -3.81, 95% CI -4.79- -2,83), age (OR -0,46, 95% CI -0,79- -0,12), Charlson comorbidity index (OR -2,40, 95% CI -3,23- -1,57), and operation time (OR -0,04, 95% CI -0,06- -0,02) as independent predictors of quality of recovery.

Conclusion: Frailty is a significant predictor for 30-day postoperative complications and quality of recovery in older people undergoing major abdominal surgery.
Preoperative pulmonary rehabilitation in medical inoperable patients with early stage non-small cell lung cancer and postoperative results
Authors: HAZER Seray , SATAR Seher , CANDEMİR İpek , ERGÜN Pınar , ACAR Leyla Nesrin , GULHAN Selim Şakir Erkmen
Published: October 2023

Aims: The impact of postoperative complications after surgery for lung cancer is substantial, with the increasing age of patients and the presence of comorbidities. Impairment in exercise capacity is a potential modifiable risk factor for postoperative complications. This study aimed to assess the contribution of preoperative pulmonary rehabilitation (PR) for increasing operability conditions in non-small cell lung cancer (NSCLC) for patients with limited pulmonary functions and postoperative results.

Methods: The patients with NSCLC who had preoperative pulmonary rehabilitation and underwent surgical resection in our clinics between 2010-2019 were evaluated retrospectively. The patients enrolled in a comprehensive, multidisciplinary, supervised outpatient 10-day duration PR program preoperatively, consisting of bronchial hygiene, breathing control, energy conservation techniques, exercise training (endurance and strength), psychological support, and nutritional support. Exercise capacity and VO2 peak were evaluated by using an incremental shuttle walk test (ISWT).

Results: Eighteen patients who underwent surgery due to NSCLC and had a pre-operative pulmonary rehabilitation program were evaluated. All the cases were male; the mean age was 66.2 (53-77) years. The squamous cell/adenocarcinoma ratio was 2.6 (13:5), and the mean tumor size was 4.6 (8-18) cm. The postoperative hospital stay was 12.7 (4-42) days, and they were followed up for an average of 30.2 (2-83) months.

Conclusion: Complete surgical resection is the most effective curative treatment for lung cancer. However, many patients with lung cancer also have severe COPD, increasing their risk of postoperative complications and their likelihood of being considered "inoperable." Preoperative pulmonary rehabilitation (PR) has been proposed as an intervention for risk modification and to decrease surgical morbidity and mortality. The results of our study also revealed the importance of preoperative pulmonary rehabilitation in centers where lung cancer surgery was performed.
Inter-Satellite Link Prediction with Supervised Learning Based on Kepler and SGP4 Orbits
Authors: Ferrer Estel , Ruiz-De-Azua Joan A. , Betorz Francesc , Escrig Escrig Josep
Published: October 2023

Distributed Space Systems (DSS) are gaining prominence in the space industry due to their ability to increase mission performance by allowing cooperation and resource sharing between multiple satellites. In DSS where communication between heterogeneous satellites is necessary, achieving autonomous cooperation while minimizing energy consumption is a critical requirement, particularly in sparse constellations with nano-satellites. In order to minimize the functioning time and energy consumed by the Inter-Satellite Links (ISL) established for satellite-to-satellite communication, their temporal encounters must be anticipated. This work proposes an autonomous solution based on Supervised Learning that allows heterogeneous satellites in circular polar Low-Earth Orbits to predict their encounters, given the Orbital Elements (OE) and assuming isotropic antenna patterns. The model performance is evaluated and compared in two different scenarios: 1) a simplified scenario assuming that satellites follow Kepler orbits and 2) a realistic scenario assuming that satellites follow Simplified General Perturbations 4 (SGP4) orbits. This work could be considered the first stage of a promising and alternative approach in the field of DSS.
Expert Finding for Citizen Science
Authors: Komander Bjoern , Cerquides Jesus , Piera Jaume , Chan Jeffrey , Alavi Azadeh
Published: October 2023

Citizen Science brings together scientists and public participants to collaborate on a wide range of applications and fields. With this approach, Citizen Science advances scientific research and communication while accounting for various stakeholders. Across many Citizen Science projects, digital discussion platforms play an essential role for self-governance and self-organisation. In order to increase the quality of the discussions held on these platforms, we propose a model that recommends users to new discussions in which they are likely to contribute meaningful content. Our model learns relevant user representations based on the quality of past interactions between users and discussion threads, as well as the text content of questions, using a ranking loss function, an approximation of the NDCG metric, and matrix factorization. We demonstrate that our approach is able to predict potential experts on unseen discussion threads and outperforms several baselines. Compared to state-of-the-art expert finding techniques, the architecture of our model is significantly less complex, while focusing on a mostly overlooked ranking loss function.
Addressing Scale and Density Challenges in LiDAR Point Classification
Authors: Carós Mariona , Just Ariadna , Seguí Santi , Vitrià Jordi
Published: October 2023

Semantic segmentation of LiDAR point clouds has received significant attention due to its applications in autonomous driving, forestry, and urban planning. Despite their potential, accurately classifying three-dimensional points remains a significant challenge due to the irregular distribution of data and density variation. To address this, state-of-the-art approaches use various techniques, such as voxelization, point-based networks, and graph-based methods. However, these techniques have limitations regarding the point cloud size they can handle and can be computationally expensive. Therefore, in this work, we propose a method to process point clouds of different scales and densities for point classification.
Bayesian Optimization with Additive Kernels for the Calibration of Simulation Models to Perform Cost-Effectiveness Analysis
Authors: Gómez-Guillén David , Díaz Mireia , Arcos Josep Lluís , Cerquides Jesus
Published: October 2023

The use of mathematical simulation models of diseases in economic evaluation is an essential and common tool in medicine aimed at guiding decision-making in health. Cost-effectiveness analyses are a type of economic evaluation that assess the balance between health benefits and the economic sustainability of different health interventions. One critical aspect of these models is the accurate representation of the disease’s natural history, which requires a set of parameters such as probabilities and disease burden rates. While these parameters can be obtained from scientific literature, they often need calibration to fit the model’s expected outcomes. However, the calibration process can be computationally expensive and traditional optimization methods can be time-consuming due to relatively simple heuristics that may not even guarantee feasible solutions. In this work, we investigate the use of Bayesian optimization to enhance the calibration process by leveraging domain-specific knowledge and exploiting inherent structural properties in the solution space. Specifically, we examine the effect of additive kernel decomposition and constraint handling for efficient search. Our preliminary results show that this improved Bayesian optimization procedure asymptotically improves the calibration process, leading to faster convergence and better solutions for larger simulation models.
Exploring the Role of Explainability in AI-Assisted Embryo Selection
Authors: Urcelay Lucia , Hinjos Daniel , Martin-Torres Pablo , González Marta , Méndez Marta , Cívico Salva , Álvarez-Napagao Sergio , Garcia-Gasulla Dario
Published: October 2023

In Vitro Fertilization is among the most widespread treatments for infertility. One of its main challenges is the evaluation and selection of embryo for implantation, a process with large inter- and intra-clinician variability. Deep learning based methods are gaining attention, but its opaque nature compromises their acceptance in the clinical context, where transparency in the decision making is key. In this paper we analyze the current work in the explainability of AI-assisted embryo analysis models, identifying the limitations. We also discuss how these models could be integrated in the clinical context as decision support systems, considering the needs of clinicians and patients. Finally, we propose guidelines for the sake of increasing interpretability and trustworthiness, pushing this technology forward towards established clinical practice.
Challenges in the Exploitation of Historical Clinical Data for the Classification of Diabetic Retinopathy Patients
Authors: Pascual-Fontanilles Jordi , Valls Aida , Moreno Antonio , Romero-Aroca Pedro
Published: October 2023

Analysing medical data stored in Electronic Health Records is of great interest to build clinical decision support systems. There is a lot of hidden knowledge in these databases, obtained from the continuous work of medical practitioners when attending and diagnosing patients. However, it is not easy for Machine Learning methods to exploit these data. Their use always requires a careful and complex preprocessing stage. In this paper, we study the case of diagnosis of diabetic retinopathy, which is made by ophthalmologists. The characteristics of a 12-year dataset about Type-2 diabetic people are analysed. Several numerical and categorical variables were selected by experts as relevant risk factors for this disease. We explain the challenges that are being faced in order to generate a dataset composed by time series with the same length and intervals. The final aim of the research is to build a clinical decision support system that can make a personalised prediction of the evolution of the disease.
A General Model for the Metainformation of Complex Questionnaires for Automatic Preprocessing and Reporting Under INSESS Methodology
Authors: Angerri Xavier , Gibert Karina
Published: October 2023

In the field of analytics and intelligent data analytics one of the open challenges regards to the metainformation model that allows an intelligent reporting system to preprocess and process correctly data coming from any kind of questionnaire, independently of number of variables, types of variables involved, including multiple response variables or other complex structures like compositional variables or temporal variables among others. INSESS is an intelligent system designed and used for the first time in INSESS-COVID19 project, specialized in automatic intelligent reporting for a parameterized questionnaire that includes different types of variables. From one INSESS-consultation to another, the questionnaire can change completely and the consultation may be oriented as well to different types of target populations going from citizens, to associates to an NGO or to special nonpublic populations and the pre and processing still works. The flexibility required to do this, relies on a well-founded and sufficiently general conceptual model for the metainformation associated to the questionnaire, so that the analytical module can properly reason on top of the metainformation and make intelligent decisions about the kind of preprocessing required or the kind of descriptive analysis to be applied. The metainformation model has been developed from a knowledge representation perspective.The paper presents the context of the research, the conceptual model defined and 4 real use cases where the INSESS methodology was used for different kinds of private/public consultations proving the flexibility of the tool.
Sequence Pattern Mining for Citizens Behaviour Learning in Fair-Purpose Social Games. Some Preliminary Results
Authors: López Beatriz , Campdepadros Roger , Sabater Albert
Published: October 2023

Artificial Intelligence can be used to understand how citizens interact in a society with the aim to work together towards a better world. The application of AI in this domain has shown immense potential, particularly in leveraging games as powerful tools to simulate individual roles within a society. This research endeavors to explore how sequence pattern mining are applied to data coming from a game, in which humans are posed with the dilemma of progressing through their jobs, while enjoying life, trying to catch an equilibrium between happiness and healthiness. Preliminary results on a game played with 13 citizens shows the feasibility of sequence learning for this purpose. Our findings shed light on the intricate interplay of individual aspirations, communal synergies, and the pursuit of equilibrium, laying the foundation for future advancements in AI-driven methodologies for sociological investigations.
Reversal Error in Mathematics: Training Against It Using ε⃔  coach
Authors: Santamarta Vicent , Garcia-Segarra Pablo , Ventura-Campos Noelia , Moreno-Rus Aida , Falomir Zoe
Published: October 2023

This paper presents ε⃔  coach, a serious game prototype which aims at training players to detect and avoid reversal error when representing word problems of the type “N times as many S as P” algebraically as an equation. The ε⃔  coach visualizes the relative size of the entities in the relation following a qualitative model based on containers. This serious game also provides a quantitative visualization of the entities related using graphics, it helps players to resolve the formulated equation using discrete values and it shows players’ progress.
Adding Edge Local Differential Privacy to the Dynamic Stochastic Block Model
Authors: Paul Sudipta , Salas Julián , Torra Vicenç
Published: October 2023

In today’s networked systems a massive amount of data is produced every day. These data can be modelled using graphs, where the nodes typically correspond to users or devices and the edges to the connections between them. Almost all networks change over time, with new nodes and edges appearing or disappearing as the system matures. Therefore dynamic graph models are more adequate to analyse such networks than static graphs, and appropriate tools need to be implemented to protect them. In this paper we obtain an edge differentially private version of dynamic stochastic block model. We show experimentally that the trends in the dynamic stochastic block model obtained from the original data are well preserved with the additional privacy guarantees.
A Digital Personal Health Library for the Management of Abortion-Related Care via Telemedicine
Authors: Kumsa Fekede Asefa , White Brianna , Singh Nupur , Ammar Nariman , Shaban-Nejad Arash
Published: October 2023

Abortion remains a highly controversial topic in many countries, particularly in the United States. As the COVID-19 pandemic introduced new challenges and restrictions, society saw a marked increase in demand for self-managed care. Likewise, the utilization of abortion care via telemedicine sparked interest, especially in communities with high infection rates. However, as unregulated online forums became an outlet for the discussion of sensitive health-related topics, the spread of false and misleading information markedly increased. As patients continue seeking reliable health-related information, personalized solutions are needed to provide accurate, evidence-based insight. As a pillar in digital precision health, precision health promotion via Personal Health Library (PHL) could aid in equipping patients with the necessary information to support informed health decision-making. In previous works, we have proposed the utilization of a PHL for the self-management of disease and health promotion/education. Herein, we introduce our work-in-progress in implementing the PHL-Enabled Abortion Care and Education (PEACE) platform for facilitating and supporting reliable access to informative reproductive care, such as abortion via telemedicine.
Recovery Gestures Classification Using KNN and LDA Models
Authors: Nicola Stelian , Chirila Oana-Sorina , Stoicu-Tivadar Lacramioara
Published: October 2023

Hand and joint mobility recovery involve performing a set of exercises. Gestures are often used in the hand mobility recovery process. This paper discusses the selection and the use of 2 models of neural networks for the classification of data that describe Leap Motion gestures. The gestures are: the hand opening and closing gesture and the palm rotation gesture. The purpose is the optimal selection of the neural network model to be used in the classification of the data describing the recovery gestures. The models chosen for the classification of the two gestures were: Linear Discriminant Analysis (LDA) and K-neighbors Classifier (KNN). The accuracies achieved in the classification of the gestures for each model are: 0.91 – LDA and 0.98 – KNN.
Comparison of Data Classification Results in Serious Gaming for Rehabilitation of Rheumatoid Arthritis
Authors: Varga Gabriela , Stoicu-Tivadar Lăcrămioara , Nicola Stelian
Published: October 2023

Rheumatoid arthritis is a common disease which affects the joints of the wrist, fingers, feet and in the end the daily activities. Nowadays, gestures and virtual reality are used in many activities supporting recovery, games, learning as technology is present more and more in different fields. This paper presents results related to the grip movement detected by a Leap Motion device using binary classification and machine learning algorithms. We used 2 models to compare the results: Naïve Bayes and Random Forest Classifier. The metrics for comparison were: accuracy, precision, recall and f1-score. Also, we create a confusion matrix for a clear visualization of the results. We used 5000 data to train the algorithm and 1500 data to test. The accuracy and the precision were bigger than 97% in all the cases.
Unified Identification Protocol for Cross-Border Healthcare
Authors: Latorre Fernando , Hawks Claudia E. , Colmenares Bruno , Verma Deepika , Gil Marisa , Sala Nuria
Published: October 2023

The Unified Identification Protocol (UIP) is an innovation which empowers patients and legal guardians to generate their unique digital identity for cross-border healthcare. This digital identity seamlessly links to local identifiers across different territories and organizations, bridging the gap between disparate systems. Combined with the International Patient Summary (IPS) – endorsed by the G7 and the EU – UIP is pioneering a new paradigm in telehealth services. Championing a user-centric approach in line with Web 3.0 principles, UIP places data control directly in the hands of patients and their legal guardians. This ensures accurate identification, streamlined access to health data, and robust privacy protection. When harmonized with tools like the SMART-On-FHIR API, FHIR Contract, DID Documents, and blockchain certification, UIP lays down transparent, user-approved guidelines for sharing healthcare data across borders. This framework guarantees that data is securely exchanged, encrypted specifically for the intended recipients upon user consent, adhering to international standards, and in full compliance with prevailing regulations. Furthermore, UIP facilitates certification of health courses and competences for patients, caregivers, and practitioners, enhancing healthcare understanding and management.
Digital Connecting for Health, an Open Platform Based on Data Integration and Standards to Adopt Digital and Telehealth Solutions in the Healthcare Ecosystem
Authors: Yasini Mobin , Bonns Erica , Rausch Dennis , Dahlweid Michael
Published: October 2023

The paper presents a collaborative approach employed to identify and examine the obstacles faced by telehealth solutions. The study involved the active participation of health start-ups, telehealth providers, and healthcare professionals delivering telehealth services. By harnessing the collective expertise and diverse perspectives of these stakeholders, the research led to develop an open platform, entitled Digital Connecting for Health, that has the potential to overcome the challenges impeding the widespread adoption and effectiveness of digital health services including telehealth in delivery of care. The developed platform shed light on various obstacles faced by telehealth solutions and provide valuable infrastructures for enhancing the implementation and efficacy of various digital health solutions, including telehealth applications, from various providers.
Automated ICD10-Coding of Teleconsultations Conclusions in Primary Care
Authors: Melot Bénédicte , Drouet Florian , Alvarez Caroline , Grimshaw Céline , Grosjean Julie , Duclos Catherine
Published: October 2023

The national teleconsultation platform Qare offers a promising tool to collect data in primary care by sharing the same EHR among teleconsultants. Conclusions for every teleconsultation are thus collected by a text using an ICD10 code or not. Nevertheless, the use of the ICD10 classification by primary care doctors is scarce. We developed an algorithm based on bags of words that associates an ICD10 code for each textual conclusion and evaluated it. It shows good performances (88 to 89% concordance) to assign ICD10 category. It could be used to enhance the volume of coded teleconsultations ready to be reused.
PainRE-Life: A FHIR Based Telemonitoring Ecosystem for the Management of Patients with Chronic Pain
Authors: Marceglia Sara , Manzelli Vania , Caruso Annamaria , Prenassi Marco , Prandin Roberto , Savino Chiara , Tacconi David , Ferrucci Roberta , Conti Costanza , Candiani Giulia , Toraldo Cristiano , Judica Elda , Corbo Massimo , Masiero Marianna , Pravettoni Gabriella
Published: October 2023

Chronic pain is a condition in which the use of digital health technologies, ecological momentary assessments, and digital communication tools may boost patient’s engagement and coping. Here we present the results of the PainRE-Life a project, financed by the Lombardy Region (Italy), aimed to develop a dynamic and integrated technology ecosystem based on big data management and analysis to allow care continuity in patients with pain, and able to act as a decision aid for patients and caregivers.
Patient Self-Monitoring Using Intelligent Phonocardiography on a Mobile Platform
Authors: Jakobsen Malin , Babic Ankica
Published: October 2023

The paper presents the design and high-fidelity prototype of the remote patient self-monitoring system using a combination of intelligent phonocardiography, mobile and web-based platforms. The advantage of self-monitoring is patient awareness about potential changes, the convenience of performing the measurement often, and the saving of the findings. A mobile platform enables a physician to see the data, get a summary of patient recordings, and as well as saving the data. We have designed two user profiles to enable such functionality and to enable consultations. During the three development iterations, two main prototypes were developed. In the patient prototype, the main functionality is measuring PCG signals, but with the possibility of reading more details about the results. In the physician’s prototype, the main functionality is the patient overview, with the possibility of querying through old patient data to consult newer patients. For physicians to monitor patients monitoring themselves, the solution needs to be properly clinically validated and regulatory demands satisfy before it could be utilized in the Norwegian health domain.
Setting up a Video Transmission Tool for Teleconsultation and Tele-Expertise in an African Context: Case of Burkina Faso
Authors: Barro Seydou Golo , Kambire Jonathan Gaius , Kantagba Yves M.K. , Karambiri Daniel , Ouattara Thomas A. , Ougda Chams Nour , Staccini Pascal
Published: October 2023

Since the COVID-19 health crisis, telemedicine has received a lot of attention around the world. Following attempts to set up a telemedicine system, in particular teleconsultation and teleexpertise, which proved inconclusive in Burkina, we have seen several technologies and tools that could enable the implementation of a telemedicine solution that meets the realities of Burkina Faso. The results of the study of the existing system and interviews with health professionals have made it possible to design a telemedicine platform combining a scalable video-transmission tool adapted to the country’s health system
Utilization and Satisfaction of an On-Demand Telemedicine Service in Urban and Rural Communities
Authors: Helvey Sara , Mann Jami , Barzin Amir , Edson Barbara , Khairat Saif
Published: October 2023

Health disparities between urban and rural America have been studied extensively, and findings consistently show many inequities. The objective of this study was to evaluate patient experiences and utilization among patients in North Carolina who use telemedicine. A retrospective cohort study was performed to examine the utilization and satisfaction of patients using a virtual care service at a Southeastern Medical Center in the U.S. Of 1974 telemedicine patients, 1366 (79.3%) were female, 1046(60.7%) were between 35–64 years, 913(53%) had insurance coverage. Statistically significant differences between rural and urban patients in how patients rated both their provider (p<0.01) and overall telemedicine experience (p<0.01). Our findings showed high satisfaction scores among both rural and urban communities in using the telemedicine platform with slightly higher scores among rural patients, which shows the need for on-demand telemedicine to increase health access and patient outcomes among rural communities.
Neurologists’ Experiences and Attitudes Towards Teleneurology – Has Covid-19 Pandemic Made a Difference?
Authors: Rautell Tapio , Niiranen Marja , Kuusisto Hanna
Published: October 2023

Teleneurology is an adaption of telemedicine used in neurological practices. Due to the widespread availability of the Internet and the development of information and communication technology (ICT), the use of teleneurology has increased in healthcare systems. This study aimed to determine how the Covid-19 pandemic has affected neurologists’ attitudes towards teleneurology as well as their experiences of remote health care before and after the pandemic. The study was conducted as a web-based questionnaire sent to all Finnish neurologists. Two identical surveys were sent via e-mail from the National Neurology Society. The first survey was conducted in spring 2021 and second in spring 2023. The results show that the pandemic moderately increased the use of teleneurology, which enhanced neurologists’ technical skills. Neurologists estimated that the use of teleneurology will continue to increase in the future.