bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using domain adaptation and inductive transfer learning to improve patient outcome prediction in the intensive care unit
Authors: Mutnuri Maruthi Kumar , Stelfox Henry T. , Forkert Nils D. , Lee Joon 
Published: July 2023

Abstract
Predicting patient outcomes in the intensive care unit (ICU) can allow for more effective and efficient patient care. Deep learning models are effective in learning from data to accurately predict patient outcomes; however, they require huge amounts of data to train and massive computational power. Transfer learning (TL) helps in scenarios when data and computational resources are scarce. TL is commonly used in medical image analysis and natural language processing but is comparatively rare in electronic health record (EHR) analysis. In medical image analysis and natural language processing, domain adaptation (DA) is the most commonly used TL method in the literature while inductive transfer learning (ITL) is quite rare. This study explores DA as well as rarely researched ITL for predicting ICU outcomes using EHR data. To investigate the effectiveness of these TL models, we compared them with baseline models of fully connected neural networks (FCNN), logistic regression, and lasso regression in the prediction of 30-day mortality, acute kidney injury (AKI), hospital length of stay (H_LOS), and ICU length of stay (ICU_LOS). TL models transfer the knowledge gained while training for the source prediction task on the source domain to improve the prediction performance of the target prediction task on the target domain. Whereas baseline models were trained directly on the target domain for the target prediction task. Two cohorts were used in this study for the development and evaluation. The first was eCritical, a multicenter ICU data linked with administrative data with 55,689 unique admission records from 48,672 unique patients admitted to 15 medical-surgical ICUs in Alberta, Canada, between March 2013 and December 2019. The second was MIMIC-III, a single-center, publicly available ICU dataset from Boston, USA, acquired between 2001 and 2012. Random subsets of training data, ranging from 1% to 75%, as well as the full dataset were used to compare the performances of DA and ITL with FCNN, logistic and lasso regression. Overall, the ITL outperformed baseline FCNN, logistic and lasso regressions in 55 out of the 56 comparisons (7 data subsets, 4 outcomes, and 2 baseline models), whereas DA models outperformed the baseline models in 45 out of 56 cases. ITL performance was comparatively better than DA, considering the number of times it outperformed baseline models and the margin with which it outperformed baseline models. In 11 out of 16 cases (8 out of 8 for ITL and 3 out of 8 for DA), TL models outperformed baseline models when trained using the 1% data subset. This is significant because TL models are useful in data-scarce scenarios. The publicly available pre-trained models from this study can be used to predict ICU patient outcomes and serve as building blocks in further research for the development and validation of models in other cohorts and outcomes.
Adherence to multidisciplinary team meeting recommendations in elderly patients with HER2-positive breast cancer
Authors: Geissler Franziska , Maggi Nadia , Brülhart Alice , Nesic Ksenija , Zwimpfer Tibor A. , Schoetzau Andreas , Vetter Marcus , Kappos Elisabeth A. , Weber Walter P. , Kurzeder Christian , Heinzelmann-Schwarz Viola , Dedes Konstantin J. , Schwab Fabienne Dominique 
Published: July 2023

Abstract
Purpose
Applying current treatment guidelines to elderly breast cancer (BC) patients is challenged by limited trial guidance, higher toxicities, and non-cancer related mortality. This study investigated adherence to multidisciplinary team meeting (MDTM) recommendations in elderly women with HER2 positive BC (HER2+BC) and its impacts on patient survival.
Methods
This retrospective multicentre cohort study collected data from 305 patients with primary diagnosis of HER2+BC. Women aged ≥65 years were classified into “concordant” or “discordant” groups according to MDTM recommendation adherence. Cox proportional hazards models and logistic regression analysis were used to assess the association between prognostic factors and patient outcomes.
Results
Of 305 HER2+BC patients, 111 (36%) were ≥65 years old. Of these, 55 (49.5%) and 53 (47.7%) were assigned to the concordant or discordant groups, respectively. The most frequent cause of MDTM discordance was treatment rejection by patients (57%). Median age (79 vs 70 years, p = <0.001) and Charlson Comorbidity Index (score 6 vs 5, p = 0.017) were significantly higher in the discordant group. After adjusting for age, UICC stage and hormone receptor status, overall survival (OS; HR 1.49, CI [0.64-3.46], p = 0.36) showed no significant difference between groups with a median follow up of 42.8 months. Patients with pre-existing cardiac diseases (29.6%) had significantly reduced OS (log-rank test p = 0.0082).
Conclusion
Adjustments to MDTMs for older BC patients may be necessary to increase patient treatment acceptance. Moreover, approaches to reduce treatment intensity in selected elderly patients with HER2+BC should be investigated further.
WITHDRAWN: Geochemical and isotopic characteristics of thermal and mineral waters in the Sakarya (Adapazarı) Pull-Apart Basin
Authors: Akıllı Hafize , Kayadibi Önder , Gürboğa Şule , Mutlu Halim , Karadağlar Macit , Arıkan Serap , Tan Sevilay 
Published: July 2023

Abstract

This study based on the project titled "Examination of the Economic Potentials of the developed Basins Along the North Anatolian Fault System (NAFS) and East Anatolian Fault System (EAFS) with Remote Sensing and Geographic Information Systems (GIS) Methods" carried out by the General Directorate of Mineral Research and Exploration (MTA). The Sakarya (Adapazarı) Basin is controlled by the right lateral strike-slip North Anatolian Fault Zone (NAFS) which is one of the most active structures in Turkey. The Basin showing a different evolution mechanism from the pull-apart basins along the NAFS is represented by the sedimentation by the Sakarya River in the northern site towards the Black Sea. Moreover, the southern margin of the basin is shaped by the active faults. On the accounts of these factors, the geological significance of the basin for both geothermal and deformation features entirely focuses around the southern margin of the basin. In the study area, the Paleocene and Eocene aged limestones have abundantly broken, cracked and karstic cavity. The clayey-silty levels of the Plio-Quaternary sediments on the limestones showing the reservoir character are also cover rock.Chemical and isotopic compositions were determined by sampling from hot fluids produced from wells drilled in the Sakarya Basin and various cold-water sources. The electrical conductivity of cold and thermal waters varies between 199 and 7250 µS/cm. Temperatures of fluids taken from wells and springs are between 24 and 80° C. According to the classification of International Association of Hydrogeologists (IAH), Sakarya thermal waters are classified as boron-containing sodium, chlorinated, bicarbonate thermal and mineral waters and are deeply circulating fluids.The maturation index of Sapanca-Uzunkum and Akyazı waters is greater than 2. The cation geothermometers have been applied to these waters as well as silica geothermometers. The reservoir temperatures of these waters calculated by silica and cation geothermometers are 44-109oC and 34-99oC, respectively. These results can be attributed to the enrichment of oxygen isotopes in Akyazı and Sapanca-Uzunkum waters, their deep circulation and longer interaction with reservoir rocks.
A computational validation for the Health Concept Maturity Levels Questionnaire
Authors: Trognon Arthur , Habibi Islem , Altakroury Hamza , Mahdar-Recorbet Loann , Poli Alizée , Servais David , Lambert Julien , Béjean Mathias , Abraham Denis 
Published: July 2023

Abstract

Background
The healthcare market is undergoing rapid transformation, requiring the integration of user needs from the earliest stages of product and service design. Living Labs are emerging as a model for the co-creation and evaluation of user-centered innovations. In this work, we developed a health CML grid and questionnaire to assess the maturity of health concepts.
Methods
The research process included multiple stages, starting with the creation of the Association Innov'Autonomie - Health Concept Maturity Levels Questionnaire − 178-items (AIA/CMLHQ178), designed to evaluate health concept maturity levels. Speech acts from Health CML expert interventions were then annotated and used as data for our machine learning and deep learning models. We used the CatBoost algorithm in the first experiment to discern individual Health CML factors from speech acts to generate factor probabilities used to feed a neural network trained to take the final decision, to evaluate whether the network could accurately identify the membership factors of Health CML criteria when presented with items from the AIA/CMLHQ178 questionnaire.
Results
The results of the study indicate that only the models trained with the true factors are able to correctly identify the corresponding factor in the sequentially encoded texts, with the exception of the need domains whose artificial performance was measured according to sensitivity. The general performance of the different CatBoost algorithms used to predict one factor versus the other two showed similar performance. For the questionnaire, the models trained with the real factors also showed better performance in identifying the matching factors compared to the random factors. A marginal difference was observed between the "Need" and "Technology" factors.
Conclusion
This study provides initial evidence of content validity for the AIA/CMLHQ178, introducing a novel approach to validate psychometric instruments using machine learning and deep learning techniques. However, overlaps between "Programmatic" and "Need" factors indicate a need for improvement in the CML Health model. Future work will focus on enhancing these models and investigating their potential application in other psychometric tools.
Opinions and perceptions of patients with cardiovascular disease on adherence: a qualitative study of focus groups
Authors: Carbonell-Soliva Álvaro , Nouni-García Rauf , López-Pineda Adriana , Cordero-Fort Alberto , Pérez-Jover Virtudes , Quesada Jose A. , Orozco-Beltrán Domingo , Nolasco Andreu , Castellano-Vázquez Jose Maria , Mira-Solves Jose Joaquin , Gil-Guillén Vicente F. , Carratalá-Munuera Concepción 
Published: July 2023

Abstract
Background: Cardiovascular diseases are becoming more frequent throughout the world. Adherence to both pharmacological and non-pharmacological treatment, as well as lifestyles, is important for good management and control of the disease. This study aims to explore the opinions and perceptions of patients with ischemic heart disease on the difficulties associated with therapeutic adherence.
Methods. An interpretive phenomenological study was carried out using focus groups and one semi-structured interview. The MAXQDA qualitative data analysis program was used for inductive interpretation of the group discourses and interview. Data were coded, and these were grouped by categories and then consolidated under the main themes identified.
Results. Two in-person focus groups and one remote semi-structured interview were performed. Twelve participants (6 men and 6 women) from the Hospital de San Juan de Alicante participated. The main themes identified were aspects related to the individual, heart disease, drug treatment, and the perception of the health care system.
Conclusions. Adhering to recommendations on healthy behaviors and taking prescribed medications for cardiovascular disease was important for most participants. However, they sometimes found polypharmacy difficult to manage, especially when they did not perceive the symptoms of their disease. Participants related the concept of fear to therapeutic adherence, believing that the latter increased with the former. The relationship with health professionals was described as optimal, but, nevertheless, the coordination of the health care system was seen as limited.
Altered Brain Functional Connectivity of resting-state networks in Neurologically Asymptomatic Patients with End-Stage Renal Disease Undergoing Maintenance Hemodialysis
Authors: Wu Yunfan , Liu Mengchen , Lin Weizhao , Zhou Zhihua , Meng Shandong , Ma Xiaofen , Fu Shishun , Su Huanhuan , Li Jincheng , Liang Quanhai , Li Meng , Xu Shoujun , Xiao Wenqing , Chen Yanying , Li Rujin , Xia Bin , Jiang Guihua 
Published: July 2023

Abstract
Purpose: To investigate changes in the functional connectivity (FC) of neurologically asymptomatic patients with end-stage renal disease (ESRD) on haemodialysis (HD) and determine the relationship between these changes and clinical variables.
Methods:Resting-state functional magnetic resonance imaging was performed in 36 neurologically asymptomatic patients with ESRD on HD (ESHD) and 31 healthy control subjects. Using independent component analysis, abnormal resting-state networks and FC regions were identified in the two groups. Partial correlation analysis was performed between abnormal FC regions and clinical variables.
Results: The ESHD group exhibited abnormal FC in multiple regions of three RSNs, including the posterior default mode network (DMN), attention network, and external visual network (VN). Significant correlations were found between the FC values of multiple brain regions (the right supramarginal gyrus, the left angular gyrus, and the left middle occipital gyrus) of the DMN or VN and the neurocognitive scale scores in the ESHD group. Additionally, the FC value of the right median cingulate gyrus was negatively correlated with the serum calcium level.
Conclusion: Our study found that the ESHD group exhibited multiple abnormal FC regions in three RSNs involved in visual–spatial processing, short-term memory, language, attention and executive function; these findings are consistent with the characteristics of cognitive impairment during the HD process. Our findings provided both empirical and complementary evidence indicating that FC changes may be associated with the underlying clinical symptoms of patients with ESHD.
Rare variants in 45 genes account for 25% of cases with NDDs in 415 pediatric patients
Authors: Aspromonte Maria Cristina , Conte Alessio Del , Polli Roberta , Baldo Demetrio , Benedicenti Francesco , Bettella Elisa , Bigoni Stefania , Boni Stefania , Ciaccio Claudia , D’Arrigo Stefano , Donati Ilaria , Granocchio Elisa , Mammi Isabella , Milani Donatella , Negrin Susanna , Nosadini Margherita , Soli Fiorenza , Stanzial Franco , Turolla Licia , Tosatto Silvio C.E. , Murgia Alessandra , Leonardi Emanuela 
Published: July 2023

Abstract

Neurodevelopmental disorders (NDDs) are common conditions including clinically diverse and genetically heterogeneous diseases, such as intellectual disability, autism spectrum disorders, and epilepsy. Understanding the genetic causes of NDDs is challenging due to their complex and heterogeneous nature. In this study, a targeted gene panel sequencing has been used to investigate the genetic causes of NDDs in a cohort of 415 pediatric patients. We identified 60 pathogenic and 49 potentially pathogenic variants in 102 individuals that accounted for 25% of NDD cases in the cohort. The majority of causative variants were de novo, with some inherited from mildly affected parents. Loss-of-function variants were the most common type of causative mutation. In silico analysis tools were used to assess the potential impact of variants on splicing and structural/functional effects of missense variants. The study highlights the challenges in variant interpretation and predicting associated phenotypes. The clinical features observed in some patients were atypical for the mutations found in their genes. Overall, this study provides valuable insights into the genetic causes of NDDs and emphasizes the importance of understanding the underlying genetic factors for accurate diagnosis, prognosis, and intervention development in neurodevelopmental conditions.
The Impact of Operating Efficiency, Non-Interest Income, and Bank Size on Profitability: Evidence from the Ethiopian Banking Sector
Authors: Hordofa Dereje Fedasa 
Published: July 2023

Abstract
This manuscript examines the effect of operating efficiency and non-interest income on bank profitability in the Ethiopian banking sector. Drawing on a comprehensive literature review and utilizing panel data from 15 banks over a specified period, we investigate the determinants of profitability and their implications for the Ethiopian banking landscape. The findings reveal that operating efficiency, measured through various indicators, positively influences bank profitability, aligning with prior research. Furthermore, the analysis highlights the complex relationship between non-interest income and profitability, indicating a significant but non-linear association. The study also underscores the role of bank size and leverage as important factors shaping profitability in the Ethiopian context. The results contribute to the existing literature by providing empirical evidence on the specific dynamics of the Ethiopian banking sector and offer valuable insights for policymakers and industry practitioners. By understanding the factors that drive bank profitability, stakeholders can formulate effective strategies to promote a sustainable and robust banking sector in Ethiopia. This manuscript provides a valuable contribution to the literature and offers actionable recommendations for enhancing the performance and profitability of banks in Ethiopia.
NER Sequence Embedding of Unified Medical Corpora to incorporate Semantic Intelligence in Big Data Healthcare Diagnostics
Authors: Shafqat Sarah , Anwar Zahid , Javaid Qaisar , Ahmad Hafiz Farooq 
Published: July 2023

Abstract
Clinical diagnoses is a challenging task for which high expertise is required at doctors’ end. It is recognized that technology integration with clinical domain would facilitate the diagnostic process. Semantic understanding of medical domain and clinical context is needed to make intelligent analytics. These analytics need to learn the medical context for different purposes of diagnosing and treating patients. Traditional diagnoses are made through phenotype features from patients’ profile. It is also a known fact that diabetes mellitus (DM) is widely affecting the population and is a chronic disease that requires timely diagnosis. Motivation for this research comes from the gap found in discovering the common ground for medical context learning in analytics to diagnose DM and its comorbidity diseases. Therefore, a unified medical knowledge base is found significantly important to learn contextual Named Entity Recognition (NER) embedding for semantic intelligence. Our search for possible solutions for medical context learning told us that unified corpora tagged with medical terms was missing to train the analytics for diagnoses of DM and its comorbidities. Hence, we put effort in collecting endocrine diagnostic electronic health records (EHR) corpora for clinical purposes that is labeled with ICD-10-CM international coding scheme. International Codes for Diseases (ICD) by World Health Organization (WHO) is a known schema to represent medical codes for diagnoses. The complete endocrine EHR corpuses make DM-Comorbid-EHR-ICD-10 Corpora. DM-Comorbid-EHR-ICD-10 Corpora is tagged for understanding the medical context with uniformity. We experimented with different NER sequence embedding approaches using advanced ML integrated with NLP techniques. Different experiments used common frameworks like; Spacy, Flair, and TensorFlow, Keras. In our experiments albeit label sets in form of (instance, label) pair for diagnoses were tagged with Sequential() model found in TensorFlow.Keras using Bi-LSTM and dense layers. The maximum accuracy achieved was 0.9 for Corpus14407\_DM\_pts\_33185 with maximum number of diagnostic features taken as input. The sequential DNN NER model diagnostic accuracy increased as the size of corpus grew from 100 to 14407 DM patients suffering from comorbidity diseases. The significance of clinical notes and practitioner comments available as free text is clearly seen in the diagnostic accuracy.
A Comprehensive Review on the Neuropharmacological Effects of
Antidepressants in Various Prospects
Authors: Varshney Sandesh , Alam Md. Aftab , Kaur Awaneet , Sharma Shaweta 
Published: July 2023

Abstract:
Depression is one of the most challenging diseases for society to treat. It is a highly prevalent
and disabling illness in the general population. Affective disorders are characterised by depressed
mood, diminished interest and pleasure, feelings of guilt or poor self-worth, sleep or food difficulties,
decreased energy, and impaired attention. This manuscript will look at depression from a behavioural
analytic perspective. The pathogenesis of major depressive disorder is poorly understood. Several
lines of experimental and clinical evidence, however, show that the therapeutic effect of most antidepressant
drugs is related to an increase in 5-HT-mediated neurotransmission. Alternative techniques,
however, are employed to obtain this net effect. A better understanding of the neurological mechanism
underpinning antidepressant drugs' delayed onset of action has resulted in the development of ways to
accelerate antidepressant responses, which are discussed further below. Many antidepressant medications
on the market today are beneficial, but they come with many downsides, including unpleasant
side effects, potential interactions, and a low response rate. Natural drugs, on the other hand, are extremely
effective, have a low risk, and a limited amount of side effects, which are covered briefly in
this paper. Alternative modalities of administration have received a lot of attention in recent decades
as a complement to approved prescription pharmaceuticals, especially for people who cannot tolerate
oral or parenteral methods. The most promising non-invasive systemic delivery techniques are transdermal
and transbronchial administration, and these are the focus of this research.
Explaining Why Africa had Much Lower COVID-19 Fatalities Per Million than Europe and North America
Authors: Boretti Alberto 
Published: July 2023

Abstract:This letter aims to explain the observed difference between COVID-19 fatalities in Europe and America compared to Africa. At present (May 3, 2023), the cumulative number of COVID-19 fatalities per million people has been 3,093 in South America, 2,761 in Europe, and 2,662 in North America. In particular, the United States and the United Kingdom had 3,323 and 3,320, respectively. With fewer opportunities to practice social distancing or hygiene and much more limited access to health care, Africa only had 181 fatalities per million. The better performances of Africa have been ascribed to the positive effects of reduced hygiene, increased biome diversity, lack of masking mandates and restrictions, better access to antivirals, a younger population, and lower vaccination rates, which all may have contributed to the better performances of Africa compared to Europe and North America against COVID-19.