Heart failure is a clinical syndrome that occurs when the heart is too weak or stiff and cannot pump enough blood that our body needs. It is one of the most expensive diseases due to frequent hospitalizations and emergency room visits. Reducing unnecessary rehospitalizations is also an important and challenging task that has the potential of saving healthcare costs, enabling discharge planning, and identifying patients at high risk. Therefore, this paper proposes a deep learning-based prediction model of heart failure rehospitalization during 6, 12, 24-month follow-ups after hospital discharge in patients with acute myocardial infarction (AMI). We used the Korea Acute Myocardial Infarction-National Institutes of Health (KAMIR-NIH) registry which included 13,104 patient records and 551 features. The proposed deep learning-based rehospitalization prediction model outperformed traditional machine learning algorithms such as logistic regression, support vector machine, AdaBoost, gradient boosting machine, and random forest. The performance of the proposed model was accuracy, the area under the curve, precision, recall, specificity, and F1 score of 99.37%, 99.90%, 96.86%, 98.61%, 99.49%, and 97.73%, respectively. This study showed the potential of a deep learning-based model for cardiology, which can be used for decision-making and medical diagnosis tool of heart failure rehospitalization in patients with AMI.

South Korea has the mandatory National Health Insurance (NHI) and supplemental Private Indemnity Health Insurance (PIHI). According to the Financial Supervisory Service, the share of the population with PIHI increased to 66% in 2018 due to the financial burden. However, since the traditional PIHI claim workflow is based on the paper attachment method, it is a big burden to every stakeholder and limits the usability and accessibility of the claims data. To improve the traditional PIHI claim workflow, we developed the electronic claim (eClaim) service for the PIHI in Korea. We also applied the HL7® (Health Level Seven) FHIR® (Fast Healthcare Interoperability Resources) standard to ensure interoperability of the claims data. The proposed eClaim Service has been launched in 2017. It has been increased from 8155 in the first half of 2018 to 114,087 in the second half of 2020. Currently, 60 healthcare providers and 22 payers participated in this service. In this study, we proposed an eClaim workflow and service to improve the legacy system. The proposed method can be helpful to other entities planning for their own health insurance system and also applied to various practical purposes including value-based care, automated claim review, and clinical research.

Nurses need professional competencies for safe and effective care. In this regard, nursing informatics competence is a significant need for intensive care unit nurses. In this study, the Delphi method was used during four rounds. The participants included 30 panelists selected by purposive sampling method. The data were analyzed using qualitative content analysis and descriptive statistics. During the rounds, the items were evaluated for importance, necessity, relevancy, and percentage of agreement. The findings of nursing informatics competencies in the intensive care units (ICU) were classified in three categories including basic computer skills (7 subcategories and 20 items), skills to use information management software (2 subcategories and 9 items), and specific nursing informatics skills (2 subcategories and 22 items). In order for nurses to perform successfully in ICU, they must meet some abilities such as computer skills, available software management, nursing information systems familiarity, nursing databases, web search methods, and English proficiency.

Discretization is a preprocessing technique used for converting continuous features into categorical. This step is essential for processing algorithms that cannot handle continuous data as input. In addition, in the big data era, it is important for a discretizer to be able to efficiently discretize data. In this paper, a new supervised density-based discretization (DBAD) algorithm is proposed, which satisfies these requirements. For the evaluation of the algorithm, 11 datasets that cover a wide range of datasets in the medical domain were used. The proposed algorithm was tested against three state-of-the art discretizers using three classifiers with different characteristics. A parallel version of the algorithm was evaluated using two synthetic big datasets. In the majority of the performed tests, the algorithm was found performing statistically similar or better than the other three discretization algorithms it was compared to. Additionally, the algorithm was faster than the other discretizers in all of the performed tests. Finally, the parallel version of DBAD shows almost linear speedup for a Message Passing Interface (MPI) implementation (9.64× for 10 nodes), while a hybrid MPI/OpenMP implementation improves execution time by 35.3× for 10 nodes and 6 threads per node.

The fat acceptance (FA) movement aims to counteract weight stigma and discrimination against individuals who are overweight/obese. We developed a supervised neural network model to classify sentiment toward the FA movement in tweets and identify links between FA sentiment and various Twitter user characteristics. We collected any tweet containing either "fat acceptance" or "#fatacceptance" from 2010-2019 and obtained 48,974 unique tweets. We independently labeled 2000 of them and implemented/trained an Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) neural network that incorporates transfer learning from language modeling to automatically identify each tweet's stance toward the FA movement. Our model achieved nearly 80% average precision and recall in classifying "supporting" and "opposing" tweets. Applying this model to the complete dataset, we observed that the majority of tweets at the beginning of the last decade supported FA, but sentiment trended downward until 2016, when support was at its lowest. Overall, public sentiment is negative across Twitter. Users who tweet more about FA or use FA-related hashtags are more supportive than general users. Our findings reveal both challenges to and strengths of the modern FA movement, with implications for those who wish to reduce societal weight stigma.

Studies on vitamin D receptor (VDR) and its association with multiple disorders are expanding. This bibliometric study aims to find and summarize VDR-related publications, and compare them across various countries, organizations, and journals to demonstrate trends in VDR research. VOSviewer and Excel 2019 were used to classify and summarize Web of Science articles from 1900 to mid-2021. Total records of 8762 articles were analyzed, and maps of co-citations bibliometric keywords co-occurrence were designed. In conclusion, relative research interest and published papers related to VDR were growing in the past 30 years. The United States of America dominates the research regarding VDR. The highest quality of VDR research was achieved by the University of California System, University of Wisconsin System, and Harvard University. J Steroid Biochem Mol Biol, PLoS One, and J Biol Chem are the leading three productive journals on VDR. Various aspects of vitamin D deficiency associated disorders and genetic studies regarding VDR, including single nucleotide polymorphism, gene variants, epigenome, long non-coding ribonucleic acid (lncRNA), and small nucleolar RNA host gene 6 are potentially the recent research hotspot in this field. Moreover, coronavirus disease, polycystic ovary syndrome, non-alcoholic fatty liver disease, gut microbiota, gestational diabetes, systemic sclerosis, and chemoresistance are the trending medical conditions associated with VDR.

This study aims to examine the characteristics of pregnant women who used and intended to use mobile applications (apps) for health and parenting information. We used data from a randomised controlled trial, Communicating Healthy Beginnings Advice by Telephone (CHAT), conducted in Australia. Telephone surveys were conducted in 2017 to collect information on women's demographic characteristics, sources of health and parenting information and willingness to use mobile apps. Multiple logistic regression was used to examine the associations of women's socio-demographic backgrounds, their mobile apps usage and their willingness to use such apps. Data included 1155 pregnant women in their third trimester. Women from culturally and linguistically diverse backgrounds and with lower income appeared to have lower uptake of mobile apps despite high ownership of smart devices. Development of evidence-based and culturally-adapted mobile apps represent an important opportunity for healthcare providers to optimise maternal and birth outcomes.

The main aim of this study is to assess the effectiveness of using a developed asthma mobile application to enhance medication adherence in Jordan. Asthma patients visiting outpatient respiratory clinics and using inhalers were recruited. Patients were assigned into two groups: intervention and control. The intervention group was instructed to download and use the application. Asthma control was assessed using Asthma Control Test (ACT) at baseline and at follow-up of 3 months for both groups. A total of 171 patients (control, n = 83, and intervention, n = 88) participated in the study. After 3 months of usage, patients in the intervention group achieved a significant improvement in ACT score compared to control (p-value <0.05), and reported a significant satisfaction of the application use. Therefore, the asthma mobile application is found as an effective tool to enhance medication adherence in asthma patients.

The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of COVID-19 patients, However, applications in real-world scenarios are still needed. We describe the development and deployment of COVID-19 decision support and segmentation system. A partnership with a Brazilian radiologist consortium, gave us access to 1000s of labeled computed tomography (CT) and X-ray images from São Paulo Hospitals. The system used EfficientNet and EfficientDet networks, state-of-the-art convolutional neural networks for natural images classification and segmentation, in a real-time scalable scenario in communication with a Picture Archiving and Communication System (PACS). Additionally, the system could reject non-related images, using header analysis and classifiers. We achieved CT and X-ray classification accuracies of 0.94 and 0.98, respectively, and Dice coefficient for lung and covid findings segmentations of 0.98 and 0.73, respectively. The median response time was 7 s for X-ray and 4 min for CT.

A record number of immigrants, many Arabic speaking, arrived in Sweden during the years 2015/2016. Immigrant women have a higher risk of pregnancy complications than native European women and cultural and communicative problems have been identified as a cause of such disparities. Maternity services are under pressure because of language and cultural barriers. Language translation programmes are sometimes used but these are not evidence-based so are not considered safe for use in maternity care. The aim of this research was to create an evidence-based app for communication with Arabic-speaking women in maternity care. User-centred iterative design was used to develop an evidence-based, fit-for-purpose app. Data were collected from midwives in a focus group interview, field observations and workshops. The iterative approach resulted in an evidence-based prototype that is currently being tested in the field.