bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fueling Clinical and Translational Research in Appalachia: Informatics Platform Approach
Authors: Cecchetti Alfred A , Bhardwaj Niharika , Murughiyan Usha , Kothakapu Gouthami , Sundaram Uma 
Published: July 2020

Background
The Appalachian population is distinct, not just culturally and geographically but also in its health care needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized data warehouse to transform health care data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data.

Objective
This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center along with its initial uses.

Methods
The Appalachian Informatics Platform was developed by the Appalachian Clinical and Translational Science Institute’s Division of Clinical Informatics and consists of 4 major components: a centralized clinical data warehouse, modeling (statistical and machine learning), visualization, and model evaluation. Data from different clinical systems, billing systems, and state- or national-level data sets were integrated into a centralized data warehouse. The platform supports research efforts by enabling curation and analysis of data using the different components, as appropriate.

Results
The Appalachian Informatics Platform is functional and has supported several research efforts since its implementation for a variety of purposes, such as increasing knowledge of the pathophysiology of diseases, risk identification, risk prediction, and health care resource utilization research and estimation of the economic impact of diseases.

Conclusions
The platform provides an inexpensive yet seamless way to translate clinical and translational research ideas into clinical applications for regions similar to Appalachia that have limited resources and a largely rural population.
Results and Guidelines From a Repeated-Measures Design Experiment Comparing Standing and Seated Full-Body Gesture-Based Immersive Virtual Reality Exergames: Within-Subjects Evaluation
Authors: Xu Wenge , Liang Hai-Ning , He Qiuyu , Li Xiang , Yu Kangyou , Chen Yuzheng 
Published: July 2020

BackgroundAlthough full-body seated exercises have been studied in a wide range of settings (ie, homes, hospitals, and daycare centers), they have rarely been converted to seated exergames. In addition, there is an increasing number of studies on immersive virtual reality (iVR) full-body gesture-based standing exergames, but the suitability and usefulness of seated exergames remain largely unexplored.ObjectiveThis study aimed to evaluate the difference between playing a full-body gesture-based iVR standing exergame and seated exergame in terms of gameplay performance, intrinsic motivation, and motion sickness.MethodsA total of 52 participants completed the experiment. The order of the game mode (standing and sitting) was counterbalanced. Gameplay performance was evaluated by action or gesture completion time and the number of missed gestures. Exertion was measured by the average heart rate (HR) percentage (AvgHR%), increased HR%, calories burned, and the Borg 6-20 questionnaire. Intrinsic motivation was assessed with the Intrinsic Motivation Inventory (IMI), whereas motion sickness was assessed via the Motion Sickness Assessment Questionnaire (MSAQ). In addition, we measured the fear of falling using a 10-point Likert scale questionnaire.ResultsPlayers missed more gestures in the seated exergame than in the standing exergame, but the overall miss rate was low (2.3/120, 1.9%). The analysis yielded significantly higher AvgHR%, increased HR%, calories burned, and Borg 6-20 rating of perceived exertion values for the seated exergame (all P<.001). The seated exergame was rated significantly higher on peripheral sickness (P=.02) and sopite-related sickness (MSAQ) (P=.004) than the standing exergame. The score of the subscale “value/usefulness” from IMI was reported to be higher for the seated exergame than the standing exergame. There was no significant difference between the seated exergame and standing exergame in terms of intrinsic motivation (interest/enjoyment, P=.96; perceived competence, P=.26; pressure/tension, P=.42) and the fear of falling (P=.25).ConclusionsSeated iVR full-body gesture-based exergames can be valuable complements to standing exergames. Seated exergames have the potential to lead to higher exertion, provide higher value to players, and be more applicable in small spaces compared with standing exergames. However, gestures for seated exergames need to be designed carefully to minimize motion sickness, and more time should be given to users to perform gestures in seated exergames compared with standing exergames.
Effectiveness of Virtual Reality in Nursing Education: Meta-Analysis
Authors: Chen Feng-Qin , Leng Yu-Fei , Ge Jian-Feng , Wang Dan-Wen , Li Cheng , Chen Bin , Sun Zhi-Ling 
Published: July 2020

Background
Virtual reality (VR) is the use of computer technology to create an interactive three-dimensional (3D) world, which gives users a sense of spatial presence. In nursing education, VR has been used to help optimize teaching and learning processes.

Objective
The purpose of this study was to evaluate the effectiveness of VR in nursing education in the areas of knowledge, skills, satisfaction, confidence, and performance time.

Methods
We conducted a meta-analysis of the effectiveness of VR in nursing education based on the Cochrane methodology. An electronic literature search using the Cochrane Library, Web of Science, PubMed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature), up to December 2019 was conducted to identify studies that reported the effectiveness of VR on knowledge, skills, satisfaction, confidence, and performance time. The study selection and data extraction were carried out by two independent reviewers. The methodological quality of the selected studies was determined using the Cochrane criteria for risk-of-bias assessment.

Results
A total of 12 studies, including 821 participants, were selected for the final analysis. We found that VR was more effective than the control conditions in improving knowledge (standard mean difference [SMD]=0.58, 95% CI 0.41-0.75, P<.001, I2=47%). However, there was no difference between VR and the control conditions in skills (SMD=0.01, 95% CI –0.24 to 0.26, P=.93, I2=37%), satisfaction (SMD=0.01, 95% CI –0.79 to 0.80, P=.99, I2=86%), confidence (SMD=0.00, 95% CI –0.28 to 0.27, P=.99, I2=0%), and performance time (SMD=–0.55, 95% CI –2.04 to 0.94, P=.47, I2=97%).

Conclusions
The results of this study suggest that VR can effectively improve knowledge in nursing education, but it was not more effective than other education methods in areas of skills, satisfaction, confidence, and performance time. Further rigorous studies with a larger sample size are warranted to confirm these results.
Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study
Authors: Masina Fabio , Orso Valeria , Pluchino Patrik , Dainese Giulia , Volpato Stefania , Nelini Cristian , Mapelli Daniela , Spagnolli Anna , Gamberini Luciano 
Published: July 2020

Background
Voice assistants allow users to control appliances and functions of a smart home by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants’ actual motor, linguistic, and cognitive abilities.

Objective
The aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home).

Methods
A group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable of accounting for an efficient interaction with the voice assistant was investigated by performing multiple linear regression models. The best model was identified by adopting a selection strategy based on the Akaike information criterion (AIC).

Results
For users with disabilities, the effectiveness of interacting with a voice assistant is predicted by the Mini-Mental State Examination (MMSE) and the Robertson Dysarthria Profile (specifically, the ability to repeat sentences), as the best model shows (AIC=130.11).

Conclusions
Users with motor, linguistic, and cognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities.
Respiratory Health of Pacific Youth: An Observational Study of Associated Risk and Protective Factors Throughout Childhood
Authors: Tautolo El-Shadan , Wong Conroy , Vandal Alain , Jalili-Moghaddam Shabnam , Griffiths Emily , Iusitini Leon , Trenholme Adrian , Byrnes Catherine 
Published: July 2020

Background
Respiratory disease is the third most common cause of death in New Zealand, with Pacific people living in New Zealand bearing the greatest burden of this type of disease. Although some epidemiological outcomes are known, we lack the specifics required to formulate targeted and effective public health interventions. The Pacific Islands Families (PIF) birth cohort study is a study that provides a unique source of data to assess lung function and current respiratory health among participants entering early adulthood and to examine associations with early life events during critical periods of growth.

Objective
This paper aims to provide an overview of the design, methods, and scope of the Respiratory Health of Pacific Youth Study, which uses the overall PIF study cohort aged 18-19 years.

Methods
From 2000-2019, the PIF study has followed, from birth, the growth, and the development of 1398 Pacific children born in Auckland, New Zealand. Participants were nested within the overall PIF study (at ages 18-19 years) from June 2018, and assessments were undertaken until mid-November 2019. The assessments included respiratory and general medical histories, a general physical examination, assessment of lung function (forced expiratory volume and forced vital capacity), self-completed questionnaires (St George’s Respiratory Questionnaire, European Quality of Life 5 Dimensions-3 Level, Epworth Sleepiness Scale for Children and Adolescents, and Leicester Cough Questionnaire), blood tests (eosinophils, Immunoglobulin E, Immunoglobulin G, Immunoglobulin A, Immunoglobulin M, and C-reactive protein), and chest x-rays. Noninferential analyses will be carried out on dimensionally reduced risk and protective factors and confounders.

Results
Data collection began in June 2018 and ended in November 2019, with a total of 466 participants recruited for submission of the paper. Collection and collation of chest x-ray data is still underway, and data analysis and expected results will be published by November 2020.

Conclusions
This is the first longitudinal observational study to address the burden of respiratory disease among Pacific youth by determining factors in early life that impose long-term detriments in lung function and are associated with the presence of respiratory illness. Identifying risk factors and the magnitude of their effects will help in adopting preventative measures, establishing whether any avoidable risks can be modified by later resilient behaviors, and provide baseline measurements for the development of respiratory disease in later adult life. The study results can be translated into practice guidelines and inform health strategies with immediate national and international impact.

International Registered Report Identifier (IRRID)
DERR1-10.2196/18916
Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach
Authors: Li Rui , Yin Changchang , Yang Samuel , Qian Buyue , Zhang Ping 
Published: July 2020

Background
Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly.

Objective
The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation.

Methods
A domain-knowledge–guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation.

Results
We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz.

Conclusions
In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN–based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.
SISTEM INFORMASI AKADEMIK BERBASIS WEB DENGAN IMLEMENTASI HMVC (HIERARCHICAL, MODEL, VIEW, CONTROLLER) (STUDI KASUS: SMK MUHAMMADIYAH 8 SILIRAGUNG BANYUWANGI)
Authors: Yuana Ayu Siska , Aminuddin Aminuddin , Nuryasin Ilyas - 
Published: July 2020

Sistem informasi akademik ialah sebuah sistem yang mampu memberikan sebuah informasi mengenai segala hal mengenai pelayanan akademik suatu instansi pendidikan. Sistem informasi akademik dapat menjadi solusi dalam upaya meningkatkan sebuah kualitas pendidikan dengan cara memperlancar informasi akademik siswa dengan pihak sekolah sehingga lebih efesien. SMK Muhammadiyah 8 siliragung adalah suatu instansi pendidikan kejuruan dengan matapelajaran adaptif,normative serta produktif sehingga sistem informasi akademik akan sangat menunjang proses pembelajaran di sekolah. Penelitian ini akan membahas sebuah pembangunan sistem informasi akademik SMK Muhammadiyah 8 Siliragung berbasis web dengan mengimpelemntasikan metodel arsitektur HMVC (Hierarchical, Model, View, Controller) yang mampu membuat aplikasi dalam skala besar yang akan dibangun dalam modul-modul yang nantinya setiap modul dapat diimplementasikan kembali ke dalam modul lain ataupun pengembangan . Sistem informasi akademik SMK Muhammaiyah 8 Siliragung dikembangkan dengan pengujian black-box, pengujian UAT (User Acceptance Testing) dan integrasi testing dengan (PHPUnit 8) sebagai sarana untuk menguji model HMVC. Dalam rangka memudahkan dalam pembangunan sistem maka akan digunakannya sebuah metode Waterfall sehingga sistem informasi akan tersusun secara sistematik dan berurutan yang akan meminimalisir sebuah kesalahan.
PENERAPAN METODE K-MEANS DENGAN METODE ELBOW UNTUK SEGMENTASI PELANGGAN MENGGUNAKAN MODEL RFM(Recency, Frequency, & Monetary)
Authors: Kiat Adnan Burhan Hidayat , Azhar Yufiz , Rahmayanti Vinna 
Published: July 2020

Segmentasi pelanggan pada perusahaan merupakan tindakan yang dapat mempermudah perusahaan dalam mengambil keputusan ke depan. Pada penelitian ini data yang digunakan berasal dari perusahaan otomotif, PT Hasjrat Abadi Ambon. Data yang dipakai terdiri dari data transaksi dan pelanggan kendaraan bermotor. Penerapan model RFM dapat mengelompokkan pelanggan-pelanggan berdasarkan nilai variabel Recency, Frequency dan Monetary. Hasil dari model RFM akan memperoleh status baru pada tiap pelanggan dari skala terbaik sampai terburuk. Pelanggan yang telah memiliki status akan dikelompokkan menggunakan metode K-Means menjadi beberapa Cluster(kelompok). Dalam menentukan jumlah Cluster yang optimal maka diterapkan metode Elbow. Algoritma yang digunakan dalam pembentukan Cluster terdiri dari Euclidean Distance dan Manhattan Distance. Kedua algoritma akan dibandingkan kualitas pembentukan Clusternya menggunakan metode Silhoutte Coefficient. Hasil yang diberikan pada penelitian ini berupa data yang terbagi atas 5 kelompok dengan dilakukannya lima kali pengujian untuk menentukan centroid yang unggul. Cluster yang unggul akan dibuatkan visualisasi datanya untuk memudahkan perusahaan dalam mengambil keputusan. Berdasarkan penerapan Silhoutte Coefficient, algoritma yang lebih unggul yaitu Manhattan Distance dengan nilai s(i) sebesar 0.152695. Customer segmentation at the company is an action that can facilitate the company in making decisions going forward. In this study the data used came from an automotive company, PT Hasjrat Abadi Ambon. The data used consists of transaction data and motor vehicle customers. The application of the RFM model can classify customers based on the value of the Recency, Frequency and Monetary variables. The results of the RFM model will obtain a new status on each customer from the best to the worst scale. Customers who already have status will be grouped using the K-Means method into several Clusters (groups). In determining the optimal number of Clusters, the Elbow method is applied. The algorithm used in Cluster formation consists of Euclidean Distance and Manhattan Distance. The two algorithms will be compared the quality of the Cluster formation using the Silhoutte Coefficient method. The results given in this study are in the form of data divided into 5 groups by conducting five tests to determine superior centroids. Excellent clusters will be made of data visualization to facilitate the company in making decisions. Based on the application of Silhoutte Coefficient, a superior algorithm is Manhattan Distance with value s(i) : 0.152695.
Diabetes, Glycated Hemoglobin, and the Risk of Myocardial Infarction in Women and Men: A Prospective Cohort Study of the UK Biobank
Authors: de Jong Marit , Woodward Mark , Peters Sanne A.E. 
Published: July 2020

OBJECTIVE
Diabetes has shown to be a stronger risk factor for myocardial infarction (MI) in women than men. Whether sex differences exist across the glycemic spectrum is unknown. We investigated sex differences in the associations of diabetes status and glycated hemoglobin (HbA1c) with the risk of MI.

RESEARCH DESIGN AND METHODS
Data were used from 471,399 (56% women) individuals without cardiovascular disease (CVD) included in the UK Biobank. Sex-specific incidence rates were calculated by diabetes status and across levels of HbA1c using Poisson regression. Cox proportional hazards analyses estimated sex-specific hazard ratios (HRs) and women-to-men ratios by diabetes status and HbA1c for MI during a mean follow-up of 9 years.

RESULTS
Women had lower incidence rates of MI than men, regardless of diabetes status or HbA1c level. Compared with individuals without diabetes, prediabetes, undiagnosed diabetes, and previously diagnosed diabetes were associated with an increased risk of MI in both sexes. Previously diagnosed diabetes was more strongly associated with MI in women (HR 2.33 [95% CI 1.96; 2.78]) than men (1.81 [1.63; 2.02]), with a women-to-men ratio of HRs of 1.29 (1.05; 1.58). Each 1% higher HbA1c, independent of diabetes status, was associated with an 18% greater risk of MI in both women and men.

CONCLUSIONS
Although the incidence of MI was higher in men than women, the presence of diabetes is associated with a greater excess relative risk of MI in women. However, each 1% higher HbA1c was associated with an 18% greater risk of MI in both women and men.
Pengaruh Work Family Conflict Terhadap Kinerja Karyawan Wanita Di The Sanchaya Bintan
Authors: Sari Aprilita Yulan , Waryono Waryono 
Published: July 2020

Abstract
Dalam penelitian ini untuk mengetahui pengaruh work family conflict terhadap kinerja karyawan wanita di the sanchaya bintan. Jenis penelitian ini digolongkan kepada penelitian kuantitatif dengan metode asosiatif kausal. Populasi dalam penelitian ini adalah seluruh karyawan wanita yang berkerja di the sanchaya bintan sebanyak 54 orang dengan teknik pengambilan sampel jenuh. Pengumpulan data dilakukan menggunakan kuesioner (angket) dengan skala likert yang telah teruji validitas dan reliabilitasnya. Selanjutnya data dianalisis melalui analisis deskripsi dan uji regresi linier sederhana dengan bantuan Program Komputer SPSS versi 20.00. Hasil penelitian menunjukan bahwa: 1) Work Family Conlict di The Sanchaya berada pada kategori Cukup dengan persentase 46,30%, 2) Kinerja karyawan wanita di The Sanchaya Bintan berada pada kategori cukup dengan persentase 66.67% . Selanjutnya diperoleh nilai R Square sebesar 0,206 dengan taraf sig. 0,001 < 0,05, maka variabel Work Family Conflict dapat menjelaskan variabel Kinerja Karyawan Wanita sebesar 20.6% dan 79.4% dipengaruhi oleh faktor lain.
Kata Kunci: Work Family Conflict, Kinerja Karyawan.