Journal of Medical Systems最新文献

{"title":"Text-Mining and Video Analytics of COVID-19 Narratives Shared by Patients on YouTube.","authors":"Ranganathan Chandrasekaran, Karthik Konaraddi, Sakshi S Sharma, Evangelos Moustakas","doi":"10.1007/s10916-024-02047-1","DOIUrl":"10.1007/s10916-024-02047-1","url":null,"abstract":"<p><p>This study explores how individuals who have experienced COVID-19 share their stories on YouTube, focusing on the nature of information disclosure, public engagement, and emotional impact pertaining to consumer health. Using a dataset of 186 YouTube videos, we used text mining and video analytics techniques to analyze textual transcripts and visual frames to identify themes, emotions, and their relationship with viewer engagement metrics. Findings reveal eight key themes: infection origins, symptoms, treatment, mental well-being, isolation, prevention, government directives, and vaccination. While viewers engaged most with videos about infection origins, treatment, and vaccination, fear and sadness in the text consistently drove views, likes, and comments. Visuals primarily conveyed happiness and sadness, but their influence on engagement varied. This research highlights the crucial role YouTube plays in disseminating COVID-19 patient narratives and suggests its potential for improving health communication strategies. By understanding how emotions and content influence viewer engagement, healthcare professionals and public health officials can tailor their messaging to better connect with the public and address pandemic-related anxieties.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"21"},"PeriodicalIF":5.3,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139735385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Operating Room Management.","authors":"Valentina Bellini, Michele Russo, Tania Domenichetti, Matteo Panizzi, Simone Allai, Elena Giovanna Bignami","doi":"10.1007/s10916-024-02038-2","DOIUrl":"10.1007/s10916-024-02038-2","url":null,"abstract":"<p><p>This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"19"},"PeriodicalIF":3.5,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10867065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139729834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An mHealth Application in German Health Care System: Importance of User Participation in the Development Process.","authors":"Peter Bickmann, Ingo Froböse, Christopher Grieben","doi":"10.1007/s10916-024-02042-6","DOIUrl":"10.1007/s10916-024-02042-6","url":null,"abstract":"<p><p>This paper addresses the challenges and solutions in developing a holistic prevention mobile health application (mHealth app) for Germany's healthcare sector. Despite Germany's lag in healthcare digitalization, the app aims to enhance primary prevention in physical activity, nutrition, and stress management. A significant focus is on user participation and usability to counter the prevalent issue of user attrition in mHealth applications, as described by Eysenbach's 'law of attrition'. The development process, conducted in a scientific and university context, faces constraints like limited budgets and external service providers. The study firstly presents the structure and functionality of the app for people with statutory health insurance in Germany and secondly the implementation of user participation through a usability study. User participation is executed via usability tests, particularly the think-aloud method, where users verbalize their thoughts while using the app. This approach has proven effective in identifying and resolving usability issues, although some user feedback could not be implemented due to cost-benefit considerations. The implementation of this study into the development process was able to show that user participation, facilitated by methods like think-aloud, is vital for developing mHealth apps. Especially in health prevention, where long-term engagement is a challenge. The findings highlight the importance of allocating time and resources for user participation in the development of mHealth applications.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"20"},"PeriodicalIF":5.3,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10866790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139729833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring the Coverage of the HL7® FHIR® Standard in Supporting Data Acquisition for 3 Public Health Registries.","authors":"Manju Bikkanuri, Taiquitha T Robins, Lori Wong, Emel Seker, Melody L Greer, Tremaine B Williams, Maryam Y Garza","doi":"10.1007/s10916-023-02033-z","DOIUrl":"10.1007/s10916-023-02033-z","url":null,"abstract":"<p><p>With the increasing need for timely submission of data to state and national public health registries, current manual approaches to data acquisition and submission are insufficient. In clinical practice, federal regulations are now mandating the use of data messaging standards, i.e., the Health Level Seven (HL7^®) Fast Healthcare Interoperability Resources (FHIR^®) standard, to facilitate the electronic exchange of clinical (patient) data. In both research and public health practice, we can also leverage FHIR^® ‒ and the infrastructure already in place for supporting exchange of clinical practice data ‒ to enable seamless exchange between the electronic medical record and public health registries. That said, in order to understand the current utility of FHIR^® for supporting the public health use case, we must first measure the extent to which the standard resources map to the required registry data elements. Thus, using a systematic mapping approach, we evaluated the level of completeness of the FHIR^® standard to support data collection for three public health registries (Trauma, Stroke, and National Surgical Quality Improvement Program). On average, approximately 80% of data elements were available in FHIR^® (71%, 77%, and 92%, respectively; inter-annotator agreement rates: 82%, 78%, and 72%, respectively). This tells us that there is the potential for significant automation to support EHR-to-Registry data exchange, which will reduce the amount of manual, error-prone processes and ensure higher data quality. Further, identification of the remaining 20% of data elements that are \"not mapped\" will enable us to improve the standard and develop profiles that will better fit the registry data model.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"18"},"PeriodicalIF":5.3,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10853080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139702708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChatGPT for Parents of Children Seeking Emergency Care - so much Hope, so much Caution.","authors":"Julie Yu, Clyde Matava","doi":"10.1007/s10916-024-02036-4","DOIUrl":"10.1007/s10916-024-02036-4","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"17"},"PeriodicalIF":5.3,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139672018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adoption and Sustained Use of Primary Care Video Visits Among Veterans with VA Video-Enabled Tablets.","authors":"Zainub Dhanani, Jacqueline M Ferguson, James Van Campen, Cindie Slightam, Leonie Heyworth, Donna M Zulman","doi":"10.1007/s10916-024-02035-5","DOIUrl":"10.1007/s10916-024-02035-5","url":null,"abstract":"<p><p>In 2020, the U.S. Department of Veterans Affairs (VA) expanded an initiative to distribute video-enabled tablets to Veterans with limited virtual care access. We examined patient characteristics associated with adoption and sustained use of video-based primary care among Veterans. We conducted a retrospective cohort study of Veterans who received VA-issued tablets between 3/11/2020-9/10/2020. We used generalized linear models to evaluate the sociodemographic and clinical factors associated with video-based primary care adoption (i.e., likelihood of having a primary care video visit) and sustained use (i.e., rate of video care) in the six months after a Veteran received a VA-issued tablet. Of the 36,077 Veterans who received a tablet, 69% had at least one video-based visit within six months, and 24% had a video-based visit in primary care. Veterans with a history of housing instability or a mental health condition, and those meeting VA enrollment criteria for low-income were significantly less likely to adopt video-based primary care. However, among Veterans who had a video visit in primary care (e.g., those with at least one video visit), older Veterans, and Veterans with a mental health condition had more sustained use (higher rate) than younger Veterans or those without a mental health condition. We found no differences in adoption of video-based primary care by rurality, age, race, ethnicity, or low/moderate disability and high disability priority groups compared to Veterans with no special enrollment category. VA's tablet initiative has supported many Veterans with complex needs in accessing primary care by video. While Veterans with certain social and clinical challenges were less likely to have a video visit, those who adopted video telehealth generally had similar or higher rates of sustained use. These patterns suggest opportunities for tailored interventions that focus on needs specific to initial uptake vs. sustained use of video care.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"16"},"PeriodicalIF":3.5,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139576073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning.","authors":"Hidir Selcuk Nogay, Hojjat Adeli","doi":"10.1007/s10916-023-02032-0","DOIUrl":"10.1007/s10916-023-02032-0","url":null,"abstract":"<p><p>The fact that the rapid and definitive diagnosis of autism cannot be made today and that autism cannot be treated provides an impetus to look into novel technological solutions. To contribute to the resolution of this problem through multiple classifications by considering age and gender factors, in this study, two quadruple and one octal classifications were performed using a deep learning (DL) approach. Gender in one of the four classifications and age groups in the other were considered. In the octal classification, classes were created considering gender and age groups. In addition to the diagnosis of ASD (Autism Spectrum Disorders), another goal of this study is to find out the contribution of gender and age factors to the diagnosis of ASD by making multiple classifications based on age and gender for the first time. Brain structural MRI (sMRI) scans of participators with ASD and TD (Typical Development) were pre-processed in the system originally designed for this purpose. Using the Canny Edge Detection (CED) algorithm, the sMRI image data was cropped in the data pre-processing stage, and the data set was enlarged five times with the data augmentation (DA) techniques. The most optimal convolutional neural network (CNN) models were developed using the grid search optimization (GSO) algorism. The proposed DL prediction system was tested with the five-fold cross-validation technique. Three CNN models were designed to be used in the system. The first of these models is the quadruple classification model created by taking gender into account (model 1), the second is the quadruple classification model created by taking into account age (model 2), and the third is the eightfold classification model created by taking into account both gender and age (model 3). ). The accuracy rates obtained for all three designed models are 80.94, 85.42 and 67.94, respectively. These obtained accuracy rates were compared with pre-trained models by using the transfer learning approach. As a result, it was revealed that age and gender factors were effective in the diagnosis of ASD with the system developed for ASD multiple classifications, and higher accuracy rates were achieved compared to pre-trained models.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"15"},"PeriodicalIF":3.5,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10803393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139512749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images.","authors":"Nico Curti, Yuri Merli, Corrado Zengarini, Michela Starace, Luca Rapparini, Emanuela Marcelli, Gianluca Carlini, Daniele Buschi, Gastone C Castellani, Bianca Maria Piraccini, Tommaso Bianchi, Enrico Giampieri","doi":"10.1007/s10916-023-02029-9","DOIUrl":"10.1007/s10916-023-02029-9","url":null,"abstract":"<p><p>Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"14"},"PeriodicalIF":3.5,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10791717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139472468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revolutionizing the Teaching of Ultrasound-Guided Vascular Access Procedures with Augmented Reality Headsets","authors":"Elizabeth Ternent-Rech, Thomas James Lockhart, J. A. Gálvez Delgado","doi":"10.1007/s10916-023-02025-z","DOIUrl":"https://doi.org/10.1007/s10916-023-02025-z","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"26 4","pages":"1-2"},"PeriodicalIF":5.3,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk","authors":"Dung-Jang Tsai, Chin Lin, Chin Lin, Chia-Cheng Lee, Chih-Hung Wang, Wen-Hui Fang","doi":"10.1007/s10916-023-02030-2","DOIUrl":"https://doi.org/10.1007/s10916-023-02030-2","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"29 11","pages":"1-12"},"PeriodicalIF":5.3,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}