Health Informatics Journal最新文献

{"title":"Machine learning-based prediction of cerebral oxygen saturation based on multi-modal cerebral oximetry data.","authors":"Kwang-Sig Lee, Su Jin Kim, Dong Cheol Kim, Sang-Hyun Park, Dong-Hyun Jang, Eung Hwi Kim, YoungShin Kang, Sijin Lee, Sung Woo Lee","doi":"10.1177/14604582241259341","DOIUrl":"10.1177/14604582241259341","url":null,"abstract":"<p><p>This study develops machine learning-based algorithms that facilitate accurate prediction of cerebral oxygen saturation using waveform data in the near-infrared range from a multi-modal oxygen saturation sensor. Data were obtained from 150,000 observations of a popular cerebral oximeter, Masimo O3™ regional oximetry (Co., United States) and a multi-modal cerebral oximeter, Votem (Inc., Korea). Among these observations, 112,500 (75%) and 37,500 (25%) were used for training and test sets, respectively. The dependent variable was the cerebral oxygen saturation value from the Masimo O3™ (0-100%). The independent variables were the time of measurement (0-300,000 ms) and the 16-bit decimal amplitudes values (infrared and red) from Votem (0-65,535). For the right part of the forehead, the root mean square error of the random forest (0.06) was much smaller than those of linear regression (1.22) and the artificial neural network with one, two or three hidden layers (2.58). The result was similar for the left part of forehead, that is, random forest (0.05) vs logistic regression (1.22) and the artificial neural network with one, two or three hidden layers (2.97). Machine learning aids in accurately predicting of cerebral oxygen saturation, employing the data from a multi-modal cerebral oximeter.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241259341"},"PeriodicalIF":2.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141285394","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":"Paving the way for COVID survivors' psychosocial rehabilitation: Mining topics, sentiments, and their trajectories over time from Reddit.","authors":"Moez Farokhnia Hamedani, Mostafa Esmaeili, Yao Sun, Ehsan Sheybani, Giti Javidi","doi":"10.1177/14604582241240680","DOIUrl":"10.1177/14604582241240680","url":null,"abstract":"<p><p><b>Objective:</b> This study examined major themes and sentiments and their trajectories and interactions over time using subcategories of Reddit data. The aim was to facilitate decision-making for psychosocial rehabilitation. <b>Materials and Methods:</b> We utilized natural language processing techniques, including topic modeling and sentiment analysis, on a dataset consisting of more than 38,000 topics, comments, and posts collected from a subreddit dedicated to the experiences of people who tested positive for COVID-19. In this longitudinal exploratory analysis, we studied the dynamics between the most dominant topics and subjects' emotional states over an 18-month period. <b>Results:</b> Our findings highlight the evolution of the textual and sentimental status of major topics discussed by COVID survivors over an extended period of time during the pandemic. We particularly studied pre- and post-vaccination eras as a turning point in the timeline of the pandemic. The results show that not only does the relevance of topics change over time, but the emotions attached to them also vary. Major social events, such as the administration of vaccines or enforcement of nationwide policies, are also reflected through the discussions and inquiries of social media users. In particular, the emotional state (i.e., sentiments and polarity of their feelings) of those who have experienced COVID personally. <b>Discussion:</b> Cumulative societal knowledge regarding the COVID-19 pandemic impacts the patterns with which people discuss their experiences, concerns, and opinions. The subjects' emotional state with respect to different topics was also impacted by extraneous factors and events, such as vaccination. <b>Conclusion:</b> By mining major topics, sentiments, and trajectories demonstrated in COVID-19 survivors' interactions on Reddit, this study contributes to the emerging body of scholarship on COVID-19 survivors' mental health outcomes, providing insights into the design of mental health support and rehabilitation services for COVID-19 survivors.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241240680"},"PeriodicalIF":2.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917652","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":"User experience of a family health history chatbot: A quantitative analysis.","authors":"Hiral Soni, Heath Morrison, Dinko Vasilev, Triton Ong, Hattie Wilczewski, Caitlin Allen, Chanita Hughes-Halbert, Jordon B Ritchie, Alexa Narma, Joshua D Schiffman, Julia Ivanova, Brian E Bunnell, Brandon M Welch","doi":"10.1177/14604582241262251","DOIUrl":"10.1177/14604582241262251","url":null,"abstract":"<p><strong>Objective: </strong>Family health history (FHx) is an important tool in assessing one's risk towards specific health conditions. However, user experience of FHx collection tools is rarely studied. ItRunsInMyFamily.com (ItRuns) was developed to assess FHx and hereditary cancer risk. This study reports a quantitative user experience analysis of ItRuns.</p><p><strong>Methods: </strong>We conducted a public health campaign in November 2019 to promote FHx collection using ItRuns. We used software telemetry to quantify abandonment and time spent on ItRuns to identify user behaviors and potential areas of improvement.</p><p><strong>Results: </strong>Of 11,065 users who started the ItRuns assessment, 4305 (38.91%) reached the final step to receive recommendations about hereditary cancer risk. Highest abandonment rates were during Introduction (32.82%), Invite Friends (29.03%), and Family Cancer History (12.03%) subflows. Median time to complete the assessment was 636 s. Users spent the highest median time on Proband Cancer History (124.00 s) and Family Cancer History (119.00 s) subflows. Search list questions took the longest to complete (median 19.50 s), followed by free text email input (15.00 s).</p><p><strong>Conclusion: </strong>Knowledge of objective user behaviors at a large scale and factors impacting optimal user experience will help enhance the ItRuns workflow and improve future FHx collection.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241262251"},"PeriodicalIF":2.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11391477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307470","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":"Continuously monitoring the human machine? - A cross-sectional study to assess the acceptance of wearables in Germany.","authors":"Michael Hindelang, Hannah Wecker, Tilo Biedermann, Alexander Zink","doi":"10.1177/14604582241260607","DOIUrl":"10.1177/14604582241260607","url":null,"abstract":"<p><p><b>Background:</b> Wearables have the potential to transform healthcare by enabling early detection and monitoring of chronic diseases. This study aimed to assess wearables' acceptance, usage, and reasons for non-use. <b>Methods:</b> Anonymous questionnaires were used to collect data in Germany on wearable ownership, usage behaviour, acceptance of health monitoring, and willingness to share data. <b>Results:</b> Out of 643 respondents, 550 participants provided wearable acceptance data. The average age was 36.6 years, with 51.3% female and 39.6% residing in rural areas. Overall, 33.8% reported wearing a wearable, primarily smartwatches or fitness wristbands. Men (63.3%) and women (57.8%) expressed willingness to wear a sensor for health monitoring, and 61.5% were open to sharing data with healthcare providers. Concerns included data security, privacy, and perceived lack of need. <b>Conclusion:</b> The study highlights the acceptance and potential of wearables, particularly for health monitoring and data sharing with healthcare providers. Addressing data security and privacy concerns could enhance the adoption of innovative wearables, such as implants, for early detection and monitoring of chronic diseases.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241260607"},"PeriodicalIF":2.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141433429","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":"A novel deep learning technique for medical image analysis using improved optimizer.","authors":"Vertika Agarwal, M C Lohani, Ankur Singh Bist","doi":"10.1177/14604582241255584","DOIUrl":"10.1177/14604582241255584","url":null,"abstract":"<p><p>Application of Convolutional neural network in spectrum of Medical image analysis are providing benchmark outputs which converges the interest of many researchers to explore it in depth. Latest preprocessing technique Real ESRGAN (Enhanced super resolution generative adversarial network) and GFPGAN (Generative facial prior GAN) are proving their efficacy in providing high resolution dataset. <b>Objective:</b> Optimizer plays a vital role in upgrading the functioning of CNN model. Different optimizers like Gradient descent, Stochastic Gradient descent, Adagrad, Adadelta and Adam etc. are used for classification and segmentation of Medical image but they suffer from slow processing due to their large memory requirement. Stochastic Gradient descent suffers from high variance and is computationally expensive. Dead neuron problem also proves to detrimental to the performance of most of the optimizers. A new optimization technique Gradient Centralization is providing the unparalleled result in terms of generalization and execution time. <b>Method:</b> Our paper explores the next factor which is the employment of new optimization technique, Gradient centralization (GC) to our integrated framework (Model with advanced preprocessing technique). <b>Result and conclusion:</b> Integrated Framework of Real ESRGAN and GFPGAN with Gradient centralization provides an optimal solution for deep learning models in terms of Execution time and Loss factor improvement.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241255584"},"PeriodicalIF":2.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140960972","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":"Nonproliferative diabetic retinopathy dataset(NDRD): A database for diabetic retinopathy screening research and deep learning evaluation.","authors":"Xing Liang, Haiqi Wen, Yajian Duan, Kan He, Xiufang Feng, Guohong Zhou","doi":"10.1177/14604582241259328","DOIUrl":"10.1177/14604582241259328","url":null,"abstract":"<p><strong>Objectives: </strong>In this article, we provide a database of nonproliferative diabetes retinopathy, which focuses on early diabetes retinopathy with hard exudation, and further explore its clinical application in disease recognition.</p><p><strong>Methods: </strong>We collect the photos of nonproliferative diabetes retinopathy taken by Optos Panoramic 200 laser scanning ophthalmoscope, filter out the pictures with poor quality, and label the hard exudative lesions in the images under the guidance of professional medical personnel. To validate the effectiveness of the datasets, five deep learning models are used to perform learning predictions on the datasets. Furthermore, we evaluate the performance of the model using evaluation metrics.</p><p><strong>Results: </strong>Nonproliferative diabetes retinopathy is smaller than proliferative retinopathy and more difficult to identify. The existing segmentation models have poor lesion segmentation performance, while the intersection over union (<i>IOU</i>) value for deep lesion segmentation of models targeting small lesions can reach 66.12%, which is higher than ordinary lesion segmentation models, but there is still a lot of room for improvement.</p><p><strong>Conclusion: </strong>The segmentation of small hard exudative lesions is more challenging than that of large hard exudative lesions. More targeted datasets are needed for model training. Compared with the previous diabetes retina datasets, the NDRD dataset pays more attention to micro lesions.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241259328"},"PeriodicalIF":2.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307469","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":"Contents analysis of telemedicine applications in South Korea: An analysis of possibility of inducing selective or unnecessary medical care.","authors":"HyoRim Ju, Donghee Seo, Soojeong Kim, Juyoung Choi, EunKyo Kang","doi":"10.1177/14604582241260644","DOIUrl":"10.1177/14604582241260644","url":null,"abstract":"<p><p>The use of telemedicine and telehealth has rapidly increased since the start of the COVID-19 pandemic, however, could lead to unnecessary medical service. This study analyzes the contents of telemedicine apps (applications) in South Korea to investigate the use of telemedicine for selective or unnecessary medical treatments and the presence of advertising for the hospital. This study analyzed 49 telemedicine mobile apps in Korea; a content analysis of the apps' features and quality using a Mobile Application Rating Scale was done. The study analyzed 49 mobile telemedicine apps and found that 65.3% of the apps provide immediate telemedicine service without reservations, with an average rating of 4.35. 87% of the apps offered selective care, but the overall quality of the apps was low, with an average total quality score of 3.27. 73.9% of the apps were able to provide selective care for alopecia or morning-after pill prescription, 65.2% of the apps for weight loss, and 52.2% of the apps for erectile dysfunction, with the potential to encourage medical inducement or abuse. Therefore, before introducing telemedicine, it is helpful to prevent the possibility of abuse of telemedicine by establishing detailed policies for methods and scope of telemedicine.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241260644"},"PeriodicalIF":2.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318969","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":"Longitudinal evaluation of an electronic audit and feedback system for patient safety in a large tertiary hospital setting.","authors":"James Soresi, Kevin Murray, Theresa Marshall, David B Preen","doi":"10.1177/14604582241262707","DOIUrl":"10.1177/14604582241262707","url":null,"abstract":"<p><p><b>Objective:</b> This study sought to assess the impact of a novel electronic audit and feedback (e-A&F) system on patient outcomes. <b>Methods:</b> The e-A&F intervention was implemented in a tertiary hospital and involved near real-time feedback via web-based dashboards. We used a segmented regression analysis of interrupted time series. We modelled the pre-post change in outcomes for the (1) announcement of this priority list, and (2) implementation of the e-A&F intervention to have affected patient outcomes. <b>Results:</b> Across the study period there were 222,792 episodes of inpatient care, of which 13,904 episodes were found to contain one or more HACs, a risk of 6.24%. From the point of the first intervention until the end of the study the overall risk of a HAC reduced from 8.57% to 4.12% - a 51.93% reduction. Of this reduction the proportion attributed to each of these interventions was found to be 29.99% for the announcement of the priority list and 21.93% for the implementation of the e-A&F intervention. <b>Discussion:</b> Our findings lend evidence to a mechanism that the announcement of a measurement framework, at a national level, can lead to local strategies, such as e-A&F, that lead to significant continued improvements over time.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241262707"},"PeriodicalIF":2.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318970","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":"Dilemmas and prospects of artificial intelligence technology in the data management of medical informatization in China: A new perspective on SPRAY-type AI applications.","authors":"Lu Lu, Yun Zhong, Shuqing Luo, Sichen Liu, Zhongzhou Xiao, Jinru Ding, Jin Shao, Hailong Fu, Jie Xu","doi":"10.1177/14604582241262961","DOIUrl":"10.1177/14604582241262961","url":null,"abstract":"<p><p><b>Objectives:</b> This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data within the healthcare sector, particularly within the context of Chinese medical information data management. The research seeks to propose a solution in the form of a medical metadata governance framework that is efficient and suitable for clinical research and transformation. <b>Methods:</b> The article begins by outlining the background of medical information data management and reviews the advancements in artificial intelligence (AI) technology relevant to the field. It then introduces the \"Service, Patient, Regression, base/Away, Yeast\" (SPRAY)-type AI application as a case study to illustrate the potential of AI in EMR data management. <b>Results:</b> The research identifies the scarcity of scientific research on the transformation of EMR data in Chinese hospitals and proposes a medical metadata governance framework as a solution. This framework is designed to achieve scientific governance of clinical data by integrating metadata management and master data management, grounded in clinical practices, medical disciplines, and scientific exploration. Furthermore, it incorporates an information privacy security architecture to ensure data protection. <b>Conclusion:</b> The proposed medical metadata governance framework, supported by AI technology, offers a structured approach to managing and transforming EMR data into valuable scientific research outcomes. This framework provides guidance for the identification, cleaning, mining, and deep application of EMR data, thereby addressing the bottlenecks currently faced in the healthcare scenario and paving the way for more effective clinical research and data-driven decision-making.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241262961"},"PeriodicalIF":2.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332559","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":"Transition database for rare diseases and its use for clinical documentation.","authors":"Michele Zoch, Christian Gierschner, Richard Gebler, Liz A Leutner, Tanita Kretschmer, Adrian Danker, Min Ae Lee-Kirsch, Reinhard Berner, Martin Sedlmayr","doi":"10.1177/14604582241259322","DOIUrl":"10.1177/14604582241259322","url":null,"abstract":"<p><p>Patients with rare diseases commonly suffer from severe symptoms as well as chronic and sometimes life-threatening effects. Not only the rarity of the diseases but also the poor documentation of rare diseases often leads to an immense delay in diagnosis. One of the main problems here is the inadequate coding with common classifications such as the International Statistical Classification of Diseases and Related Health Problems. Instead, the ORPHAcode enables precise naming of the diseases. So far, just few approaches report in detail how the technical implementation of the ORPHAcode is done in clinical practice and for research. We present a concept and implementation of storing and mapping of ORPHAcodes. The Transition Database for Rare Diseases contains all the information of the Orphanet catalog and serves as the basis for documentation in the clinical information system as well as for monitoring Key Performance Indicators for rare diseases at the hospital. The five-step process (especially using open source tools and the <i>DataVault 2.0</i> logic) for set-up the Transition Database allows the approach to be adapted to local conditions as well as to be extended for additional terminologies and ontologies.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 2","pages":"14604582241259322"},"PeriodicalIF":2.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297321","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}