Health data science最新文献

{"title":"Cost-Utility Analysis of Screening for Diabetic Retinopathy in China.","authors":"Yue Zhang, Weiling Bai, Ruyue Li, Yifan Du, Runzhou Sun, Tao Li, Hong Kang, Ziwei Yang, Jianjun Tang, Ningli Wang, Hanruo Liu","doi":"10.34133/2022/9832185","DOIUrl":"10.34133/2022/9832185","url":null,"abstract":"<p><p><i>Background</i>. Diabetic retinopathy (DR) has been primarily indicated to cause vision impairment and blindness, while no studies have focused on the cost-utility of telemedicine-based and community screening programs for DR in China, especially in rural and urban areas, respectively.<i>Methods</i>. We developed a Markov model to calculate the cost-utility of screening programs for DR in DM patients in rural and urban settings from the societal perspective. The incremental cost-utility ratio (ICUR) was calculated for the assessment.<i>Results</i>. In the rural setting, the community screening program obtained 1 QALY with a cost of $4179 (95% CI 3859 to 5343), and the telemedicine screening program had an ICUR of $2323 (95% CI 1023 to 3903) compared with no screening, both of which satisfied the criterion of a significantly cost-effective health intervention. Likewise, community screening programs in urban areas generated an ICUR of $3812 (95% CI 2906 to 4167) per QALY gained, with telemedicine screening at an ICUR of $2437 (95% CI 1242 to 3520) compared with no screening, and both were also cost-effective. By further comparison, compared to community screening programs, telemedicine screening yielded an ICUR of 1212 (95% CI 896 to 1590) per incremental QALY gained in rural setting and 1141 (95% CI 859 to 1403) in urban setting, which both meet the criterion for a significantly cost-effective health intervention.<i>Conclusions</i>. Both telemedicine and community screening for DR in rural and urban settings were cost-effective in China, and telemedicine screening programs were more cost-effective.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9832185"},"PeriodicalIF":0.0,"publicationDate":"2022-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10904067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42114491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Misinformation versus Facts: Understanding the Influence of News regarding COVID-19 Vaccines on Vaccine Uptake.","authors":"Hanjia Lyu, Zihe Zheng, Jiebo Luo","doi":"10.34133/2022/9858292","DOIUrl":"10.34133/2022/9858292","url":null,"abstract":"<p><strong>Background: </strong>There is a lot of fact-based information and misinformation in the online discourses and discussions about the COVID-19 vaccines.</p><p><strong>Method: </strong>Using a sample of nearly four million geotagged English tweets and the data from the CDC COVID Data Tracker, we conducted the Fama-MacBeth regression with the Newey-West adjustment to understand the influence of both misinformation and fact-based news on Twitter on the COVID-19 vaccine uptake in the US from April 19 when US adults were vaccine eligible to June 30, 2021, after controlling state-level factors such as demographics, education, and the pandemic severity. We identified the tweets related to either misinformation or fact-based news by analyzing the URLs.</p><p><strong>Results: </strong>One percent increase in fact-related Twitter users is associated with an approximately 0.87 decrease (<i>B</i> = -0.87, SE = 0.25, and <i>p</i> < .001) in the number of daily new vaccinated people per hundred. No significant relationship was found between the percentage of fake-news-related users and the vaccination rate.</p><p><strong>Conclusion: </strong>The negative association between the percentage of fact-related users and the vaccination rate might be due to a combination of a larger user-level influence and the negative impact of online social endorsement on vaccination intent.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9858292"},"PeriodicalIF":0.0,"publicationDate":"2022-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40700244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Determinants, Data Science, and Decision Making: The 3-D Approach to Achieving Health Equity in Asia.","authors":"Luxia Zhang, Sabina Faiz Rashid, Gabriel Leung","doi":"10.34133/2022/9805154","DOIUrl":"10.34133/2022/9805154","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9805154"},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44436520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States.","authors":"Senqi Zhang, Li Sun, Daiwei Zhang, Pin Li, Yue Liu, Ajay Anand, Zidian Xie, Dongmei Li","doi":"10.34133/2022/9758408","DOIUrl":"10.34133/2022/9758408","url":null,"abstract":"<p><strong>Background: </strong>During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns.</p><p><strong>Methods: </strong>COVID-19-related tweets from March 5^th, 2020, to January 31^st, 2021, were collected through Twitter streaming API using keywords (i.e., \"corona,\" \"covid19,\" and \"covid\"). By further filtering using keywords (i.e., \"depress,\" \"failure,\" and \"hopeless\"), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users' discussions surrounding mental health concerns. Deep learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the pandemic.</p><p><strong>Results: </strong>We observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that \"stay-at-home,\" \"death poll,\" and \"politics and policy\" were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns.</p><p><strong>Conclusions: </strong>The COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males and White) were more likely to have mental health concerns during the COVID-19 pandemic.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9758408"},"PeriodicalIF":0.0,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40700245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Next Decade's AI-Based Drug Development Features Tight Integration of Data and Computation.","authors":"Yunan Luo, Jian Peng, Jianzhu Ma","doi":"10.34133/2022/9816939","DOIUrl":"10.34133/2022/9816939","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9816939"},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47169378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of PM _2.5 Reduction with Improved Kidney Function: A Nationwide Quasiexperiment among Chinese Adults.","authors":"Yiqun Han, Tao Xue, Frank J Kelly, Yixuan Zheng, Yao Yao, Jiajianghui Li, Jiwei Li, Chun Fan, Pengfei Li, Tong Zhu","doi":"10.34133/2022/9846805","DOIUrl":"10.34133/2022/9846805","url":null,"abstract":"<p><p><i>Background</i>. Increasing evidence from human studies has revealed the adverse impact of ambient fine particles (PM _2.5) on health outcomes related to metabolic disorders and distant organs. Whether exposure to ambient PM _2.5 leads to kidney impairment remains unclear. The rapid air quality improvement driven by the clean air actions in China since 2013 provides an opportunity for a quasiexperiment to investigate the beneficial effect of PM _2.5 reduction on kidney function.<i>Methods</i>. Based on two repeated nationwide surveys of the same population of 5115 adults in 2011 and 2015, we conducted a difference-in-difference study. Variations in long-term exposure to ambient PM _2.5 were associated with changes in kidney function biomarkers, including estimated glomerular filtration rate by serum creatinine (GFR _scr) or cystatin C (GFR _cys), blood urea nitrogen (BUN), and uric acid (UA).<i>Results</i>. For a 10  <i>μ</i>g/m ³ reduction in PM _2.5, a significant improvement was observed for multiple kidney functional biomarkers, including GFR _scr, BUN and UA, with a change of 0.42 (95% confidence interval [CI]: 0.06, 0.78) mL/min/1.73m ², -0.38 (-0.64, -0.12) mg/dL, and -0.06 (-0.12, -0.00) mg/dL, respectively. A lower socioeconomic status, indicated by rural residence or low educational level, enhanced the adverse effect of PM _2.5 on kidney function.<i>Conclusions</i>. These results support a significant nephrotoxicity of PM _2.5 based on multiple serum biomarkers and indicate a beneficial effect of improved air quality on kidney function.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"2022 ","pages":"9846805"},"PeriodicalIF":0.0,"publicationDate":"2022-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10904065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140133415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use.","authors":"Mohammed Ali Al-Garadi,&nbsp;Yuan-Chi Yang,&nbsp;Yuting Guo,&nbsp;Sangmi Kim,&nbsp;Jennifer S Love,&nbsp;Jeanmarie Perrone,&nbsp;Abeed Sarker","doi":"10.34133/2022/9851989","DOIUrl":"https://doi.org/10.34133/2022/9851989","url":null,"abstract":"<p><strong>Background: </strong>The behaviors and emotions associated with and reasons for nonmedical prescription drug use (NMPDU) are not well-captured through traditional instruments such as surveys and insurance claims. Publicly available NMPDU-related posts on social media can potentially be leveraged to study these aspects unobtrusively and at scale.</p><p><strong>Methods: </strong>We applied a machine learning classifier to detect self-reports of NMPDU on Twitter and extracted all public posts of the associated users. We analyzed approximately 137 million posts from 87,718 Twitter users in terms of expressed emotions, sentiments, concerns, and possible reasons for NMPDU via natural language processing.</p><p><strong>Results: </strong>Users in the NMPDU group express more negative emotions and less positive emotions, more concerns about family, the past, and body, and less concerns related to work, leisure, home, money, religion, health, and achievement compared to a control group (i.e., users who never reported NMPDU). NMPDU posts tend to be highly polarized, indicating potential emotional triggers. Gender-specific analyses show that female users in the NMPDU group express more content related to positive emotions, anticipation, sadness, joy, concerns about family, friends, home, health, and the past, and less about anger than males. The findings are consistent across distinct prescription drug categories (opioids, benzodiazepines, stimulants, and polysubstance).</p><p><strong>Conclusion: </strong>Our analyses of large-scale data show that substantial differences exist between the texts of the posts from users who self-report NMPDU on Twitter and those who do not, and between males and females who report NMPDU. Our findings can enrich our understanding of NMPDU and the population involved.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"2022 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/51/91/nihms-1819277.PMC10449547.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10101392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review.","authors":"Song Wang, Mingquan Lin, Tirthankar Ghosal, Ying Ding, Yifan Peng","doi":"10.34133/2022/9841548","DOIUrl":"10.34133/2022/9841548","url":null,"abstract":"<p><strong>Background: </strong>There is an increasing trend to represent domain knowledge in structured graphs, which provide efficient knowledge representations for many downstream tasks. Knowledge graphs are widely used to model prior knowledge in the form of nodes and edges to represent semantically connected knowledge entities, which several works have adopted into different medical imaging applications.</p><p><strong>Methods: </strong>We systematically searched over five databases to find relevant articles that applied knowledge graphs to medical imaging analysis. After screening, evaluating, and reviewing the selected articles, we performed a systematic analysis.</p><p><strong>Results: </strong>We looked at four applications in medical imaging analysis, including disease classification, disease localization and segmentation, report generation, and image retrieval. We also identified limitations of current work, such as the limited amount of available annotated data and weak generalizability to other tasks. We further identified the potential future directions according to the identified limitations, including employing semisupervised frameworks to alleviate the need for annotated data and exploring task-agnostic models to provide better generalizability.</p><p><strong>Conclusions: </strong>We hope that our article will provide the readers with aggregated documentation of the state-of-the-art knowledge graph applications for medical imaging to encourage future research.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40480656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Angiotensin-Converting Enzyme (ACE) Inhibitors May Moderate COVID-19 Hyperinflammatory Response: An Observational Study with Deep Immunophenotyping.","authors":"Venkata R Duvvuri,&nbsp;Andrew Baumgartner,&nbsp;Sevda Molani,&nbsp;Patricia V Hernandez,&nbsp;Dan Yuan,&nbsp;Ryan T Roper,&nbsp;Wanessa F Matos,&nbsp;Max Robinson,&nbsp;Yapeng Su,&nbsp;Naeha Subramanian,&nbsp;Jason D Goldman,&nbsp;James R Heath,&nbsp;Jennifer J Hadlock","doi":"10.34133/hds.0002","DOIUrl":"https://doi.org/10.34133/hds.0002","url":null,"abstract":"<p><strong>Background: </strong>Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin-II receptor blockers (ARB), the most commonly prescribed antihypertensive medications, counter renin-angiotensin-aldosterone system (RAAS) activation via induction of angiotensin-converting enzyme 2 (ACE2) expression. Considering that ACE2 is the functional receptor for SARS-CoV-2 entry into host cells, the association of ACEi and ARB with COVID-19 outcomes needs thorough evaluation.</p><p><strong>Methods: </strong>We conducted retrospective analyses using both unmatched and propensity score (PS)-matched cohorts on electronic health records (EHRs) to assess the impact of RAAS inhibitors on the risk of receiving invasive mechanical ventilation (IMV) and 30-day mortality among hospitalized COVID-19 patients. Additionally, we investigated the immune cell gene expression profiles of hospitalized COVID-19 patients with prior use of antihypertensive treatments from an observational prospective cohort.</p><p><strong>Results: </strong>The retrospective analysis revealed that there was no increased risk associated with either ACEi or ARB use. In fact, the use of ACEi showed decreased risk for mortality. Survival analyses using PS-matched cohorts suggested no significant relationship between RAAS inhibitors with a hospital stay and in-hospital mortality compared to non-RAAS medications and patients not on antihypertensive medications. From the analysis of gene expression profiles, we observed a noticeable up-regulation in the expression of 1L1R2 (an anti-inflammatory receptor) and RETN (an immunosuppressive marker) genes in monocytes among prior users of ACE inhibitors.</p><p><strong>Conclusion: </strong>Overall, the findings do not support the discontinuation of ACEi or ARB treatment and suggest that ACEi may moderate the COVID-19 hyperinflammatory response.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"2022 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9697205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Correcting Recurrent Neural Network for Acute Kidney Injury Prediction in Critical Care.","authors":"Hao Du, Ziyuan Pan, Kee Yuan Ngiam, Fei Wang, Ping Shum, Mengling Feng","doi":"10.34133/2021/9808426","DOIUrl":"10.34133/2021/9808426","url":null,"abstract":"<p><p><i>Background</i>. In critical care, intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions, which has always been a challenging task. Recently, deep learning models such as recurrent neural networks (RNNs) have demonstrated their strong potential on predicting such events. However, in real deployment, the patient data are continuously coming and there is no effective adaptation mechanism for RNN to incorporate those new data and become more accurate.<i>Methods</i>. In this study, we propose a novel self-correcting mechanism for RNN to fill in this gap. Our mechanism feeds prediction errors from the predictions of previous timestamps into the prediction of the current timestamp, so that the model can \"learn\" from previous predictions. We also proposed a regularization method that takes into account not only the model's prediction errors on the labels but also its estimation errors on the input data.<i>Results</i>. We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets for the task of acute kidney injury (AKI) prediction and demonstrated that the proposed model achieved an area under ROC curve at 0.893 on the MIMIC-III dataset and 0.871 on the Philips eICU dataset.<i>Conclusions</i>. The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"1 1","pages":"9808426"},"PeriodicalIF":0.0,"publicationDate":"2021-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10904062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43278640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}