Health data science最新文献_第5页

Stratification of Patients with Diabetes Using Continuous Glucose Monitoring Profiles and Machine Learning. 使用连续血糖监测资料和机器学习对糖尿病患者进行分层

Health data science Pub Date : 2022-04-27 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9892340

Yinan Mao, Kyle Xin Quan Tan, Augustin Seng, Peter Wong, Sue-Anne Toh, Alex R Cook

{"title":"Stratification of Patients with Diabetes Using Continuous Glucose Monitoring Profiles and Machine Learning.","authors":"Yinan Mao, Kyle Xin Quan Tan, Augustin Seng, Peter Wong, Sue-Anne Toh, Alex R Cook","doi":"10.34133/2022/9892340","DOIUrl":"10.34133/2022/9892340","url":null,"abstract":"Background. Continuous glucose monitoring (CGM) offers an opportunity for patients with diabetes to modify their lifestyle to better manage their condition and for clinicians to provide personalized healthcare and lifestyle advice. However, analytic tools are needed to standardize and analyze the rich data that emerge from CGM devices. This would allow glucotypes of patients to be identified to aid clinical decision-making.Methods. In this paper, we develop an analysis pipeline for CGM data and apply it to 148 diabetic patients with a total of 8632 days of follow up. The pipeline projects CGM data to a lower-dimensional space of features representing centrality, spread, size, and duration of glycemic excursions and the circadian cycle. We then use principal components analysis and <math><mi>k</mi></math>-means to cluster patients' records into one of four glucotypes and analyze cluster membership using multinomial logistic regression.Results. Glucotypes differ in the degree of control, amount of time spent in range, and on the presence and timing of hyper- and hypoglycemia. Patients on the program had statistically significant improvements in their glucose levels.Conclusions. This pipeline provides a fast automatic function to label raw CGM data without manual input.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42981302","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}

引用次数: 0

A Review of Three-Dimensional Medical Image Visualization. 三维医学图像可视化研究综述

Health data science Pub Date : 2022-04-05 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9840519

Liang Zhou, Mengjie Fan, Charles Hansen, Chris R Johnson, Daniel Weiskopf

{"title":"A Review of Three-Dimensional Medical Image Visualization.","authors":"Liang Zhou, Mengjie Fan, Charles Hansen, Chris R Johnson, Daniel Weiskopf","doi":"10.34133/2022/9840519","DOIUrl":"10.34133/2022/9840519","url":null,"abstract":"Importance. Medical images are essential for modern medicine and an important research subject in visualization. However, medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques that could increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Our paper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medical experts and visualization researchers.Highlights. Fundamental visualization techniques are revisited for various medical imaging modalities, from computational tomography to diffusion tensor imaging, featuring techniques that enhance spatial perception, which is critical for medical practices. The state-of-the-art of medical visualization is reviewed based on a procedure-oriented classification of medical problems for studies of individuals and populations. This paper summarizes free software tools for different modalities of medical images designed for various purposes, including visualization, analysis, and segmentation, and it provides respective Internet links.Conclusions. Visualization techniques are a useful tool for medical experts to tackle specific medical problems in their daily work. Our review provides a quick reference to such techniques given the medical problem and modalities of associated medical images. We summarize fundamental techniques and readily available visualization tools to help medical experts to better understand and utilize medical imaging data. This paper could contribute to the joint effort of the medical and visualization communities to advance precision medicine.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43760737","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}

引用次数: 0

Cost-Utility Analysis of Screening for Diabetic Retinopathy in China. 中国糖尿病视网膜病变筛查的成本效用分析

Health data science Pub Date : 2022-03-12 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9832185

Yue Zhang, Weiling Bai, Ruyue Li, Yifan Du, Runzhou Sun, Tao Li, Hong Kang, Ziwei Yang, Jianjun Tang, Ningli Wang, Hanruo Liu

{"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":"Background. 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.Methods. 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.Results. 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.Conclusions. 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.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"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}

引用次数: 0

Misinformation versus Facts: Understanding the Influence of News regarding COVID-19 Vaccines on Vaccine Uptake. 错误信息与事实：了解有关 COVID-19 疫苗的新闻对疫苗接种的影响。

Health data science Pub Date : 2022-03-12 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9858292

Hanjia Lyu, Zihe Zheng, Jiebo Luo

{"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":"Background: There is a lot of fact-based information and misinformation in the online discourses and discussions about the COVID-19 vaccines.Method: 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.Results: One percent increase in fact-related Twitter users is associated with an approximately 0.87 decrease (B = -0.87, SE = 0.25, and p < .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.Conclusion: 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.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"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}

引用次数: 0

Social Determinants, Data Science, and Decision Making: The 3-D Approach to Achieving Health Equity in Asia. 社会决定因素、数据科学和决策:实现亚洲卫生公平的三维方法

Health data science Pub Date : 2022-02-21 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9805154

Luxia Zhang, Sabina Faiz Rashid, Gabriel Leung

引用次数: 0

The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States. COVID-19 大流行与美国 Twitter 上的心理健康问题。

Health data science Pub Date : 2022-02-17 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9758408

Senqi Zhang, Li Sun, Daiwei Zhang, Pin Li, Yue Liu, Ajay Anand, Zidian Xie, Dongmei Li

{"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":"Background: 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.Methods: COVID-19-related tweets from March 5th, 2020, to January 31st, 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.Results: 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.Conclusions: 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.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"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}

引用次数: 0

Next Decade's AI-Based Drug Development Features Tight Integration of Data and Computation. 未来十年基于人工智能的药物开发:数据与计算紧密结合

Health data science Pub Date : 2022-01-17 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9816939

Yunan Luo, Jian Peng, Jianzhu Ma

引用次数: 0

Association of PM _2.5 Reduction with Improved Kidney Function: A Nationwide Quasiexperiment among Chinese Adults. 降低 PM 2.5 与改善肾功能的关系：一项针对中国成年人的全国性准实验。

Health data science Pub Date : 2022-01-15 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9846805

Yiqun Han, Tao Xue, Frank J Kelly, Yixuan Zheng, Yao Yao, Jiajianghui Li, Jiwei Li, Chun Fan, Pengfei Li, Tong Zhu

{"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":"Background. 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.Methods. 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).Results. For a 10 μg/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.Conclusions. 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.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"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}

引用次数: 0

Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use. 大规模的社交媒体分析揭示了与非医疗处方药使用相关的情绪。

Health data science Pub Date : 2022-01-01 DOI: 10.34133/2022/9851989

Mohammed Ali Al-Garadi, Yuan-Chi Yang, Yuting Guo, Sangmi Kim, Jennifer S Love, Jeanmarie Perrone, Abeed Sarker

{"title":"Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use.","authors":"Mohammed Ali Al-Garadi, Yuan-Chi Yang, Yuting Guo, Sangmi Kim, Jennifer S Love, Jeanmarie Perrone, Abeed Sarker","doi":"10.34133/2022/9851989","DOIUrl":"https://doi.org/10.34133/2022/9851989","url":null,"abstract":"Background: 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.Methods: 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.Results: 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).Conclusion: 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.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"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}

引用次数: 2

Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review. 医学影像分析中的知识图谱应用：范围综述。

Health data science Pub Date : 2022-01-01 Epub Date: 2022-06-14 DOI: 10.34133/2022/9841548

Song Wang, Mingquan Lin, Tirthankar Ghosal, Ying Ding, Yifan Peng

{"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":"Background: 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.Methods: 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.Results: 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.Conclusions: 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.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"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}

引用次数: 0