NPJ Digital Medicine最新文献_第3页

The real-world association between digital markers of circadian disruption and mental health risks

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-12-05 DOI: 10.1038/s41746-024-01348-6

Minki P. Lee, Dae Wook Kim, Yu Fang, Ruby Kim, Amy S. B. Bohnert, Srijan Sen, Daniel B. Forger

{"title":"The real-world association between digital markers of circadian disruption and mental health risks","authors":"Minki P. Lee, Dae Wook Kim, Yu Fang, Ruby Kim, Amy S. B. Bohnert, Srijan Sen, Daniel B. Forger","doi":"10.1038/s41746-024-01348-6","DOIUrl":"10.1038/s41746-024-01348-6","url":null,"abstract":"While circadian disruption is recognized as a potential driver of depression, its real-world impact is poorly understood. A critical step to addressing this is the noninvasive collection of physiological time-series data outside laboratory settings in large populations. Digital tools offer promise in this endeavor. Here, using wearable data, we first quantify the degrees of circadian disruption, both between different internal rhythms and between each internal rhythm and the sleep-wake cycle. Our analysis, based on over 50,000 days of data from over 800 first-year training physicians, reveals bidirectional links between digital markers of circadian disruption and mood both before and after they began shift work, while accounting for confounders such as demographic and geographic variables. We further validate this by finding clinically relevant changes in the 9-item Patient Health Questionnaire score. Our findings validate a scalable digital measure of circadian disruption that could serve as a marker for psychiatric intervention.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-15"},"PeriodicalIF":12.4,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01348-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging natural language processing to aggregate field safety notices of medical devices across the EU

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-12-04 DOI: 10.1038/s41746-024-01337-9

Yijun Ren, Enrico Gianluca Caiani

引用次数: 0

A scoping review on pediatric sepsis prediction technologies in healthcare

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-12-04 DOI: 10.1038/s41746-024-01361-9

Ryan Tennant, Jennifer Graham, Juliet Kern, Kate Mercer, J. Mark Ansermino, Catherine M. Burns

{"title":"A scoping review on pediatric sepsis prediction technologies in healthcare","authors":"Ryan Tennant, Jennifer Graham, Juliet Kern, Kate Mercer, J. Mark Ansermino, Catherine M. Burns","doi":"10.1038/s41746-024-01361-9","DOIUrl":"10.1038/s41746-024-01361-9","url":null,"abstract":"This scoping review evaluates recent advancements in data-driven technologies for predicting non-neonatal pediatric sepsis, including artificial intelligence, machine learning, and other methodologies. Of the 27 included studies, 23 (85%) were single-center investigations, and 16 (59%) used logistic regression. Notably, 20 (74%) studies used datasets with a low prevalence of sepsis-related outcomes, with area under the receiver operating characteristic scores ranging from 0.56 to 0.99. Prediction time points varied widely, and development characteristics, performance metrics, implementation outcomes, and considerations for human factors—especially workflow integration and clinical judgment—were inconsistently reported. The variations in endpoint definitions highlight the potential significance of the 2024 consensus criteria in future development. Future research should strengthen the involvement of clinical users to enhance the understanding and integration of human factors in designing and evaluating these technologies, ultimately aiming for safe and effective integration in pediatric healthcare.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-16"},"PeriodicalIF":12.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01361-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence related safety issues associated with FDA medical device reports

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-12-03 DOI: 10.1038/s41746-024-01357-5

Jessica L. Handley, Seth A. Krevat, Allan Fong, Raj M. Ratwani

引用次数: 0

Reimbursement in the age of generalist radiology artificial intelligence

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-12-02 DOI: 10.1038/s41746-024-01352-w

Siddhant Dogra, Ezequiel “Zeke” Silva III, Pranav Rajpurkar

引用次数: 0

Identifying Parkinson’s disease and its stages using static standing balance

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-30 DOI: 10.1038/s41746-024-01351-x

Dawoon Jung, Dallah Yoo, Jinwook Kim, Tae-Beom Ahn, Kyung-Ryoul Mun

引用次数: 0

Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-30 DOI: 10.1038/s41746-024-01300-8

Agustina D. Saenz, Mass General Brigham AI Governance Committee, Amanda Centi, David Ting, Jacqueline G. You, Adam Landman, Rebecca G. Mishuris

{"title":"Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions","authors":"Agustina D. Saenz, Mass General Brigham AI Governance Committee, Amanda Centi, David Ting, Jacqueline G. You, Adam Landman, Rebecca G. Mishuris","doi":"10.1038/s41746-024-01300-8","DOIUrl":"10.1038/s41746-024-01300-8","url":null,"abstract":"This report presents a comprehensive case study for the responsible integration of artificial intelligence (AI) into healthcare settings. Recognizing the rapid advancement of AI technologies and their potential to transform healthcare delivery, we propose a set of guidelines emphasizing fairness, robustness, privacy, safety, transparency, explainability, accountability, and benefit. Through a multidisciplinary collaboration, we developed and operationalized these guidelines within a healthcare system, highlighting a case study on ambient documentation to demonstrate the practical application and challenges of implementing generative AI in clinical environments. Our proposed framework ensures continuous monitoring, evaluation, and adaptation of AI technologies, addressing ethical considerations and enhancing patient care. This work contributes to the discourse on responsible AI use in healthcare, offering a blueprint for institutions to navigate the complexities of AI integration responsibly and effectively, thus promoting better, more equitable healthcare outcomes.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-6"},"PeriodicalIF":12.4,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01300-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phenotyping people with a history of injecting drug use within electronic medical records using an interactive machine learning approach

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-30 DOI: 10.1038/s41746-024-01318-y

Carol El-Hayek, Thi Nguyen, Margaret E. Hellard, Michael Curtis, Rachel Sacks-Davis, Htein Linn Aung, Jason Asselin, Douglas I. R. Boyle, Anna Wilkinson, Victoria Polkinghorne, Jane S. Hocking, Adam G. Dunn

{"title":"Phenotyping people with a history of injecting drug use within electronic medical records using an interactive machine learning approach","authors":"Carol El-Hayek, Thi Nguyen, Margaret E. Hellard, Michael Curtis, Rachel Sacks-Davis, Htein Linn Aung, Jason Asselin, Douglas I. R. Boyle, Anna Wilkinson, Victoria Polkinghorne, Jane S. Hocking, Adam G. Dunn","doi":"10.1038/s41746-024-01318-y","DOIUrl":"10.1038/s41746-024-01318-y","url":null,"abstract":"People with a history of injecting drug use are a priority for eliminating blood-borne viruses and sexually transmissible infections. Identifying them for disease surveillance in electronic medical records (EMRs) is challenged by sparsity of predictors. This study introduced a novel approach to phenotype people who have injected drugs using structured EMR data and interactive human-in-the-loop methods. We iteratively trained random forest classifiers removing important features and adding new positive labels each time. The initial model achieved 92.7% precision and 93.5% recall. Models maintained >90% precision and recall after nine iterations, revealing combinations of less obvious features influencing predictions. Applied to approximately 1.7 million patients, the final model identified 128,704 (7.7%) patients as potentially having injected drugs, beyond the 50,510 (2.9%) with known indicators of injecting drug use. This process produced explainable models that revealed otherwise hidden combinations of predictors, offering an adaptive approach to addressing the inherent challenge of inconsistently missing data in EMRs.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-10"},"PeriodicalIF":12.4,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01318-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-30 DOI: 10.1038/s41746-024-01328-w

Mingyang Chen, Yuting Wang, Qiankun Wang, Jingyi Shi, Huike Wang, Zichen Ye, Peng Xue, Youlin Qiao

{"title":"Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation","authors":"Mingyang Chen, Yuting Wang, Qiankun Wang, Jingyi Shi, Huike Wang, Zichen Ye, Peng Xue, Youlin Qiao","doi":"10.1038/s41746-024-01328-w","DOIUrl":"10.1038/s41746-024-01328-w","url":null,"abstract":"Clinicians face increasing workloads in medical imaging interpretation, and artificial intelligence (AI) offers potential relief. This meta-analysis evaluates the impact of human-AI collaboration on image interpretation workload. Four databases were searched for studies comparing reading time or quantity for image-based disease detection before and after AI integration. The Quality Assessment of Studies of Diagnostic Accuracy was modified to assess risk of bias. Workload reduction and relative diagnostic performance were pooled using random-effects model. Thirty-six studies were included. AI concurrent assistance reduced reading time by 27.20% (95% confidence interval, 18.22%–36.18%). The reading quantity decreased by 44.47% (40.68%–48.26%) and 61.72% (47.92%–75.52%) when AI served as the second reader and pre-screening, respectively. Overall relative sensitivity and specificity are 1.12 (1.09, 1.14) and 1.00 (1.00, 1.01), respectively. Despite these promising results, caution is warranted due to significant heterogeneity and uneven study quality.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-10"},"PeriodicalIF":12.4,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01328-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural language processing in mixed-methods evaluation of a digital sleep-alcohol intervention for young adults 自然语言处理在针对年轻人的数字睡眠-酒精干预混合方法评估中的应用

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-29 DOI: 10.1038/s41746-024-01321-3

Frances J. Griffith, Garrett I. Ash, Madilyn Augustine, Leah Latimer, Naomi Verne, Nancy S. Redeker, Stephanie S. O’Malley, Kelly S. DeMartini, Lisa M. Fucito

{"title":"Natural language processing in mixed-methods evaluation of a digital sleep-alcohol intervention for young adults","authors":"Frances J. Griffith, Garrett I. Ash, Madilyn Augustine, Leah Latimer, Naomi Verne, Nancy S. Redeker, Stephanie S. O’Malley, Kelly S. DeMartini, Lisa M. Fucito","doi":"10.1038/s41746-024-01321-3","DOIUrl":"10.1038/s41746-024-01321-3","url":null,"abstract":"We used natural language processing (NLP) in convergent mixed methods to evaluate young adults’ experiences with Call it a Night (CIAN), a digital personalized feedback and coaching sleep-alcohol intervention. Young adults with heavy drinking (N = 120) were randomized to CIAN or controls (A + SM: web-based advice + self-monitoring or A: advice; clinicaltrials.gov, 8/31/18, #NCT03658954). Most CIAN participants (72.0%) preferred coaching to control interventions. Control participants found advice more helpful than CIAN participants (X2 = 27.34, p < 0.001). Most participants were interested in sleep factors besides alcohol and appreciated increased awareness through monitoring. NLP corroborated generally positive sentiments (M = 15.07(10.54)) and added critical insight that sleep (40%), not alcohol use (12%), was a main participant motivator. All groups had high adherence, satisfaction, and feasibility. CIAN (Δ = 0.48, p = 0.008) and A + SM (Δ = 0.55, p < 0.001) had higher reported effectiveness than A (F(2, 115) = 8.45, p < 0.001). Digital sleep-alcohol interventions are acceptable, and improving sleep and wellness may be important motivations for young adults.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":12.4,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01321-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0