NPJ Digital Medicine最新文献_第2页

Long-term changes in wearable sensor data in people with and without Long Covid Long Covid 患者和非 Long Covid 患者的可穿戴传感器数据的长期变化

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-13 DOI: 10.1038/s41746-024-01238-x

Jennifer M. Radin, Julia Moore Vogel, Felipe Delgado, Erin Coughlin, Matteo Gadaleta, Jay A. Pandit, Steven R. Steinhubl

引用次数: 0

Results and implications for generative AI in a large introductory biomedical and health informatics course 大型生物医学和健康信息学入门课程中生成式人工智能的结果和影响

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-13 DOI: 10.1038/s41746-024-01251-0

William Hersh, Kate Fultz Hollis

引用次数: 0

A randomized controlled trial investigating experiential virtual reality communication on prudent antibiotic use 一项随机对照试验，调查虚拟现实体验式传播对谨慎使用抗生素的影响

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-12 DOI: 10.1038/s41746-024-01240-3

Adéla Plechatá, Guido Makransky, Robert Böhm

{"title":"A randomized controlled trial investigating experiential virtual reality communication on prudent antibiotic use","authors":"Adéla Plechatá, Guido Makransky, Robert Böhm","doi":"10.1038/s41746-024-01240-3","DOIUrl":"10.1038/s41746-024-01240-3","url":null,"abstract":"Antimicrobial resistance (AMR) is a global health threat. This randomized controlled trial evaluates the impact of experiential virtual reality (VR) versus information provision via VR or leaflet on prudent antibiotic use. A total of 249 (239 analyzed) participants were randomized into three conditions: VR Information + Experience, VR Information, or Leaflet Information. All participants received AMR information, while those in the VR Information + Experience condition additionally engaged in a game, making treatment decisions for their virtual avatar’s infection. Participants in the VR Information + Experience condition showed a significant increase in prudent use intentions from baseline (d = 1.48). This increase was significantly larger compared to the VR Information (d = 0.50) and Leaflet Information (d = 0.79) conditions. The increase in intentions from baseline remained significant at follow-up in the VR Information + Experience condition (d = 1.25). Experiential VR communication shows promise for promoting prudent antibiotics use.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01240-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170392","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

Ethical debates amidst flawed healthcare artificial intelligence metrics 医疗人工智能指标缺陷中的伦理争论

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-11 DOI: 10.1038/s41746-024-01242-1

Jack Gallifant, Danielle S. Bitterman, Leo Anthony Celi, Judy W. Gichoya, Joao Matos, Liam G. McCoy, Robin L. Pierce

引用次数: 0

Understanding activity and physiology at scale: The Apple Heart & Movement Study 大规模了解活动和生理学苹果心脏与运动研究

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-10 DOI: 10.1038/s41746-024-01187-5

James Truslow, Angela Spillane, Huiming Lin, Katherine Cyr, Adeeti Ullal, Edith Arnold, Ron Huang, Laura Rhodes, Jennifer Block, Jamie Stark, James Kretlow, Alexis L. Beatty, Andreas Werdich, Deepali Bankar, Matt Bianchi, Ian Shapiro, Jaime Villalpando, Sharon Ravindran, Irida Mance, Adam Phillips, John Earl, Rahul C. Deo, Sumbul A. Desai, Calum A. MacRae

引用次数: 0

Adherence to non-pharmaceutical interventions following COVID-19 vaccination: a federated cohort study 接种 COVID-19 疫苗后坚持非药物干预措施：联合队列研究

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-10 DOI: 10.1038/s41746-024-01223-4

Benjamin Rader, Neil K. R. Sehgal, Julie Michelman, Stefan Mellem, Marinanicole D. Schultheiss, Tom Hoddes, Jamie MacFarlane, Geoff Clark, Shawn O’Banion, Paul Eastham, Gaurav Tuli, James A. Taylor, John S. Brownstein

{"title":"Adherence to non-pharmaceutical interventions following COVID-19 vaccination: a federated cohort study","authors":"Benjamin Rader, Neil K. R. Sehgal, Julie Michelman, Stefan Mellem, Marinanicole D. Schultheiss, Tom Hoddes, Jamie MacFarlane, Geoff Clark, Shawn O’Banion, Paul Eastham, Gaurav Tuli, James A. Taylor, John S. Brownstein","doi":"10.1038/s41746-024-01223-4","DOIUrl":"10.1038/s41746-024-01223-4","url":null,"abstract":"In pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities. To address this challenge and demonstrate a framework for conducting observational research with sensitive data in a privacy-conscious manner, we employ a privacy-centric epidemiological study design: the federated cohort. This approach leverages recent computational advances to allow for distributed participants to contribute to a prospective, observational research study while maintaining full control of their data. We apply this strategy here to explore pandemic intervention adherence patterns. Participants (n = 3808) were enrolled in our federated cohort via the “Google Health Studies” mobile application. Participants completed weekly surveys and contributed empirically measured mobility data from their Android devices between November 2020 to August 2021. Using federated analytics, differential privacy, and secure aggregation, we analyzed data in five 6-week periods, encompassing the pre- and post-vaccination phases. Our results showed that participants largely utilized non-pharmaceutical intervention strategies until they were fully vaccinated against COVID-19, except for individuals without plans to become vaccinated. Furthermore, this project offers a blueprint for conducting a federated cohort study and engaging in privacy-preserving research during a public health emergency.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01223-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160355","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

Author Correction: Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling 作者更正：利用基于变压器的序列建模技术将纵向医学成像技术用于眼科疾病的预后分析

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-09 DOI: 10.1038/s41746-024-01243-0

Gregory Holste, Mingquan Lin, Ruiwen Zhou, Fei Wang, Lei Liu, Qi Yan, Sarah H. Van Tassel, Kyle Kovacs, Emily Y. Chew, Zhiyong Lu, Zhangyang Wang, Yifan Peng

引用次数: 0

Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises 预测心理健康危机的机器学习算法的跨大西洋可转移性和可复制性

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-09 DOI: 10.1038/s41746-024-01203-8

João Guerreiro, Roger Garriga, Toni Lozano Bagén, Brihat Sharma, Niranjan S. Karnik, Aleksandar Matić

{"title":"Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises","authors":"João Guerreiro, Roger Garriga, Toni Lozano Bagén, Brihat Sharma, Niranjan S. Karnik, Aleksandar Matić","doi":"10.1038/s41746-024-01203-8","DOIUrl":"10.1038/s41746-024-01203-8","url":null,"abstract":"Transferring and replicating predictive algorithms across healthcare systems constitutes a unique yet crucial challenge that needs to be addressed to enable the widespread adoption of machine learning in healthcare. In this study, we explored the impact of important differences across healthcare systems and the associated Electronic Health Records (EHRs) on machine-learning algorithms to predict mental health crises, up to 28 days in advance. We evaluated both the transferability and replicability of such machine learning models, and for this purpose, we trained six models using features and methods developed on EHR data from the Birmingham and Solihull Mental Health NHS Foundation Trust in the UK. These machine learning models were then used to predict the mental health crises of 2907 patients seen at the Rush University System for Health in the US between 2018 and 2020. The best one was trained on a combination of US-specific structured features and frequency features from anonymized patient notes and achieved an AUROC of 0.837. A model with comparable performance, originally trained using UK structured data, was transferred and then tuned using US data, achieving an AUROC of 0.826. Our findings establish the feasibility of transferring and replicating machine learning models to predict mental health crises across diverse hospital systems.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01203-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158948","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

Closing the gap between open source and commercial large language models for medical evidence summarization 缩小开源和商业大型语言模型在医学证据摘要方面的差距

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-09 DOI: 10.1038/s41746-024-01239-w

Gongbo Zhang, Qiao Jin, Yiliang Zhou, Song Wang, Betina Idnay, Yiming Luo, Elizabeth Park, Jordan G. Nestor, Matthew E. Spotnitz, Ali Soroush, Thomas R. Campion Jr., Zhiyong Lu, Chunhua Weng, Yifan Peng

{"title":"Closing the gap between open source and commercial large language models for medical evidence summarization","authors":"Gongbo Zhang, Qiao Jin, Yiliang Zhou, Song Wang, Betina Idnay, Yiming Luo, Elizabeth Park, Jordan G. Nestor, Matthew E. Spotnitz, Ali Soroush, Thomas R. Campion Jr., Zhiyong Lu, Chunhua Weng, Yifan Peng","doi":"10.1038/s41746-024-01239-w","DOIUrl":"10.1038/s41746-024-01239-w","url":null,"abstract":"Large language models (LLMs) hold great promise in summarizing medical evidence. Most recent studies focus on the application of proprietary LLMs. Using proprietary LLMs introduces multiple risk factors, including a lack of transparency and vendor dependency. While open-source LLMs allow better transparency and customization, their performance falls short compared to the proprietary ones. In this study, we investigated to what extent fine-tuning open-source LLMs can further improve their performance. Utilizing a benchmark dataset, MedReview, consisting of 8161 pairs of systematic reviews and summaries, we fine-tuned three broadly-used, open-sourced LLMs, namely PRIMERA, LongT5, and Llama-2. Overall, the performance of open-source models was all improved after fine-tuning. The performance of fine-tuned LongT5 is close to GPT-3.5 with zero-shot settings. Furthermore, smaller fine-tuned models sometimes even demonstrated superior performance compared to larger zero-shot models. The above trends of improvement were manifested in both a human evaluation and a larger-scale GPT4-simulated evaluation.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01239-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160323","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

MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images MyThisYourThat 用于可解释地识别生物医学图像联合学习中的系统性偏差

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-07 DOI: 10.1038/s41746-024-01226-1

Klavdiia Naumova, Arnout Devos, Sai Praneeth Karimireddy, Martin Jaggi, Mary-Anne Hartley

{"title":"MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images","authors":"Klavdiia Naumova, Arnout Devos, Sai Praneeth Karimireddy, Martin Jaggi, Mary-Anne Hartley","doi":"10.1038/s41746-024-01226-1","DOIUrl":"10.1038/s41746-024-01226-1","url":null,"abstract":"Distributed collaborative learning is a promising approach for building predictive models for privacy-sensitive biomedical images. Here, several data owners (clients) train a joint model without sharing their original data. However, concealed systematic biases can compromise model performance and fairness. This study presents MyThisYourThat (MyTH) approach, which adapts an interpretable prototypical part learning network to a distributed setting, enabling each client to visualize feature differences learned by others on their own image: comparing one client’s ''This’ with others’ ''That’. Our setting demonstrates four clients collaboratively training two diagnostic classifiers on a benchmark X-ray dataset. Without data bias, the global model reaches 74.14% balanced accuracy for cardiomegaly and 74.08% for pleural effusion. We show that with systematic visual bias in one client, the performance of global models drops to near-random. We demonstrate how differences between local and global prototypes reveal biases and allow their visualization on each client’s data without compromising privacy.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01226-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143891","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