NPJ Digital Medicine最新文献_第8页

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

Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins 为可穿戴式冠状动脉数字双胞胎建立纵向血液动力学绘图框架

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-06 DOI: 10.1038/s41746-024-01216-3

Cyrus Tanade, Nusrat Sadia Khan, Emily Rakestraw, William D. Ladd, Erik W. Draeger, Amanda Randles

{"title":"Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins","authors":"Cyrus Tanade, Nusrat Sadia Khan, Emily Rakestraw, William D. Ladd, Erik W. Draeger, Amanda Randles","doi":"10.1038/s41746-024-01216-3","DOIUrl":"10.1038/s41746-024-01216-3","url":null,"abstract":"Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient’s circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002–0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMFC, where hemodynamically similar heartbeats are clustered to avoid redundant simulations, accurately reconstructing longitudinal hemodynamic maps (LHMs). This study captured 3D hemodynamics over 4.5 million heartbeats, paving the way for cardiovascular digital twins.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01216-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142605","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

Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease 将数字步态数据与代谢组学和临床数据相结合，预测帕金森病的预后

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-06 DOI: 10.1038/s41746-024-01236-z

Cyril Brzenczek, Quentin Klopfenstein, Tom Hähnel, Holger Fröhlich, Enrico Glaab, On behalf of the NCER-PD Consortium

{"title":"Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease","authors":"Cyril Brzenczek, Quentin Klopfenstein, Tom Hähnel, Holger Fröhlich, Enrico Glaab, On behalf of the NCER-PD Consortium","doi":"10.1038/s41746-024-01236-z","DOIUrl":"10.1038/s41746-024-01236-z","url":null,"abstract":"Parkinson’s disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data’s utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01236-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142606","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

Navigating the EU AI Act: implications for regulated digital medical products 驾驭欧盟人工智能法案：对受监管数字医疗产品的影响

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-06 DOI: 10.1038/s41746-024-01232-3

Mateo Aboy, Timo Minssen, Effy Vayena

引用次数: 0

Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation 人工智能估计心电图年龄作为心房颤动导管消融术后的复发预测指标

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-05 DOI: 10.1038/s41746-024-01234-1

Hanjin Park, Oh-Seok Kwon, Jaemin Shim, Daehoon Kim, Je-Wook Park, Yun-Gi Kim, Hee Tae Yu, Tae-Hoon Kim, Jae-Sun Uhm, Jong-Il Choi, Boyoung Joung, Moon-Hyoung Lee, Hui-Nam Pak

{"title":"Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation","authors":"Hanjin Park, Oh-Seok Kwon, Jaemin Shim, Daehoon Kim, Je-Wook Park, Yun-Gi Kim, Hee Tae Yu, Tae-Hoon Kim, Jae-Sun Uhm, Jong-Il Choi, Boyoung Joung, Moon-Hyoung Lee, Hui-Nam Pak","doi":"10.1038/s41746-024-01234-1","DOIUrl":"10.1038/s41746-024-01234-1","url":null,"abstract":"The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01234-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137895","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