NPJ Digital Medicine最新文献_第3页

An umbrella review of efficacy of digital health interventions for workers 对数字健康干预措施对工人的有效性进行全面审查

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-14 DOI: 10.1038/s41746-025-01578-2

Masahiro Iwakura, Chihiro Ozeki, Songee Jung, Teiichiro Yamazaki, Takako Miki, Michiko Nohara, Kyoko Nomura

{"title":"An umbrella review of efficacy of digital health interventions for workers","authors":"Masahiro Iwakura, Chihiro Ozeki, Songee Jung, Teiichiro Yamazaki, Takako Miki, Michiko Nohara, Kyoko Nomura","doi":"10.1038/s41746-025-01578-2","DOIUrl":"https://doi.org/10.1038/s41746-025-01578-2","url":null,"abstract":"Efficacy of digital health (d-Health) interventions on workers’ physical activity (PA), sedentary behavior, and physiological outcomes remains unclear. This umbrella review searched PubMed, Cochrane Library, and Google Scholar up to October 25, 2024. We identified 24 systematic reviews (SRs) and selected 130 individual studies from these SRs for analysis. The AMSTAR 2 tool rated the quality of most SRs as critically low. Narrative syntheses suggested that d-Health interventions could potentially improve all outcomes compared with no intervention. However, whether d-Health interventions outperform non-d-Health interventions remains uncertain. Meta-analyses showed a significantly small effect of d-Health interventions on step counts, sedentary/sitting time, and weight compared with no intervention, while d-Health interventions slightly improved only moderate-to-vigorous PA compared with non-d-Health interventions. Subgroup analyses identified potential sources of heterogeneity (e.g., risk of bias, control conditions), which may vary between outcomes. Further high-quality studies are needed to evaluate the efficacy of d-Health interventions.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"1 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143827655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging pretrained language models for seizure frequency extraction from epilepsy evaluation reports 利用预训练语言模型从癫痫评估报告中提取癫痫发作频率

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-14 DOI: 10.1038/s41746-025-01592-4

Rashmie Abeysinghe, Shiqiang Tao, Samden D. Lhatoo, Guo-Qiang Zhang, Licong Cui

{"title":"Leveraging pretrained language models for seizure frequency extraction from epilepsy evaluation reports","authors":"Rashmie Abeysinghe, Shiqiang Tao, Samden D. Lhatoo, Guo-Qiang Zhang, Licong Cui","doi":"10.1038/s41746-025-01592-4","DOIUrl":"https://doi.org/10.1038/s41746-025-01592-4","url":null,"abstract":"Seizure frequency is essential for evaluating epilepsy treatment, ensuring patient safety, and reducing risk for Sudden Unexpected Death in Epilepsy. As this information is often described in clinical narratives, this study presents an approach to extracting structured seizure frequency details from such unstructured text. We investigated two tasks: (1) extracting phrases describing seizure frequency, and (2) extracting seizure frequency attributes. For both tasks, we fine-tuned three BERT-based models (bert-large-cased, biobert-large-cased, and Bio_ClinicalBERT), as well as three generative large language models (GPT-4, GPT-3.5 Turbo, and Llama-2-70b-hf). The final structured output integrated the results from both tasks. GPT-4 attained the best performance across all tasks with precision, recall, and F1-score of 86.61%, 85.04%, and 85.79% respectively for frequency phrase extraction; 90.23%, 93.51%, and 91.84% for seizure frequency attribute extraction; and 86.64%, 85.06%, and 85.82% for the final structured output. These findings highlight the potential of fine-tuned generative models in extractive tasks from limited text strings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"23 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143827692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fine-grained forecasting of COVID-19 trends at the county level in the United States 美国县级COVID-19趋势的细粒度预测

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-11 DOI: 10.1038/s41746-025-01606-1

Tzu-Hsi Song, Leonardo Clemente, Xiang Pan, Junbong Jang, Mauricio Santillana, Kwonmoo Lee

{"title":"Fine-grained forecasting of COVID-19 trends at the county level in the United States","authors":"Tzu-Hsi Song, Leonardo Clemente, Xiang Pan, Junbong Jang, Mauricio Santillana, Kwonmoo Lee","doi":"10.1038/s41746-025-01606-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01606-1","url":null,"abstract":"The novel coronavirus (COVID-19) pandemic has had a devastating global impact, profoundly affecting daily life, healthcare systems, and public health infrastructure. Despite the availability of treatments and vaccines, hospitalizations and deaths continue. Real-time surveillance of infection trends supports resource allocation and mitigation strategies, but reliable forecasting remains a challenge. While deep learning has advanced time-series forecasting, its effectiveness relies on large datasets, a significant obstacle given the pandemic’s evolving nature. Most models use national or state-level data, limiting both dataset size and the granularity of insights. To address this, we propose the Fine-Grained Infection Forecast Network (FIGI-Net), a stacked bidirectional LSTM structure designed to leverage county-level data to produce daily forecasts up to two weeks in advance. FIGI-Net outperforms existing models, accurately predicting sudden changes such as new outbreaks or peaks, a capability many state-of-the-art models lack. This approach could enhance public health responses and outbreak preparedness.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"107 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of intracranial pressure epochs using a novel machine learning framework 使用新的机器学习框架对颅内压时代进行分类

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-11 DOI: 10.1038/s41746-025-01612-3

Rohan Mathur, Sudha Yellapantula, Lin Cheng, Peter Dziedzic, Niteesh Potu, Eusebia Calvillo, Vishank Shah, Austen Lefebvre, Julian Bosel, Elizabeth K. Zink, Susanne Muehlschlegel, Jose I. Suarez

{"title":"Classification of intracranial pressure epochs using a novel machine learning framework","authors":"Rohan Mathur, Sudha Yellapantula, Lin Cheng, Peter Dziedzic, Niteesh Potu, Eusebia Calvillo, Vishank Shah, Austen Lefebvre, Julian Bosel, Elizabeth K. Zink, Susanne Muehlschlegel, Jose I. Suarez","doi":"10.1038/s41746-025-01612-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01612-3","url":null,"abstract":"Patients with acute brain injuries are at risk for life threatening elevated intracranial pressure (ICP). External Ventricular Drains (EVDs) are used to measure and treat ICP, which switch between clamped and draining configurations, with accurate ICP data only available during clamped periods. While traditional guidelines focus on mean ICP values, evolving evidence indicates other waveform features may hold prognostic value. However, current machine learning models using ICP waveforms exclude EVD data due to a lack of digital labels indicating the clamped state, markedly limiting their generalizability. We introduce, detail, and validate CICL (Classification of ICP epochs using a machine Learning framework), a semi-supervised approach to classify ICP segments from EVDs as clamped, draining, or noise. This paves the way for multiple applications, including generalizable ICP crisis prediction, potentially benefiting tens of thousands of patients annually and highlights an innovate methodology to label large high frequency physiological time series datasets.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"104 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection 纵向语音监测在一个分散的带你自己的设备试验呼吸系统疾病检测

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-11 DOI: 10.1038/s41746-025-01584-4

Mar Santamaria, Yiorgos Christakis, Charmaine Demanuele, Yao Zhang, Pirinka Georgiev Tuttle, Fahimeh Mamashli, Jiawei Bai, Rogier Landman, Kara Chappie, Stefan Kell, John G. Samuelsson, Kisha Talbert, Leonardo Seoane, W. Mark Roberts, Edmond Kato Kabagambe, Joseph Capelouto, Paul Wacnik, Jessica Selig, Lukas Adamowicz, Sheraz Khan, Robert J. Mather

{"title":"Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection","authors":"Mar Santamaria, Yiorgos Christakis, Charmaine Demanuele, Yao Zhang, Pirinka Georgiev Tuttle, Fahimeh Mamashli, Jiawei Bai, Rogier Landman, Kara Chappie, Stefan Kell, John G. Samuelsson, Kisha Talbert, Leonardo Seoane, W. Mark Roberts, Edmond Kato Kabagambe, Joseph Capelouto, Paul Wacnik, Jessica Selig, Lukas Adamowicz, Sheraz Khan, Robert J. Mather","doi":"10.1038/s41746-025-01584-4","DOIUrl":"https://doi.org/10.1038/s41746-025-01584-4","url":null,"abstract":"The Acute Respiratory Illness Surveillance (AcRIS) Study was a low-interventional trial that examined voice changes with respiratory illnesses. This longitudinal trial was the first of its kind, conducted in a fully decentralized manner via a Bring Your Own Device mobile application. The app enabled social-media-based recruitment, remote consent, at-home sample collection, and daily remote voice and symptom capture in real-world settings. From April 2021 to April 2022, the trial enrolled 9151 participants, followed for up to eight weeks. Despite mild symptoms experienced by reverse transcription polymerase chain reaction (RT-PCR) positive participants, two machine learning algorithms developed to screen respiratory illnesses reached the pre-specified success criteria. Algorithm testing on independent cohorts demonstrated that the algorithm’s sensitivity increased as symptoms increased, while specificity remained consistent. Study findings suggest voice features can identify individuals with viral respiratory illnesses and provide valuable insights into fully decentralized clinical trials design, operation, and adoption (study registered at ClinicalTrials.gov (NCT04748445) on 5 February 2021).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"95 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continuous sleep depth index annotation with deep learning yields novel digital biomarkers for sleep health 持续睡眠深度指数注释与深度学习产生新的数字生物标志物睡眠健康

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-11 DOI: 10.1038/s41746-025-01607-0

Songchi Zhou, Ge Song, Haoqi Sun, Deyun Zhang, Yue Leng, M. Brandon Westover, Shenda Hong

引用次数: 0

Vision transformer based interpretable metabolic syndrome classification using retinal Images 基于视网膜图像的视觉变形可解释代谢综合征分类

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-11 DOI: 10.1038/s41746-025-01588-0

Tae Kwan Lee, So Yeon Kim, Hyuk Jin Choi, Eun Kyung Choe, Kyung-Ah Sohn

引用次数: 0

Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension 用于肺动脉高压检测的多模态深度学习算法的开发和验证

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-10 DOI: 10.1038/s41746-025-01593-3

Wei Zhao, Zhihua Huang, Xiaolin Diao, Zhan Yang, Zhihui Zhao, Yun Xia, Qing Zhao, Zhaohong Sun, Qunying Xi, Yanni Huo, Ou Xu, Jiahui Geng, Xin Li, Anqi Duan, Sicheng Zhang, Luyang Gao, Yijia Wang, Sicong Li, Qin Luo, Zhihong Liu

{"title":"Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension","authors":"Wei Zhao, Zhihua Huang, Xiaolin Diao, Zhan Yang, Zhihui Zhao, Yun Xia, Qing Zhao, Zhaohong Sun, Qunying Xi, Yanni Huo, Ou Xu, Jiahui Geng, Xin Li, Anqi Duan, Sicheng Zhang, Luyang Gao, Yijia Wang, Sicong Li, Qin Luo, Zhihong Liu","doi":"10.1038/s41746-025-01593-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01593-3","url":null,"abstract":"Transthoracic echocardiography (TTE), commonly used for initial screening of pulmonary hypertension (PH), often lacks sufficient accuracy. To address this gap, we developed and validated a multimodal fusion model for improved PH screening (MMF-PH). The study was registered in the ClinicalTrials.gov (NCT05566002, 09/30/2022). The MMF-PH underwent extensive training, validation, and testing, including comparisons with TTE and evaluations across various patient subgroups to assess robustness and reliability. We analyzed 2451 patients who underwent right heart catheterization, supplemented by a prospective dataset of 477 patients and an external dataset. The MMF-PH demonstrated robust performance across different datasets. The model outperformed TTE in terms of specificity and negative predictive value across all test datasets. An ablation study using the external test dataset confirmed the essential role of each module in the MMF-PH. The MMF-PH significantly advances PH detection, offering robust and reliable diagnostic accuracy across diverse patient populations and clinical settings.<figure></figure>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"167 4 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Is regulatory science ready for artificial intelligence? 监管科学为人工智能做好准备了吗？

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-10 DOI: 10.1038/s41746-025-01596-0

Thomas Hartung, Maurice Whelan, Weida Tong, Robert M. Califf

引用次数: 0

A group concept mapping study of stakeholder perspectives on digital therapeutics economic value drivers 数字治疗经济价值驱动因素的利益相关者视角的群体概念映射研究

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-10 DOI: 10.1038/s41746-025-01600-7

Yoann Sapanel, L. Martin Cloutier, Gabriel Tremblay, Anh Bourcet, Florian Koerber, David Lariviere, Xavier Tadeo, Dean Ho

{"title":"A group concept mapping study of stakeholder perspectives on digital therapeutics economic value drivers","authors":"Yoann Sapanel, L. Martin Cloutier, Gabriel Tremblay, Anh Bourcet, Florian Koerber, David Lariviere, Xavier Tadeo, Dean Ho","doi":"10.1038/s41746-025-01600-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01600-7","url":null,"abstract":"Digital therapeutics (DTx), software as a medical device, present a promising avenue for addressing the increasing burden of a range of conditions, yet their widespread implementation remains contingent upon demonstrating economic value—an understudied domain in current literature. Using a Group Concept Mapping approach, this study synthesized perspectives from healthcare professionals, researchers, industry, and public sector representatives to understand factors perceived to influence DTx economic value throughout its lifecycle. Analysis revealed 59 factors organized into eight clusters. Stakeholders consistently identified DTx Impact on Patient Outcomes and DTx Implementation as the most influential clusters affecting economic value. However, DTx Associated Costs and DTx Monetization Models clusters were reported as not receiving sufficient consideration throughout the DTx development lifecycle, particularly among researchers. Consequently, a conceptual framework of priority clusters and factors driving DTx economic value is proposed.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"1 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143814038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0