NPJ Digital Medicine最新文献_第9页

Genomic language models could transform medicine but not yet 基因组语言模型可能会改变医学，但目前还不行

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-18 DOI: 10.1038/s41746-025-01603-4

Micaela Elisa Consens, Ben Li, Anna R. Poetsch, Stephen Gilbert

引用次数: 0

Primary care telehealth in a dynamic healthcare environment from digital divide to healthcare outcomes 从数字鸿沟到医疗保健结果的动态医疗保健环境中的初级保健远程保健

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-17 DOI: 10.1038/s41746-025-01599-x

Yunxi Zhang, Lincy S. Lal, Saurabh Chandra, John Michael Swint

引用次数: 0

Artificial intelligence tools in supporting healthcare professionals for tailored patient care 人工智能工具支持医疗保健专业人员进行量身定制的患者护理

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-16 DOI: 10.1038/s41746-025-01604-3

Jiyeong Kim, Michael L. Chen, Shawheen J. Rezaei, Tina Hernandez-Boussard, Jonathan H. Chen, Fatima Rodriguez, Summer S. Han, Rayhan A. Lal, Sun H. Kim, Chrysoula Dosiou, Susan M. Seav, Tugce Akcan, Carolyn I. Rodriguez, Steven M. Asch, Eleni Linos

{"title":"Artificial intelligence tools in supporting healthcare professionals for tailored patient care","authors":"Jiyeong Kim, Michael L. Chen, Shawheen J. Rezaei, Tina Hernandez-Boussard, Jonathan H. Chen, Fatima Rodriguez, Summer S. Han, Rayhan A. Lal, Sun H. Kim, Chrysoula Dosiou, Susan M. Seav, Tugce Akcan, Carolyn I. Rodriguez, Steven M. Asch, Eleni Linos","doi":"10.1038/s41746-025-01604-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01604-3","url":null,"abstract":"Artificial intelligence (AI) tools to support clinicians in providing patient-centered care can contribute to patient empowerment and care efficiency. We aimed to draft potential AI tools for tailored patient support corresponding to patients’ needs and assess clinicians’ perceptions about the usefulness of those AI tools. To define patients’ issues, we analyzed 528,199 patient messages of 11,123 patients with diabetes by harnessing natural language processing and AI. Applying multiple prompt-engineering techniques, we drafted a series of AI tools, and five endocrinologists evaluated them for perceived usefulness and risk. Patient education and administrative support for timely and streamlined interaction were perceived as highly useful, yet deeper integration of AI tools into patient data was perceived as risky. This study proposes assorted AI applications as clinical assistance tailored to patients’ needs substantiated by clinicians’ evaluations. Findings could offer essential ramifications for developing potential AI tools for precision patient care for diabetes and beyond.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"60 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841196","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

Network analysis of extraintestinal manifestations and associated autoimmune disorders in Crohn’s disease and ulcerative colitis 克罗恩病和溃疡性结肠炎肠外表现及相关自身免疫性疾病的网络分析

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-15 DOI: 10.1038/s41746-025-01504-6

Daniel C. Baumgart, C. Hing Cheng, Tian X. Du, Michael D. Parkes, Daniel C. Sadowski, Eytan Wine, Frank Hoentjen, Brendan P. Halloran, Aldo Montano-Loza, Sergio Zepeda-Gomez, Karen Wong, Farhad Peerani, Randolph Goebel, J. Ross Mitchell

{"title":"Network analysis of extraintestinal manifestations and associated autoimmune disorders in Crohn’s disease and ulcerative colitis","authors":"Daniel C. Baumgart, C. Hing Cheng, Tian X. Du, Michael D. Parkes, Daniel C. Sadowski, Eytan Wine, Frank Hoentjen, Brendan P. Halloran, Aldo Montano-Loza, Sergio Zepeda-Gomez, Karen Wong, Farhad Peerani, Randolph Goebel, J. Ross Mitchell","doi":"10.1038/s41746-025-01504-6","DOIUrl":"https://doi.org/10.1038/s41746-025-01504-6","url":null,"abstract":"We detect and interactively visualize occurrence, frequency, sequence, and clustering of extraintestinal manifestations (EIM) and associated immune disorders (AID) in 30,334 inflammatory bowel disease (IBD) patients (Crohn’s disease (CD) n = 15924, ulcerative colitis (UC) n = 11718, IBD unclassified, IBD-U n = 2692, 52% female, median age 40 years (IQR: 25)) with artificial intelligence (AI). 57% (CD > UC 60% vs. 54%, p < 0.00001) had one or more EIM and/or AID. Mental, musculoskeletal and genitourinary disorders were most frequently associated with IBD: 18% (CD vs. UC 19% vs. 16%, p < 0.00001), 17% (CD vs. UC 20% vs. 15%, p < 0.00001) and 11% (CD vs. UC 13% vs. 9%, p < 0.00001), respectively. AI detected 4 vs. 5 vs. 5 distinct EIM/AID communities with 420 vs. 396 vs. 467 nodes and 11,492 vs. 9116 vs. 16,807 edges (links) in CD vs. UC vs. IBD, respectively. Our newly developed interactive free web app shows previously unknown communities, relationships, and temporal patterns—the diseasome and interactome.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"1 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836679","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

Virtual urgent care in an integrated value based healthcare system 基于综合价值的医疗保健系统中的虚拟紧急护理

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-14 DOI: 10.1038/s41746-025-01590-6

Khang Nguyen, Dinh Nguyen, Sinjin Lee, Jin Chang, Yvonne Bach, Kien La, Ferdinand Justus, Tianyuan Shao, Caleb Wang, Mason Kellogg, Raquel Taylor, Alan Evans

引用次数: 0

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