NPJ Digital Medicine最新文献_第10页

Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-17 DOI: 10.1038/s41746-025-01465-w

Zhiqiang Huo, John Booth, Thomas Monks, Philip Knight, Liam Watson, Mark Peters, Christina Pagel, Padmanabhan Ramnarayan, Kezhi Li

{"title":"Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI","authors":"Zhiqiang Huo, John Booth, Thomas Monks, Philip Knight, Liam Watson, Mark Peters, Christina Pagel, Padmanabhan Ramnarayan, Kezhi Li","doi":"10.1038/s41746-025-01465-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01465-w","url":null,"abstract":"Critically ill children who require inter-hospital transfers to paediatric intensive care units are sicker than other admissions and have higher mortality rates. Current transport practice primarily relies on early clinical assessments within the initial hours of transport. Real-time mortality risk during transport is lacking due to the absence of data-driven assessment tools. Addressing this gap, our research introduces the PROMPT (Patient-centred Real-time Outcome monitoring and Mortality PredicTion), an explainable end-to-end machine learning pipeline to forecast 30-day mortality risks. The PROMPT integrates continuous time-series vital signs and medical records with episode-specific transport data to provide real-time mortality prediction. The results demonstrated that with PROMPT, both the random forest and logistic regression models achieved the best performance with AUROC 0.83 (95% CI: 0.79–0.86) and 0.81 (95% CI: 0.76–0.85), respectively. The proposed model has demonstrated proof-of-principle in predicting mortality risk in transported children and providing individual-level model interpretability during inter-hospital transports.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"85 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143435204","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

Smartphone administered pulsed radio frequency energy therapy for expedited cutaneous wound healing

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-15 DOI: 10.1038/s41746-025-01462-z

Mengxia Yu, Hongjia Yang, Haoteng Ye, Shuhuang Lin, Yujie Lu, Haoqiang Deng, Lulu Xu, Yongxin Guo, John S. Ho, Terry Tao Ye

{"title":"Smartphone administered pulsed radio frequency energy therapy for expedited cutaneous wound healing","authors":"Mengxia Yu, Hongjia Yang, Haoteng Ye, Shuhuang Lin, Yujie Lu, Haoqiang Deng, Lulu Xu, Yongxin Guo, John S. Ho, Terry Tao Ye","doi":"10.1038/s41746-025-01462-z","DOIUrl":"https://doi.org/10.1038/s41746-025-01462-z","url":null,"abstract":"Pulsed radio frequency energy (PRFE) therapy is a non-invasive, electromagnetic field-based treatment modality successfully used in clinical applications. However, conventional PRFE devices are often bulky, expensive, and require extended treatment durations, limiting patient adherence and efficacy. Here, we present a lightweight, cost-effective wearable PRFE system consisting of a flexible electronic bandage and a smartphone. The bandage, mainly composed of an NFC Frequency Doubler (NFD) and a Radiofrequency Energy Radiator (RER), is powered and administered by the smartphone to generate 27.12 MHz radio wave pulses, for simplified, smartphone-enabled PRFE therapy. Its ultra-flexible, battery-free design supports personalized wound care at a low-cost (<US$1). Both electromagnetic field simulation and measurement demonstrated that the proposed PRFE bandage achieves the field strength of clinical-grade PRFE equipment. In rat full-thickness wound models, PRFE therapy improved wound closure rates by ~20%, with enhanced re-epithelialization and angiogenesis compared to controls.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"15 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417304","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

Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-15 DOI: 10.1038/s41746-025-01499-0

Raissa Souza, Emma A. M. Stanley, Anthony J. Winder, Chris Kang, Kimberly Amador, Erik Y. Ohara, Gabrielle Dagasso, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert

{"title":"Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis","authors":"Raissa Souza, Emma A. M. Stanley, Anthony J. Winder, Chris Kang, Kimberly Amador, Erik Y. Ohara, Gabrielle Dagasso, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert","doi":"10.1038/s41746-025-01499-0","DOIUrl":"https://doi.org/10.1038/s41746-025-01499-0","url":null,"abstract":"Distributed learning enables collaborative machine learning model training without requiring cross-institutional data sharing, thereby addressing privacy concerns. However, local quality control variability can negatively impact model performance while systematic human visual inspection is time-consuming and may violate the goal of keeping data inaccessible outside acquisition centers. This work proposes a novel self-supervised method to identify and eliminate harmful data during distributed learning model training fully-automatically. Harmful data is defined as samples that, when included in training, increase misdiagnosis rates. The method was tested using neuroimaging data from 83 centers for Parkinson’s disease classification with simulated inclusion of a few harmful data samples. The proposed method reliably identified harmful images, with centers providing only harmful datasets being easier to identify than single harmful images within otherwise good datasets. While only evaluated using neuroimaging data, the presented method is application-agnostic and presents a step towards automated quality control in distributed learning.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"79 6 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417273","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

Identifying major depressive disorder in older adults through naturalistic driving behaviors and machine learning

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-15 DOI: 10.1038/s41746-025-01500-w

Chen Chen, David C. Brown, Noor Al-Hammadi, Sayeh Bayat, Anne Dickerson, Brenda Vrkljan, Matthew Blake, Yiqi Zhu, Jean-Francois Trani, Eric J. Lenze, David B. Carr, Ganesh M. Babulal

{"title":"Identifying major depressive disorder in older adults through naturalistic driving behaviors and machine learning","authors":"Chen Chen, David C. Brown, Noor Al-Hammadi, Sayeh Bayat, Anne Dickerson, Brenda Vrkljan, Matthew Blake, Yiqi Zhu, Jean-Francois Trani, Eric J. Lenze, David B. Carr, Ganesh M. Babulal","doi":"10.1038/s41746-025-01500-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01500-w","url":null,"abstract":"Depression in older adults is often underdiagnosed and has been linked to adverse outcomes, including motor vehicle crashes. With a growing population of older drivers in the United States, innovations in screening methods are needed to identify older adults at greatest risk of decline. This study used machine learning techniques to analyze real-world naturalistic driving data to identify depression status in older adults and examined whether specific demographics and medications improved model performance. We analyzed two years of GPS data from 157 older adults, including 81 with major depressive disorder, using XGBoost and logistic regression models. The top-performing model achieved an area under the curve of 0.86 with driving features combined with total medication use. These findings suggest that naturalistic driving data holds high potential as a functional digital neurobehavioral marker for AI identifying depression in older adults on a national scale, thereby ensuring equitable access to treatment.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"9 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417301","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

Self supervised artificial intelligence predicts poor outcome from primary cutaneous squamous cell carcinoma at diagnosis.

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2025-02-15 DOI: 10.1038/s41746-025-01496-3

Nicolas Coudray, Michelle C Juarez, Maressa C Criscito, Adalberto Claudio Quiros, Reason Wilken, Stephanie R Jackson Cullison, Mary L Stevenson, Nicole A Doudican, Ke Yuan, Jamie D Aquino, Daniel M Klufas, Jeffrey P North, Siegrid S Yu, Fadi Murad, Emily Ruiz, Chrysalyne D Schmults, Cristian D Cardona Machado, Javier Cañueto, Anirudh Choudhary, Alysia N Hughes, Alyssa Stockard, Zachary Leibovit-Reiben, Aaron R Mangold, Aristotelis Tsirigos, John A Carucci

{"title":"Self supervised artificial intelligence predicts poor outcome from primary cutaneous squamous cell carcinoma at diagnosis.","authors":"Nicolas Coudray, Michelle C Juarez, Maressa C Criscito, Adalberto Claudio Quiros, Reason Wilken, Stephanie R Jackson Cullison, Mary L Stevenson, Nicole A Doudican, Ke Yuan, Jamie D Aquino, Daniel M Klufas, Jeffrey P North, Siegrid S Yu, Fadi Murad, Emily Ruiz, Chrysalyne D Schmults, Cristian D Cardona Machado, Javier Cañueto, Anirudh Choudhary, Alysia N Hughes, Alyssa Stockard, Zachary Leibovit-Reiben, Aaron R Mangold, Aristotelis Tsirigos, John A Carucci","doi":"10.1038/s41746-025-01496-3","DOIUrl":"10.1038/s41746-025-01496-3","url":null,"abstract":"Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of 0.73 in the development cohort and 0.84 in the Mayo cohort. The model's interpretability revealed that features like poor differentiation and deep invasion were strongly associated with poor prognosis. Furthermore, the model is effective in stratifying risk among BWH T2a and AJCC T2, known for outcome heterogeneity.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"8 1","pages":"105"},"PeriodicalIF":12.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11830021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425871","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

Improving medical machine learning models with generative balancing for equity and excellence

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-14 DOI: 10.1038/s41746-025-01438-z

Brandon Theodorou, Benjamin Danek, Venkat Tummala, Shivam Pankaj Kumar, Bradley Malin, Jimeng Sun

{"title":"Improving medical machine learning models with generative balancing for equity and excellence","authors":"Brandon Theodorou, Benjamin Danek, Venkat Tummala, Shivam Pankaj Kumar, Bradley Malin, Jimeng Sun","doi":"10.1038/s41746-025-01438-z","DOIUrl":"https://doi.org/10.1038/s41746-025-01438-z","url":null,"abstract":"Applying machine learning to clinical outcome prediction is challenging due to imbalanced datasets and sensitive tasks that contain rare yet critical outcomes and where equitable treatment across diverse patient groups is essential. Despite attempts, biases in predictions persist, driven by disparities in representation and exacerbated by the scarcity of positive labels, perpetuating health inequities. This paper introduces FairPlay, a synthetic data generation approach leveraging large language models, to address these issues. FairPlay enhances algorithmic performance and reduces bias by creating realistic, anonymous synthetic patient data that improves representation and augments dataset patterns while preserving privacy. Through experiments on multiple datasets, we demonstrate that FairPlay boosts mortality prediction performance across diverse subgroups, achieving up to a 21% improvement in F1 Score without requiring additional data or altering downstream training pipelines. Furthermore, FairPlay consistently reduces subgroup performance gaps, as shown by universal improvements in performance and fairness metrics across four experimental setups.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"11 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417302","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 large language models for academic conference organization

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-14 DOI: 10.1038/s41746-025-01492-7

Yuan Luo, Yikuan Li, Omolola Ogunyemi, Eileen Koski, Blanca E. Himes

引用次数: 0

Improving musculoskeletal care with AI enhanced triage through data driven screening of referral letters.

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2025-02-14 DOI: 10.1038/s41746-025-01495-4

Tjardo Daniël Maarseveen, Herman Kasper Glas, Josien Veris-van Dieren, Erik van den Akker, Rachel Knevel

{"title":"Improving musculoskeletal care with AI enhanced triage through data driven screening of referral letters.","authors":"Tjardo Daniël Maarseveen, Herman Kasper Glas, Josien Veris-van Dieren, Erik van den Akker, Rachel Knevel","doi":"10.1038/s41746-025-01495-4","DOIUrl":"10.1038/s41746-025-01495-4","url":null,"abstract":"Musculoskeletal complaints account for 30% of GP consultations, with many referred to rheumatology clinics via letters. This study developed a Machine Learning (ML) pipeline to prioritize referrals by identifying rheumatoid arthritis (RA), osteoarthritis, fibromyalgia, and patients requiring long-term care. Using 8044 referral letters from 5728 patients across 12 clinics, we trained and validated ML models in two large centers and tested their generalizability in the remaining ten. The models were robust, with RA achieving an AUC-ROC of 0.78 (CI: 0.74-0.83), osteoarthritis 0.71 (CI: 0.67-0.74), fibromyalgia 0.81 (CI: 0.77-0.85), and chronic follow-up 0.63 (CI: 0.61-0.66). The RA-classifier outperformed manual referral systems, as it prioritised RA over non-RA cases (P < 0.001), while the manual referral system could not differentiate between the two. The other classifiers showed similar prioritisation improvements, highlighting the potential to enhance care efficiency, reduce clinician workload, and facilitate earlier specialized care. Future work will focus on building clinical decision-support tools.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"8 1","pages":"98"},"PeriodicalIF":12.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11825706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414792","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

Personalized decision making for coronary artery disease treatment using offline reinforcement learning.

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2025-02-14 DOI: 10.1038/s41746-025-01498-1

Peyman Ghasemi, Matthew Greenberg, Danielle A Southern, Bing Li, James A White, Joon Lee

{"title":"Personalized decision making for coronary artery disease treatment using offline reinforcement learning.","authors":"Peyman Ghasemi, Matthew Greenberg, Danielle A Southern, Bing Li, James A White, Joon Lee","doi":"10.1038/s41746-025-01498-1","DOIUrl":"10.1038/s41746-025-01498-1","url":null,"abstract":"Choosing optimal revascularization strategies for patients with obstructive coronary artery disease (CAD) remains a clinical challenge. While randomized controlled trials offer population-level insights, gaps remain regarding personalized decision-making for individual patients. We applied off-policy reinforcement learning (RL) to a composite data model from 41,328 unique patients with angiography-confirmed obstructive CAD. In an offline setting, we estimated optimal treatment policies and evaluated these policies using weighted importance sampling. Our findings indicate that RL-guided therapy decisions outperformed physician-based decision making, with RL policies achieving up to 32% improvement in expected rewards based on composite major cardiovascular events outcomes. Additionally, we introduced methods to ensure that RL CAD treatment policies remain compatible with locally achievable clinical practice models, presenting an interpretable RL policy with a limited number of states. Overall, this novel RL-based clinical decision support tool, RL4CAD, demonstrates potential to optimize care in patients with obstructive CAD referred for invasive coronary angiography.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"8 1","pages":"99"},"PeriodicalIF":12.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11825836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414850","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

Ecosystems and monopolies in digital surgery

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-02-12 DOI: 10.1038/s41746-024-01379-z

Andrew Yiu, Kapil Sahnan

引用次数: 0