NPJ Digital Medicine最新文献_第10页

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":" ","pages":"1-10"},"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":" ","pages":"1-8"},"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":" ","pages":"1-10"},"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":" ","pages":"1-16"},"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":" ","pages":"1-16"},"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":" ","pages":"1-10"},"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

Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension 用于检测颅内高压的深度学习方法的衍生、外部和临床验证

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-05 DOI: 10.1038/s41746-024-01227-0

Faris Gulamali, Pushkala Jayaraman, Ashwin S. Sawant, Jacob Desman, Benjamin Fox, Annette Chang, Brian Y. Soong, Naveen Arivazagan, Alexandra S. Reynolds, Son Q. Duong, Akhil Vaid, Patricia Kovatch, Robert Freeman, Ira S. Hofer, Ankit Sakhuja, Neha S. Dangayach, David S. Reich, Alexander W. Charney, Girish N. Nadkarni

{"title":"Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension","authors":"Faris Gulamali, Pushkala Jayaraman, Ashwin S. Sawant, Jacob Desman, Benjamin Fox, Annette Chang, Brian Y. Soong, Naveen Arivazagan, Alexandra S. Reynolds, Son Q. Duong, Akhil Vaid, Patricia Kovatch, Robert Freeman, Ira S. Hofer, Ankit Sakhuja, Neha S. Dangayach, David S. Reich, Alexander W. Charney, Girish N. Nadkarni","doi":"10.1038/s41746-024-01227-0","DOIUrl":"10.1038/s41746-024-01227-0","url":null,"abstract":"Increased intracranial pressure (ICP) ≥15 mmHg is associated with adverse neurological outcomes, but needs invasive intracranial monitoring. Using the publicly available MIMIC-III Waveform Database (2000–2013) from Boston, we developed an artificial intelligence-derived biomarker for elevated ICP (aICP) for adult patients. aICP uses routinely collected extracranial waveform data as input, reducing the need for invasive monitoring. We externally validated aICP with an independent dataset from the Mount Sinai Hospital (2020–2022) in New York City. The AUROC, accuracy, sensitivity, and specificity on the external validation dataset were 0.80 (95% CI, 0.80–0.80), 73.8% (95% CI, 72.0–75.6%), 73.5% (95% CI 72.5–74.5%), and 73.0% (95% CI, 72.0–74.0%), respectively. We also present an exploratory analysis showing aICP predictions are associated with clinical phenotypes. A ten-percentile increment was associated with brain malignancy (OR = 1.68; 95% CI, 1.09-2.60), intracerebral hemorrhage (OR = 1.18; 95% CI, 1.07–1.32), and craniotomy (OR = 1.43; 95% CI, 1.12–1.84; P < 0.05 for all).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-7"},"PeriodicalIF":12.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01227-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137901","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

Regulatory considerations for developing remote measurement technologies for Alzheimer’s disease research 为阿尔茨海默病研究开发远程测量技术的监管考虑因素

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-04 DOI: 10.1038/s41746-024-01211-8

Gül Erdemli, Margarita Grammatikopoulou, Bertil Wagner, Srinivasan Vairavan, Jelena Curcic, Dag Aarsland, Gayle Wittenberg, Spiros Nikolopoulos, Marijn Muurling, Holger Froehlich, Casper de Boer, Niraj M. Shanbhag, Vera J. M. Nies, Neva Coello, Dianne Gove, Ana Diaz, Suzanne Foy, Wim Dartee, Anna-Katharine Brem

引用次数: 0