NPJ Digital Medicine最新文献

Zero shot health trajectory prediction using transformer

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2024-09-19 DOI: 10.1038/s41746-024-01235-0

Pawel Renc, Yugang Jia, Anthony E. Samir, Jaroslaw Was, Quanzheng Li, David W. Bates, Arkadiusz Sitek

引用次数: 0

Regulatory responses and approval status of artificial intelligence medical devices with a focus on China

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2024-09-18 DOI: 10.1038/s41746-024-01254-x

Yuehua Liu, Wenjin Yu, Tharam Dillon

引用次数: 0

A systematic review and meta analysis on digital mental health interventions in inpatient settings 关于住院环境中数字心理健康干预措施的系统回顾和元分析

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2024-09-17 DOI: 10.1038/s41746-024-01252-z

Alexander Diel, Isabel Carolin Schröter, Anna-Lena Frewer, Christoph Jansen, Anita Robitzsch, Gertraud Gradl-Dietsch, Martin Teufel, Alexander Bäuerle

{"title":"A systematic review and meta analysis on digital mental health interventions in inpatient settings","authors":"Alexander Diel, Isabel Carolin Schröter, Anna-Lena Frewer, Christoph Jansen, Anita Robitzsch, Gertraud Gradl-Dietsch, Martin Teufel, Alexander Bäuerle","doi":"10.1038/s41746-024-01252-z","DOIUrl":"https://doi.org/10.1038/s41746-024-01252-z","url":null,"abstract":"E-mental health (EMH) interventions gain increasing importance in the treatment of mental health disorders. Their outpatient efficacy is well-established. However, research on EMH in inpatient settings remains sparse and lacks a meta-analytic synthesis. This paper presents a meta-analysis on the efficacy of EMH in inpatient settings. Searching multiple databases (PubMed, ScienceGov, PsycInfo, CENTRAL, references), 26 randomized controlled trial (RCT) EMH inpatient studies (n = 6112) with low or medium assessed risk of bias were included. A small significant total effect of EMH treatment was found (g = 0.3). The effect was significant both for blended interventions (g = 0.42) and post-treatment EMH-based aftercare (g = 0.29). EMH treatment yielded significant effects across different patient groups and types of therapy, and the effects remained stable post-treatment. The results show the efficacy of EMH treatment in inpatient settings. The meta-analysis is limited by the small number of included studies.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":15.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235063","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

A drug mix and dose decision algorithm for individualized type 2 diabetes management 个性化 2 型糖尿病管理的药物组合和剂量决策算法

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2024-09-17 DOI: 10.1038/s41746-024-01230-5

Mila Nambiar, Yong Mong Bee, Yu En Chan, Ivan Ho Mien, Feri Guretno, David Carmody, Phong Ching Lee, Sing Yi Chia, Nur Nasyitah Mohamed Salim, Pavitra Krishnaswamy

{"title":"A drug mix and dose decision algorithm for individualized type 2 diabetes management","authors":"Mila Nambiar, Yong Mong Bee, Yu En Chan, Ivan Ho Mien, Feri Guretno, David Carmody, Phong Ching Lee, Sing Yi Chia, Nur Nasyitah Mohamed Salim, Pavitra Krishnaswamy","doi":"10.1038/s41746-024-01230-5","DOIUrl":"https://doi.org/10.1038/s41746-024-01230-5","url":null,"abstract":"Pharmacotherapy guidelines for type 2 diabetes (T2D) emphasize patient-centered care, but applying this approach effectively in outpatient practice remains challenging. Data-driven treatment optimization approaches could enhance individualized T2D management, but current approaches cannot account for drug-specific and dose-dependent variations in safety and efficacy. We developed and evaluated an AI Drug mix and dose Advisor (AIDA) for glycemic management, using electronic medical records from 107,854 T2D patients in the SingHealth Diabetes Registry. Given a patient’s medical profile, AIDA leverages a predict-then-optimize approach to identify the minimal drug mix and dose changes required to optimize glycemic control, subject to clinical knowledge-based guidelines. On unseen data from large internal, external, and temporal validation sets, AIDA recommendations were estimated to improve post-visit glycated hemoglobin (HbA1c) by an average of 0.40–0.68% over standard of care (P < 0.0001). In qualitative evaluations on 60 diverse cases by a panel of three endocrinologists, AIDA recommendations were mostly rated as reasonable and precise. Finally, AIDA’s ability to account for drug-dose specifics offered several advantages over competing methods, including greater consistency with practice preferences and clinical guidelines for practical but effective options, indication-based treatments, and renal dosing. As AIDA provides drug-dose recommendations to improve outcomes for individual T2D patients, it could be used for clinical decision support at point-of-care, especially in resource-limited settings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":15.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235102","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

Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias 深度行为表征学习揭示恶性室性心律失常的风险特征

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2024-09-16 DOI: 10.1038/s41746-024-01247-w

Maarten Z. H. Kolk, Diana My Frodi, Joss Langford, Tariq O. Andersen, Peter Karl Jacobsen, Niels Risum, Hanno L. Tan, Jesper Hastrup Svendsen, Reinoud E. Knops, Søren Zöga Diederichsen, Fleur V. Y. Tjong

{"title":"Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias","authors":"Maarten Z. H. Kolk, Diana My Frodi, Joss Langford, Tariq O. Andersen, Peter Karl Jacobsen, Niels Risum, Hanno L. Tan, Jesper Hastrup Svendsen, Reinoud E. Knops, Søren Zöga Diederichsen, Fleur V. Y. Tjong","doi":"10.1038/s41746-024-01247-w","DOIUrl":"https://doi.org/10.1038/s41746-024-01247-w","url":null,"abstract":"We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (n = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (n = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (n = 63) exhibited a high-intensity activity profile. Cluster D (n = 51) showed above-average sleep efficiency. Cluster E (n = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54–8.53, p < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":15.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234429","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

The case for inclusive co-creation in digital health innovation 数字医疗创新中的包容性共创案例

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2024-09-16 DOI: 10.1038/s41746-024-01256-9

Grace C. Nickel, Serena Wang, Jethro C. C. Kwong, Joseph C. Kvedar

引用次数: 0

How might Hospital at Home enable a greener and healthier future? 居家医院如何实现更环保、更健康的未来？

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2024-09-16 DOI: 10.1038/s41746-024-01249-8

Dylan Powell, Fanny Burrows, Geraint Lewis, Stephen Gilbert

引用次数: 0

A randomized trial testing digital medicine support models for mild-to-moderate alcohol use disorder 测试轻度至中度酒精使用障碍数字医学支持模式的随机试验

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-14 DOI: 10.1038/s41746-024-01241-2

Andrew Quanbeck, Ming-Yuan Chih, Linda Park, Xiang Li, Qiang Xie, Alice Pulvermacher, Samantha Voelker, Rachel Lundwall, Katherine Eby, Bruce Barrett, Randall Brown

{"title":"A randomized trial testing digital medicine support models for mild-to-moderate alcohol use disorder","authors":"Andrew Quanbeck, Ming-Yuan Chih, Linda Park, Xiang Li, Qiang Xie, Alice Pulvermacher, Samantha Voelker, Rachel Lundwall, Katherine Eby, Bruce Barrett, Randall Brown","doi":"10.1038/s41746-024-01241-2","DOIUrl":"10.1038/s41746-024-01241-2","url":null,"abstract":"This paper reports the results of a hybrid effectiveness-implementation randomized trial that systematically varied levels of human oversight required to support the implementation of a digital medicine intervention for persons with mild-to-moderate alcohol use disorder (AUD). Participants were randomly assigned to three groups representing possible digital health support models within a health system: self-monitored use (SM; n = 185), peer-supported use (PS; n = 186), or a clinically integrated model CI; (n = 187). Across all three groups, the percentage of self-reported heavy drinking days dropped from 38.4% at baseline (95% CI [35.8%, 41%]) to 22.5% (19.5%, 25.5%) at 12 months. The clinically integrated group showed significant improvements in mental health and quality of life compared to the self-monitoring group (p = 0.011). However, higher attrition rates in the clinically integrated group warrant consideration in interpreting this result. Results suggest that making a self-guided digital intervention available to patients may be a viable option for health systems looking to promote alcohol risk reduction. This study was prospectively registered at clinicaltrials.gov on 7/03/2019 (NCT04011644).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01241-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231290","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

Variational Bayes machine learning for risk adjustment of general outcome indicators with examples in urology 变异贝叶斯机器学习用于一般结果指标的风险调整，以泌尿外科为例

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-14 DOI: 10.1038/s41746-024-01244-z

Harvey Jia Wei Koh, Dragan Gašević, David Rankin, Stephane Heritier, Mark Frydenberg, Stella Talic

{"title":"Variational Bayes machine learning for risk adjustment of general outcome indicators with examples in urology","authors":"Harvey Jia Wei Koh, Dragan Gašević, David Rankin, Stephane Heritier, Mark Frydenberg, Stella Talic","doi":"10.1038/s41746-024-01244-z","DOIUrl":"10.1038/s41746-024-01244-z","url":null,"abstract":"Risk adjustment is often necessary for outcome quality indicators (QIs) to provide fair and accurate feedback to healthcare professionals. However, traditional risk adjustment models are generally oversimplified and not equipped to disentangle complex factors influencing outcomes that are out of a healthcare professional’s control. We present VIRGO, a novel variational Bayes model trained on routinely collected, large administrative datasets to risk-adjust outcome QIs. VIRGO uses detailed demographics, diagnosis, and procedure codes to provide individualized risk adjustment and explanations on patient factors affecting outcomes. VIRGO achieves state-of-the-art on external datasets and features capabilities of uncertainty expression, explainable features, and counterfactual analysis capabilities. VIRGO facilitates risk adjustment by explaining how patient factors led to adverse outcomes and expresses the uncertainty of each prediction, allowing healthcare professionals to not only explore patient factors with unexplained variance that are associated with worse outcomes but also reflect on the quality of their clinical practice.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01244-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231294","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

Can social media encourage diabetes self-screenings? A randomized controlled trial with Indonesian Facebook users 社交媒体能否鼓励糖尿病自我筛查？针对印度尼西亚 Facebook 用户的随机对照试验

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-13 DOI: 10.1038/s41746-024-01246-x

Manuela Fritz, Michael Grimm, Ingmar Weber, Elad Yom-Tov, Benedictus Praditya

{"title":"Can social media encourage diabetes self-screenings? A randomized controlled trial with Indonesian Facebook users","authors":"Manuela Fritz, Michael Grimm, Ingmar Weber, Elad Yom-Tov, Benedictus Praditya","doi":"10.1038/s41746-024-01246-x","DOIUrl":"10.1038/s41746-024-01246-x","url":null,"abstract":"Nudging individuals without obvious symptoms of non-communicable diseases (NCDs) to undergo a health screening remains a challenge, especially in middle-income countries, where NCD awareness is low but the incidence is high. We assess whether an awareness campaign implemented on Facebook can encourage individuals in Indonesia to undergo an online diabetes self-screening. We use Facebook’s advertisement function to randomly distribute graphical ads related to the risk and consequences of diabetes. Depending on their risk score, participants receive a recommendation to undergo a professional screening. We were able to reach almost 300,000 individuals in only three weeks. More than 1400 individuals completed the screening, inducing costs of about US$0.75 per person. The two ads labeled “diabetes consequences” and “shock” outperform all other ads. A follow-up survey shows that many high-risk respondents have scheduled a professional screening. A cost-effectiveness analysis suggests that our campaign can diagnose an additional person with diabetes for about US$9.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01246-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174956","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