NPJ Digital Medicine最新文献

Interpretable machine learning model for digital lung cancer prescreening in Chinese populations with missing data 用于中国缺失数据人群肺癌数字化预检的可解释机器学习模型

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2024-11-19 DOI: 10.1038/s41746-024-01309-z

Shuaijie Zhang, Qing Wang, Xifeng Hu, Botao Zhang, Shuangshuang Sun, Ying Yuan, Xiaofeng Jia, Yuanyuan Yu, Fuzhong Xue

{"title":"Interpretable machine learning model for digital lung cancer prescreening in Chinese populations with missing data","authors":"Shuaijie Zhang, Qing Wang, Xifeng Hu, Botao Zhang, Shuangshuang Sun, Ying Yuan, Xiaofeng Jia, Yuanyuan Yu, Fuzhong Xue","doi":"10.1038/s41746-024-01309-z","DOIUrl":"https://doi.org/10.1038/s41746-024-01309-z","url":null,"abstract":"<p>We developed an interpretable model, BOUND (Bayesian netwOrk for large-scale lUng caNcer Digital prescreening), using a comprehensive EHR dataset from the China to improve lung cancer detection rates. BOUND employs Bayesian network uncertainty inference, allowing it to predict lung cancer risk even with missing data and identify high-risk factors. Developed using data from 905,194 individuals, BOUND achieved an AUC of 0.866 in internal validation, with time- and geography-based external validations yielding AUCs of 0.848 and 0.841, respectively. In datasets with 10%–70% missing data, AUC ranged from 0.827 – 0.746. The model demonstrates strong calibration, clinical utility, and robust performance in both balanced and imbalanced datasets. A risk scorecard was also created, improving detection rates up to 6.8 times, available free online (https://drzhang1.aiself.net/). BOUND enables non-radiative, cost-effective lung cancer prescreening, excels with missing data, and addresses treatment inequities in resource-limited primary healthcare settings.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"9 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670899","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

An iterative approach for estimating domain-specific cognitive abilities from large scale online cognitive data 从大规模在线认知数据中估算特定领域认知能力的迭代方法

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2024-11-19 DOI: 10.1038/s41746-024-01327-x

Valentina Giunchiglia, Dragos-Cristian Gruia, Annalaura Lerede, William Trender, Peter Hellyer, Adam Hampshire

{"title":"An iterative approach for estimating domain-specific cognitive abilities from large scale online cognitive data","authors":"Valentina Giunchiglia, Dragos-Cristian Gruia, Annalaura Lerede, William Trender, Peter Hellyer, Adam Hampshire","doi":"10.1038/s41746-024-01327-x","DOIUrl":"https://doi.org/10.1038/s41746-024-01327-x","url":null,"abstract":"<p>Online cognitive tasks are gaining traction as scalable and cost-effective alternatives to traditional supervised assessments. However, variability in peoples’ home devices, visual and motor abilities, and speed-accuracy biases confound the specificity with which online tasks can measure cognitive abilities. To address these limitations, we developed IDoCT (Iterative Decomposition of Cognitive Tasks), a method for estimating domain-specific cognitive abilities and trial-difficulty scales from task performance timecourses in a data-driven manner while accounting for device and visuomotor latencies, unspecific cognitive processes and speed-accuracy trade-offs. IDoCT can operate with any computerised task where cognitive difficulty varies across trials. Using data from 388,757 adults, we show that IDoCT successfully dissociates cognitive abilities from these confounding factors. The resultant cognitive scores exhibit stronger dissociation of psychometric factors, improved cross-participants distributions, and meaningful demographic’s associations. We propose that IDoCT can enhance the precision of online cognitive assessments, especially in large scale clinical and research applications.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"22 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670898","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 utility of personal wearable data in long COVID and personalized patient care. 个人可穿戴数据在长 COVID 和个性化患者护理中的实用性。

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-18 DOI: 10.1038/s41746-024-01341-z

Elizabeth J Enichen, Kimia Heydari, Serena Wang, Grace C Nickel, Joseph C Kvedar

引用次数: 0

Developing a Canadian artificial intelligence medical curriculum using a Delphi study 利用德尔菲研究开发加拿大人工智能医学课程

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-18 DOI: 10.1038/s41746-024-01307-1

Rohit Singla, Nikola Pupic, Seyed-Aryan Ghaffarizadeh, Caroline Kim, Ricky Hu, Bruce B. Forster, Ilker Hacihaliloglu

{"title":"Developing a Canadian artificial intelligence medical curriculum using a Delphi study","authors":"Rohit Singla, Nikola Pupic, Seyed-Aryan Ghaffarizadeh, Caroline Kim, Ricky Hu, Bruce B. Forster, Ilker Hacihaliloglu","doi":"10.1038/s41746-024-01307-1","DOIUrl":"10.1038/s41746-024-01307-1","url":null,"abstract":"The integration of artificial intelligence (AI) education into medical curricula is critical for preparing future healthcare professionals. This research employed the Delphi method to establish an expert-based AI curriculum for Canadian undergraduate medical students. A panel of 18 experts in health and AI across Canada participated in three rounds of surveys to determine essential AI learning competencies. The study identified key curricular components across ethics, law, theory, application, communication, collaboration, and quality improvement. The findings demonstrate substantial support among medical educators and professionals for the inclusion of comprehensive AI education, with 82 out of 107 curricular competencies being deemed essential to address both clinical and educational priorities. It additionally provides suggestions on methods to integrate these competencies within existing dense medical curricula. The endorsed set of objectives aims to enhance AI literacy and application skills among medical students, equipping them to effectively utilize AI technologies in future healthcare settings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-10"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01307-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665207","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

Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients 优化右美托咪定剂量以预防重症患者谵妄的强化学习模型

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-18 DOI: 10.1038/s41746-024-01335-x

Hong Yeul Lee, Soomin Chung, Dongwoo Hyeon, Hyun-Lim Yang, Hyung-Chul Lee, Ho Geol Ryu, Hyeonhoon Lee

{"title":"Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients","authors":"Hong Yeul Lee, Soomin Chung, Dongwoo Hyeon, Hyun-Lim Yang, Hyung-Chul Lee, Ho Geol Ryu, Hyeonhoon Lee","doi":"10.1038/s41746-024-01335-x","DOIUrl":"10.1038/s41746-024-01335-x","url":null,"abstract":"Delirium can result in undesirable outcomes including increased length of stays and mortality in patients admitted to the intensive care unit (ICU). Dexmedetomidine has emerged for delirium prevention in these patients; however, optimal dosing is challenging. A reinforcement learning-based Artificial Intelligence model for Delirium prevention (AID) is proposed to optimize dexmedetomidine dosing. The model was developed and internally validated using 2416 patients (2531 ICU admissions) and externally validated on 270 patients (274 ICU admissions). The estimated performance return of the AID policy was higher than that of the clinicians’ policy in both derivation (0.390 95% confidence interval [CI] 0.361 to 0.420 vs. −0.051 95% CI −0.077 to −0.025) and external validation (0.186 95% CI 0.139 to 0.236 vs. −0.436 95% CI −0.474 to −0.402) cohorts. Our finding indicates that AID might support clinicians’ decision-making regarding dexmedetomidine dosing to prevent delirium in ICU patients, but further off-policy evaluation is required.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01335-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665208","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

Simulating A/B testing versus SMART designs for LLM-driven patient engagement to close preventive care gaps 模拟 A/B 测试与 SMART 设计，以 LLM 驱动的患者参与弥补预防性保健差距

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-18 DOI: 10.1038/s41746-024-01330-2

Sanjay Basu, Dean Schillinger, Sadiq Y. Patel, Joseph Rigdon

{"title":"Simulating A/B testing versus SMART designs for LLM-driven patient engagement to close preventive care gaps","authors":"Sanjay Basu, Dean Schillinger, Sadiq Y. Patel, Joseph Rigdon","doi":"10.1038/s41746-024-01330-2","DOIUrl":"10.1038/s41746-024-01330-2","url":null,"abstract":"Population health initiatives often rely on cold outreach to close gaps in preventive care, such as overdue screenings or immunizations. Tailoring messages to diverse patient populations remains challenging, as traditional A/B testing requires large sample sizes to test only two alternative messages. With increasing availability of large language models (LLMs), programs can utilize tiered testing among both LLM and manual human agents, presenting the dilemma of identifying which patients need different levels of human support to cost-effectively engage large populations. Using microsimulations, we compared both the statistical power and false positive rates of A/B testing and Sequential Multiple Assignment Randomized Trials (SMART) for developing personalized communications across multiple effect sizes and sample sizes. SMART showed better cost-effectiveness and net benefit across all scenarios, but superior power for detecting heterogeneous treatment effects (HTEs) only in later randomization stages, when populations were more homogeneous and subtle differences drove engagement differences.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-8"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01330-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665211","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

Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features 利用可穿戴睡眠和昼夜节律特征准确预测情绪障碍患者的情绪发作

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-18 DOI: 10.1038/s41746-024-01333-z

Dongju Lim, Jaegwon Jeong, Yun Min Song, Chul-Hyun Cho, Ji Won Yeom, Taek Lee, Jung-Been Lee, Heon-Jeong Lee, Jae Kyoung Kim

{"title":"Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features","authors":"Dongju Lim, Jaegwon Jeong, Yun Min Song, Chul-Hyun Cho, Ji Won Yeom, Taek Lee, Jung-Been Lee, Heon-Jeong Lee, Jae Kyoung Kim","doi":"10.1038/s41746-024-01333-z","DOIUrl":"10.1038/s41746-024-01333-z","url":null,"abstract":"Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in mood disorder patients. However, current models often require various data types, limiting real-world application. Here, we develop models that predict future episodes using only sleep-wake data, easily gathered through smartphones and wearables when trained on an individual’s sleep-wake history and past mood episodes. Using mathematical modeling to longitudinal data from 168 patients (587 days average clinical follow-up, 267 days wearable data), we derived 36 sleep and circadian rhythm features. These features enabled accurate next-day predictions for depressive, manic, and hypomanic episodes (AUCs: 0.80, 0.98, 0.95). Notably, daily circadian phase shifts were the most significant predictors: delays linked to depressive episodes, advances to manic episodes. This prospective observational cohort study (ClinicalTrials.gov: NCT03088657, 2017-3-23) shows sleep-wake data, combined with prior mood episode history, can effectively predict mood episodes, enhancing mood disorder management.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01333-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665222","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

A strategy for cost-effective large language model use at health system-scale 在医疗系统范围内使用具有成本效益的大型语言模型的策略

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-18 DOI: 10.1038/s41746-024-01315-1

Eyal Klang, Donald Apakama, Ethan E. Abbott, Akhil Vaid, Joshua Lampert, Ankit Sakhuja, Robert Freeman, Alexander W. Charney, David Reich, Monica Kraft, Girish N. Nadkarni, Benjamin S. Glicksberg

{"title":"A strategy for cost-effective large language model use at health system-scale","authors":"Eyal Klang, Donald Apakama, Ethan E. Abbott, Akhil Vaid, Joshua Lampert, Ankit Sakhuja, Robert Freeman, Alexander W. Charney, David Reich, Monica Kraft, Girish N. Nadkarni, Benjamin S. Glicksberg","doi":"10.1038/s41746-024-01315-1","DOIUrl":"10.1038/s41746-024-01315-1","url":null,"abstract":"Large language models (LLMs) can optimize clinical workflows; however, the economic and computational challenges of their utilization at the health system scale are underexplored. We evaluated how concatenating queries with multiple clinical notes and tasks simultaneously affects model performance under increasing computational loads. We assessed ten LLMs of different capacities and sizes utilizing real-world patient data. We conducted >300,000 experiments of various task sizes and configurations, measuring accuracy in question-answering and the ability to properly format outputs. Performance deteriorated as the number of questions and notes increased. High-capacity models, like Llama-3–70b, had low failure rates and high accuracies. GPT-4-turbo-128k was similarly resilient across task burdens, but performance deteriorated after 50 tasks at large prompt sizes. After addressing mitigable failures, these two models can concatenate up to 50 simultaneous tasks effectively, with validation on a public medical question-answering dataset. An economic analysis demonstrated up to a 17-fold cost reduction at 50 tasks using concatenation. These results identify the limits of LLMs for effective utilization and highlight avenues for cost-efficiency at the enterprise scale.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":12.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01315-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665209","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

Cost-effectiveness analysis of mHealth applications for depression in Germany using a Markov cohort simulation 利用马尔可夫队列模拟对德国抑郁症移动医疗应用的成本效益进行分析

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-17 DOI: 10.1038/s41746-024-01324-0

Bettina Freitag, Marie Uncovska, Sven Meister, Christian Prinz, Leonard Fehring

{"title":"Cost-effectiveness analysis of mHealth applications for depression in Germany using a Markov cohort simulation","authors":"Bettina Freitag, Marie Uncovska, Sven Meister, Christian Prinz, Leonard Fehring","doi":"10.1038/s41746-024-01324-0","DOIUrl":"10.1038/s41746-024-01324-0","url":null,"abstract":"Regulated mobile health applications are called digital health applications (“DiGA”) in Germany. To qualify for reimbursement by statutory health insurance companies, DiGA have to prove positive care effects in scientific studies. Since the empirical exploration of DiGA cost-effectiveness remains largely uncharted, this study pioneers the methodology of cohort-based state-transition Markov models to evaluate DiGA for depression. As health states, we define mild, moderate, severe depression, remission and death. Comparing a future scenario where 50% of patients receive supplementary DiGA access with the current standard of care reveals a gain of 0.02 quality-adjusted life years (QALYs) per patient, which comes at additional direct costs of ~1536 EUR per patient over a five-year timeframe. Influencing factors determining DiGA cost-effectiveness are the DiGA cost structure and individual DiGA effectiveness. Under Germany’s existing cost structure, DiGA for depression are yet to demonstrate the ability to generate overall savings in healthcare expenditures.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01324-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645800","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

Multisource representation learning for pediatric knowledge extraction from electronic health records 从电子健康记录中提取儿科知识的多源表征学习

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-13 DOI: 10.1038/s41746-024-01320-4

Mengyan Li, Xiaoou Li, Kevin Pan, Alon Geva, Doris Yang, Sara Morini Sweet, Clara-Lea Bonzel, Vidul Ayakulangara Panickan, Xin Xiong, Kenneth Mandl, Tianxi Cai

{"title":"Multisource representation learning for pediatric knowledge extraction from electronic health records","authors":"Mengyan Li, Xiaoou Li, Kevin Pan, Alon Geva, Doris Yang, Sara Morini Sweet, Clara-Lea Bonzel, Vidul Ayakulangara Panickan, Xin Xiong, Kenneth Mandl, Tianxi Cai","doi":"10.1038/s41746-024-01320-4","DOIUrl":"10.1038/s41746-024-01320-4","url":null,"abstract":"Electronic Health Record (EHR) systems are particularly valuable in pediatrics due to high barriers in clinical studies, but pediatric EHR data often suffer from low content density. Existing EHR code embeddings tailored for the general patient population fail to address the unique needs of pediatric patients. To bridge this gap, we introduce a transfer learning approach, MUltisource Graph Synthesis (MUGS), aimed at accurate knowledge extraction and relation detection in pediatric contexts. MUGS integrates graphical data from both pediatric and general EHR systems, along with hierarchical medical ontologies, to create embeddings that adaptively capture both the homogeneity and heterogeneity between hospital systems. These embeddings enable refined EHR feature engineering and nuanced patient profiling, proving particularly effective in identifying pediatric patients similar to specific profiles, with a focus on pulmonary hypertension (PH). MUGS embeddings, resistant to negative transfer, outperform other benchmark methods in multiple applications, advancing evidence-based pediatric research.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-15"},"PeriodicalIF":12.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01320-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600843","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