Large language models enable prognostic stratification of cancer patients using real-world clinical notes [0.03%]
大型语言模型利用真实世界临床记录对癌症患者进行预后分层
Niklas Kiermeyer,Tim Lenfers,Amin Dada et al.
Niklas Kiermeyer et al.
In medical documentation, vast amounts of unstructured text are generated that are still underutilized in current prognostic models. We investigate the potential of self-hosted large language models (LLM) to extract clinically meaningful, p...
Ivana Nanevski,Maryam Mohebi,Sebastian Jäger et al.
Ivana Nanevski et al.
Machine Learning (ML) research in healthcare remains challenging as large, privacy-preserving open datasets are lacking. Synthetic data could offer a solution, but the value of synthetic data depends on diverse and conflicting criteria such...
Designing user-centered evaluations: Leveraging beta-testing results to develop process evaluation interview questions for the AMPLIFY program [0.03%]
以用户为中心的评估设计：利用公测结果开发“AMPLIFY”项目的流程评估访谈问题
Jami L Anderson,Laura Q Rogers,Wendy Demark-Wahnefried et al.
Jami L Anderson et al.
Refining digital health programs with end-user feedback is essential throughout program lifecycles to ensure user-centered perspectives are incorporated during all program development. Results from beta-testing "think aloud" interviews expl...
Recognition of eating episodes via commercial smartwatch sensors analysis [0.03%]
基于商用智能手表传感器分析的进食事件识别
Luca Vedovelli,Mohammad Junayed Bhuyan,Corrado Lanera et al.
Luca Vedovelli et al.
Smartwatches with movement sensors have emerged as promising tools for monitoring dietary behavior. This study uses commercial smartwatch sensor data, namely tri-axial acceleration together with device-derived orientation (pitch and roll) a...
Vision-language models for human motion understanding: Lessons from stroke rehabilitation [0.03%]
面向人类运动理解的视觉语言模型：卒中康复中的启示
Victor Li,Naveenraj Kamalakannan,Avinash Parnandi et al.
Victor Li et al.
Vision-language models (VLMs) have demonstrated remarkable performance across a wide range of computer-vision tasks, sparking interest in their potential for digital health applications. Here, we apply VLMs to two fundamental challenges in ...
A digital marker for stratifying cardiovascular metabolic comorbidities among the middle-aged and elderly adults [0.03%]
中老年人心血管代谢疾病的分层数字化标记物
Danhui Mao,Sheng Zhao,Jiao Lu
Danhui Mao
Cardiovascular Metabolic Comorbidities (CMM) share common physiological mechanisms in inflammation and immunity, oxidative stress, and insulin resistance, leading to mutual disease interactions and complex clinical manifestations. To addres...
User engagement in the tuberculosis treatment support tools intervention and its impact on treatment outcomes: A secondary analysis of a pragmatic trial [0.03%]
结核病治疗支持工具干预措施中的患者依从性及其对治疗效果的影响：实用试验的二次分析
Sarah J Iribarren,Jason Rupp,Jennifer Sprecher et al.
Sarah J Iribarren et al.
Digital adherence technologies (DATs) may improve health behaviors only when users engage, but links between engagement, user factors, and outcomes are unclear. TB Treatment Support Tools (TB-TST) is a DAT with a smartphone app connecting p...
Machine learning for risk stratification of hypertensive disorders of pregnancy: Enhancing clinical efficiency in low-resource antenatal care in Tanzania [0.03%]
坦桑尼亚孕期抗高血压疾病的机器学习风险分层：提高低资源产前护理的临床效率
Isaac Lyatuu,Emmanuel P Mwanga,Yusuph Kulindwa et al.
Isaac Lyatuu et al.
Maternal mortality in Tanzania remains a public health crisis, with Hypertensive Disorders of Pregnancy (HDP) causing 34% of direct obstetric deaths. In overburdened government clinics, high patient volumes and limited resources often restr...
The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation study [0.03%]
关于人工智能生成的健康建议信任度量表（TAIGHA）及简短版本（TAIGHA-S）的研发与验证性研究
Marvin Kopka,Azeem Majeed,Gabriella Spinelli et al.
Marvin Kopka et al.
Artificial Intelligence (AI) tools such as large language models (LLMs) are increasingly used by the public to obtain health information and support health-related decisions. Because such use involves advice-taking, following or rejecting A...
Time-series prediction of adverse birth outcomes in the U.S. using multilayer perceptron neural networks [0.03%]
美国多层感知器神经网络时间序列预测不良出生结局
Bayuh Asmamaw Hailu
Bayuh Asmamaw Hailu
Adverse birth outcomes (ABOs), including preterm birth, low birth weight, low Apgar scores, and neonatal mortality, remain major public health challenges in the United States and disproportionately affect racial, socioeconomic, and demograp...