Development of death-risk score based on epidemiology of six mental disorders and application to mortality reduction via modifiable health behaviors [0.03%]
基于六种精神障碍的流行病学发展的死亡风险评分及其在通过可改变健康行为减少死亡率方面的应用
Jihun Song,Sun Jae Park,Eun Seok Kang et al.
Jihun Song et al.
Objective: Prediction models for mortality rarely incorporate diverse psychiatric conditions, alongside clinical, behavioral, and demographic risk factors. We aimed to develop and validate a mortality risk score using a G...
A data-driven method for research trend analysis in a scientific discipline: Application to the journal of biomedical informatics [0.03%]
一种基于数据驱动的科研动向分析方法：生物医学信息学杂志研究动向分析案例研究
Yilu Fang,Gongbo Zhang,Samir Sanchez Tejada et al.
Yilu Fang et al.
Objective: Accurately characterizing research trends is critical for identifying cutting-edge scientific breakthroughs in their infancy and informing strategic priorities. This research contributes a pipeline that utilize...
Letter to the Editor: Patient-level and temporal data leakage can inflate performance in medication recommendation models: A critical appraisal of the original KATMed publication [0.03%]
写给《自然》杂志编辑的一封信：患者层面和时间层面的数据泄露可能会夸大药物推荐模型的性能：对原始KATMed论文的一项批判性评价
Isaac Zablah,Yolly Molina,Antonio Garcia Loureiro
Isaac Zablah
Interpretable Graph Convolutional Networks for cardiovascular disease risk prediction in patients with Type 2 Diabetes Mellitus [0.03%]
用于二型糖尿病患者心血管疾病风险预测的可解释图卷积网络
Ioannis Siachos,Maria Athanasiou,Konstantia Zarkogianni et al.
Ioannis Siachos et al.
Background: Cardiovascular disease (CVD) is the most prevalent complication of Type 2 Diabetes Mellitus (T2DM) and a leading cause of mortality in this population. Early and accurate CVD risk prediction is essential for t...
Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help [0.03%]
社区医疗中心难以制作出可靠的临床预测模型：数据增强可以提供帮助
Katherine E Brown,Bradley A Malin,Sharon E Davis
Katherine E Brown
Objective: Machine learning models (ML) often require localization to perform optimally in local populations. We hypothesize that smaller community healthcare centers may not have the necessary patient volume to facilitat...
Knowledge-Guided and Reinforced Selective State Space Model for radiology report generation [0.03%]
知识引导和强化的选择性状态空间模型在放射学报告生成中的应用
Ziyang Li,Dedong Yang,Rongtao Li et al.
Ziyang Li et al.
Objective: To address the limitations of "black box" sequence models and simplistic reward functions in radiology report generation, we aim to improve clinical accuracy and linguistic quality by integrating explicit medic...
Model utility and explainability in federated learning - A case study in healthcare using fundus oculi datasets [0.03%]
联邦学习中的模型实用性和可解释性——一种使用眼底数据的医疗案例研究
Niklas Penzel,Daniel Scheliga,Hannes Oppermann et al.
Niklas Penzel et al.
Objective: Introduce a case study for Federated Learning (FL) in healthcare, addressing challenges posed by patient privacy and limited large-scale datasets. Our goal is to assess the features learned by FL methods in a s...
Enhancing adverse drug event extraction and summarization for cancer drugs through large language models [0.03%]
通过大规模语言模型增强癌症药物的不良事件提取和总结能力
Sofia Jamil,Sriparna Saha,Rajiv Misra
Sofia Jamil
Purpose: Cancer treatments such as chemotherapy, targeted therapy, and immunotherapy can effectively combat malignant cells but frequently cause serious side effects by damaging healthy tissues. This underscores the need ...
Scalable discovery and validation of order-specific electronic health record event trajectories for interpretable adverse-outcome risk estimation [0.03%]
可扩展地发现和验证订单特定的电子健康记录事件轨迹以进行可解释的不良结果风险估计
Brice Edelman,Hannah Kim,Jeffrey Skolnick
Brice Edelman
Objective: Clinicians must estimate patients' risk of adverse outcomes, yet many electronic health record tools represent health history as an unordered "bag of codes," discarding temporal order. Machine learning tools th...
Context-Aware adaptive normalization LSTM (CAAN-LSTM) for immunotherapy decision support in cancer clinical data analysis [0.03%]
基于上下文自适应规范的LSTM（CAAN-LSTM）在癌症临床数据免疫治疗决策支持中的应用
Rian Balafkhar,Yaser Baalawi,Abdullah Mohammed Almashhor et al.
Rian Balafkhar et al.
Background: Clinical decision-making for cancer immunotherapy is challenged by the heterogeneous and often incomplete nature of patient time-series data. Traditional models struggle to account for individual patient varia...