Fair Multi-modal Canonical Correlation Analysis: A Neuroimaging Study of Alzheimer's Disease [0.03%]
公平的多模态典范相关分析：阿尔茨海默病神经影像学研究
Zhuoping Zhou,Boning Tong,Bojian Hou et al.
Zhuoping Zhou et al.
This study addresses fairness concerns in Multi-modal Canonical Correlation Analysis (MCCA), a technique for analyzing relationships across multiple datasets. We introduce Fair MCCA (F-MCCA), which mitigates bias by optimizing for both corr...
A Treatment Selection Model for Opioid Use Disorder Using Electronic Health Record and ZIP-Level Data [0.03%]
基于电子健康记录和ZIP级数据的阿片类药物使用障碍治疗选择模型
Leigh Anne Tang,Kristopher A Kast,Colin G Walsh
Leigh Anne Tang
Background: Buprenorphine and methadone are effective medications for opioid use disorder (OUD) but remain underused, particularly when specialists are not leading care decisions. Objective: We developed a predictive model to guide treatmen...
Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare [0.03%]
迈向安全的AI临床医生：医疗领域大型语言模型破解风险的全面研究
Hang Zhang,Qian Lou,Yanshan Wang
Hang Zhang
Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systemat...
Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide [0.03%]
基于众包的知识图谱构建用于药物副作用研究：使用大语言模型以Semaglutide为例的研究应用
Zhijie Duan,Kai Wei,Zhaoqian Xue et al.
Zhijie Duan et al.
Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy social media content remains a challenging task. We present a sys...
PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping [0.03%]
基于大型语言模型的计算表型分析方法评估框架(PHEONA)
Sarah A Pungitore,Shashank Yadav,Vignesh Subbian
Sarah A Pungitore
Computational phenotyping is essential for biomedical research but often requires significant time and resources, especially since traditional methods typically involve extensive manual data review. While machine learning and natural langua...
Using National COVID cohort collaborative (N3C) data to explore impact of COVID-19 infection on kidney function in patients receiving lithium therapy [0.03%]
利用国家COVID协同队列合作(N3C)数据探讨COVID-19感染对锂治疗患者肾功能的影响
Yujia Tian,Anastasia K Yocum,Jinju Li et al.
Yujia Tian et al.
Lithium is a drug primarily used to treat psychiatric disorders and has shown significant efficacy in treating bipolar disorder (BD) and major depressive disorder (MDD). Although lithium can stabilize emotional fluctuations and prevent the ...
HIBERT: A Hybrid Clustering BERT for Interpretable Opioid Overdose Risk Prediction [0.03%]
一种混合聚类BERT模型，用于可解释的阿片类药物过量风险预测
Zihan Ding,Xinyu Dong,Yinan Liu et al.
Zihan Ding et al.
Drug overdose, mostly from opioids, is a continuing crisis in the US. Highly accurate models for early detection of opioid overdose (OD) risk are crucial for early intervention and prevention. While deep learning has shown promise in using ...
Leveraging Large Language Models for Thyroid Nodule Information Extraction and Matching Across Medical Reports [0.03%]
利用大型语言模型进行甲状腺结节信息提取和跨医学报告匹配
Dongwoo Lee,Dominic Amara,Chandler Beon et al.
Dongwoo Lee et al.
Accurate extraction of thyroid nodule features from radiology and pathology reports is clinically essential for guiding patient management decisions, such as surgical intervention or active surveillance. However, manual data extraction from...
DGSurv: Dynamic Graph-Based Multimodal Learning for Interpretable Cancer Survival Prediction [0.03%]
基于动态图的多模态学习的癌症生存预测方法
Sajjad Shahabi,Zijun Cui,Ruishan Liu et al.
Sajjad Shahabi et al.
Multimodal learning in cancer research offers transformative potential for enhancing medical care and guiding clinical decisions. Most analyses rely on unimodal inputs or employ simplistic multimodal fusion techniques, which do not optimall...
Shedding Light on the Invisible Work of Peer Recovery Support Specialists through the Design of a Point of Care Technology [0.03%]
通过设计一种点对点护理技术来揭示同伴康复支持专家无形工作的光芒
Jessica A Pater,Elisabeth Andrews,Michelle Drouin et al.
Jessica A Pater et al.
Peer Recovery Support Specialists (PRSSs) are certified professionals that provide social, informational, and logistical supportto people in recovery fromsubstance use disorders. In this study, we report the findings from design work comple...