A Pipeline for Automating Emergency Medicine Documentation Using LLMs with Retrieval-Augmented Text Generation [0.03%]
基于检索增强文本生成的大型语言模型自动急诊医学文档处理管道
Denis Moser,Matthias Bender,Murat Sariyar
Denis Moser
Accurate and efficient documentation of patient information is vital in emergency healthcare settings. Traditional manual documentation methods are often time-consuming and prone to errors, potentially affecting patient outcomes. Large Lang...
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group [0.03%]
儿童霍奇金淋巴瘤疗效预测：儿科肿瘤协作组AHOD0031方案的机器学习分析报告
Cédric Beaulac,Jeffrey S Rosenthal,Qinglin Pei et al.
Cédric Beaulac et al.
In this manuscript we analyze a data set containing information on children with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received and survival status were collected together with other covariates such as demographics ...
Marcin Skowron,Martin Trapp,Sabine Payr et al.
Marcin Skowron et al.
We study the detection of character types from fictional dialog texts such as screenplays. As approaches based on the analysis of utterances' linguistic properties are not sufficient to identify all fictional character types, we develop an ...
Chandrima Sarkar,Sarah Cooley,Jaideep Srivastava
Chandrima Sarkar
Although feature selection is a well-developed research area, there is an ongoing need to develop methods to make classifiers more efficient. One important challenge is the lack of a universal feature selection technique which produces simi...
Maintaining Engagement in Long-term Interventions with Relational Agents [0.03%]
具有关系代理的长期干预项目的参与度维护
Timothy Bickmore,Daniel Schulman,Langxuan Yin
Timothy Bickmore
We discuss issues in designing virtual humans for applications which require long-term voluntary use, and the problem of maintaining engagement with users over time. Concepts and theories related to engagement from a variety of disciplines ...