PDF(Pubmed)
Abstract：
OBJECTIVE: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care.
METHODS: We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural Network for precise identification of patient primary concerns via multi-class classification. We examined 3 traditional machine learning models, 9 BERT-based language models, 6 fusion models, and 2 ensemble models.
RESULTS: The outcomes of our experimentation underscore the superior performance achieved by BERT-based models in comparison to traditional machine learning models. Remarkably, our fusion model emerges as the top-performing solution, delivering a notably improved accuracy score of 77.67 ± 2.74% and an F1 score of 74.37 ± 3.70% in macro-average.
CONCLUSIONS: This study highlights the feasibility and effectiveness of multi-class classification for patient primary concern detection and the proposed fusion framework for enhancing primary concern detection.
CONCLUSIONS: The use of multi-class classification enhanced by a fusion of multiple pretrained LMs not only improves the accuracy and efficiency of patient primary concern identification in PPMs but also aids in managing the rising volume of PPMs in healthcare, ensuring critical patient communications are addressed promptly and accurately.
METHODS: We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural Network for precise identification of patient primary concerns via multi-class classification. We examined 3 traditional machine learning models, 9 BERT-based language models, 6 fusion models, and 2 ensemble models.
RESULTS: The outcomes of our experimentation underscore the superior performance achieved by BERT-based models in comparison to traditional machine learning models. Remarkably, our fusion model emerges as the top-performing solution, delivering a notably improved accuracy score of 77.67 ± 2.74% and an F1 score of 74.37 ± 3.70% in macro-average.
CONCLUSIONS: This study highlights the feasibility and effectiveness of multi-class classification for patient primary concern detection and the proposed fusion framework for enhancing primary concern detection.
CONCLUSIONS: The use of multi-class classification enhanced by a fusion of multiple pretrained LMs not only improves the accuracy and efficiency of patient primary concern identification in PPMs but also aids in managing the rising volume of PPMs in healthcare, ensuring critical patient communications are addressed promptly and accurately.
摘要：
目标：随着医疗保健环境中有效的PPM分诊的需求和工作量的增加，患者门户消息（PPM）激增，刺激了对AI驱动解决方案的探索，以简化医疗保健工作流程。确保及时响应患者以满足他们的医疗保健需求。然而,在PPMs中,人们较少关注隔离和理解患者的主要关注点,这种做法有可能产生更细致入微的见解,并提高医疗保健服务和以患者为中心的护理质量.
方法：我们提出了一种融合框架，通过卷积神经网络利用具有不同语言优势的预训练语言模型（LM），通过多类分类精确识别患者的主要关注点。我们研究了3种传统的机器学习模型，9个基于BERT的语言模型，6个融合模型，和2合奏模型。
结果：我们的实验结果强调了基于BERT的模型与传统机器学习模型相比所取得的卓越性能。值得注意的是,我们的融合模型成为性能最好的解决方案，显著提高了总体平均准确率77.67±2.74%和F1评分74.37±3.70%.
结论：本研究强调了用于患者主要关注检测的多类别分类的可行性和有效性，以及用于增强主要关注检测的拟议融合框架。
结论：通过融合多个预先训练的LM来增强多类分类的使用不仅提高了PPM中患者主要关注点识别的准确性和效率，而且有助于管理不断增长的PPM在医疗保健中的数量。确保及时和准确地解决关键的病人沟通。
方法：我们提出了一种融合框架，通过卷积神经网络利用具有不同语言优势的预训练语言模型（LM），通过多类分类精确识别患者的主要关注点。我们研究了3种传统的机器学习模型，9个基于BERT的语言模型，6个融合模型，和2合奏模型。
结果：我们的实验结果强调了基于BERT的模型与传统机器学习模型相比所取得的卓越性能。值得注意的是,我们的融合模型成为性能最好的解决方案，显著提高了总体平均准确率77.67±2.74%和F1评分74.37±3.70%.
结论：本研究强调了用于患者主要关注检测的多类别分类的可行性和有效性，以及用于增强主要关注检测的拟议融合框架。
结论：通过融合多个预先训练的LM来增强多类分类的使用不仅提高了PPM中患者主要关注点识别的准确性和效率，而且有助于管理不断增长的PPM在医疗保健中的数量。确保及时和准确地解决关键的病人沟通。
