PDF(Pubmed)
Abstract：
BACKGROUND: Clinical notes contain contextualized information beyond structured data related to patients\' past and current health status.
OBJECTIVE: This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data.
METHODS: Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors.
RESULTS: The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments.
CONCLUSIONS: The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.
OBJECTIVE: This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data.
METHODS: Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors.
RESULTS: The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments.
CONCLUSIONS: The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.
摘要：
背景：临床笔记包含与患者过去和当前健康状况相关的结构化数据之外的上下文信息。
目的：本研究旨在设计一种多模态深度学习方法，以提高使用入院临床记录和易于收集的表格数据对心力衰竭（HF）的医院结局的评估精度。
方法：多模态模型的开发和验证数据来自3个开放获取的美国数据库，包括2001年至2019年从教学医院收集的重症监护医学信息集市IIIv1.4（MIMIC-III）和MIMIC-IVv1.0，以及2014年至2015年从208家医院收集的eICU协作研究数据库v1.2。研究队列由所有患有严重HF的患者组成。临床笔记，包括主诉,目前的病史，体检,病史,和入院药物，以及记录在电子健康记录中的临床变量，进行了分析。我们开发了一种针对住院患者的深度学习死亡率预测模型，经历了完整的内部，prospective,和外部评估。采用综合梯度法和SHapley加法扩张法(SHAP)分析危险因素的重要性。
结果：该研究包括发展集中的9989名（16.4%）患者，内部验证集中的2497（14.1%）名患者，预期验证集中为1896（18.3%），和外部验证组中的7432名(15%)患者。模型的受试者工作特征曲线下面积为0.838（95％CI0.827-0.851），0.849（95%CI0.841-0.856），和0.767（95%CI0.762-0.772），对于内部，prospective,和外部验证集，分别。在所有测试集中，多峰模型的接收器工作特性曲线下的面积优于单峰模型，和表格数据导致了更高的歧视。在早期评估中，病史和体格检查比其他因素更有用。
结论：合并入院记录和临床表格数据的多模式深度学习模型显示，作为评估HF患者死亡风险的潜在新方法，具有良好的疗效。提供更准确、更及时的决策支持。
目的：本研究旨在设计一种多模态深度学习方法，以提高使用入院临床记录和易于收集的表格数据对心力衰竭（HF）的医院结局的评估精度。
方法：多模态模型的开发和验证数据来自3个开放获取的美国数据库，包括2001年至2019年从教学医院收集的重症监护医学信息集市IIIv1.4（MIMIC-III）和MIMIC-IVv1.0，以及2014年至2015年从208家医院收集的eICU协作研究数据库v1.2。研究队列由所有患有严重HF的患者组成。临床笔记，包括主诉,目前的病史，体检,病史,和入院药物，以及记录在电子健康记录中的临床变量，进行了分析。我们开发了一种针对住院患者的深度学习死亡率预测模型，经历了完整的内部，prospective,和外部评估。采用综合梯度法和SHapley加法扩张法(SHAP)分析危险因素的重要性。
结果：该研究包括发展集中的9989名（16.4%）患者，内部验证集中的2497（14.1%）名患者，预期验证集中为1896（18.3%），和外部验证组中的7432名(15%)患者。模型的受试者工作特征曲线下面积为0.838（95％CI0.827-0.851），0.849（95%CI0.841-0.856），和0.767（95%CI0.762-0.772），对于内部，prospective,和外部验证集，分别。在所有测试集中，多峰模型的接收器工作特性曲线下的面积优于单峰模型，和表格数据导致了更高的歧视。在早期评估中，病史和体格检查比其他因素更有用。
结论：合并入院记录和临床表格数据的多模式深度学习模型显示，作为评估HF患者死亡风险的潜在新方法，具有良好的疗效。提供更准确、更及时的决策支持。
