Abstract：
BACKGROUND: ICU readmissions and post-discharge mortality pose significant challenges. Previous studies used EHRs and machine learning models, but mostly focused on structured data. Nursing records contain crucial unstructured information, but their utilization is challenging. Natural language processing (NLP) can extract structured features from clinical text. This study proposes the Crucial Nursing Description Extractor (CNDE) to predict post-ICU discharge mortality rates and identify high-risk patients for unplanned readmission by analyzing electronic nursing records.
OBJECTIVE: Developed a deep neural network (NurnaNet) with the ability to perceive nursing records, combined with a bio-clinical medicine pre-trained language model (BioClinicalBERT) to analyze the electronic health records (EHRs) in the MIMIC III dataset to predict the death of patients within six month and two year risk.
METHODS: A cohort and system development design was used.
METHODS: Based on data extracted from MIMIC-III, a database of critically ill in the US between 2001 and 2012, the results were analyzed.
METHODS: We calculated patients\' age using admission time and date of birth information from the MIMIC dataset. Patients under 18 or over 89 years old, or who died in the hospital, were excluded. We analyzed 16,973 nursing records from patients\' ICU stays.
METHODS: We have developed a technology called the Crucial Nursing Description Extractor (CNDE), which extracts key content from text. We use the logarithmic likelihood ratio to extract keywords and combine BioClinicalBERT. We predict the survival of discharged patients after six months and two years and evaluate the performance of the model using precision, recall, the F1-score, the receiver operating characteristic curve (ROC curve), the area under the curve (AUC), and the precision-recall curve (PR curve).
RESULTS: The research findings indicate that NurnaNet achieved good F1-scores (0.67030, 0.70874) within six months and two years. Compared to using BioClinicalBERT alone, there was an improvement in performance of 2.05 % and 1.08 % for predictions within six months and two years, respectively.
CONCLUSIONS: CNDE can effectively reduce long-form records and extract key content. NurnaNet has a good F1-score in analyzing the data of nursing records, which helps to identify the risk of death of patients after leaving the hospital and adjust the regular follow-up and treatment plan of relevant medical care as soon as possible.
OBJECTIVE: Developed a deep neural network (NurnaNet) with the ability to perceive nursing records, combined with a bio-clinical medicine pre-trained language model (BioClinicalBERT) to analyze the electronic health records (EHRs) in the MIMIC III dataset to predict the death of patients within six month and two year risk.
METHODS: A cohort and system development design was used.
METHODS: Based on data extracted from MIMIC-III, a database of critically ill in the US between 2001 and 2012, the results were analyzed.
METHODS: We calculated patients\' age using admission time and date of birth information from the MIMIC dataset. Patients under 18 or over 89 years old, or who died in the hospital, were excluded. We analyzed 16,973 nursing records from patients\' ICU stays.
METHODS: We have developed a technology called the Crucial Nursing Description Extractor (CNDE), which extracts key content from text. We use the logarithmic likelihood ratio to extract keywords and combine BioClinicalBERT. We predict the survival of discharged patients after six months and two years and evaluate the performance of the model using precision, recall, the F1-score, the receiver operating characteristic curve (ROC curve), the area under the curve (AUC), and the precision-recall curve (PR curve).
RESULTS: The research findings indicate that NurnaNet achieved good F1-scores (0.67030, 0.70874) within six months and two years. Compared to using BioClinicalBERT alone, there was an improvement in performance of 2.05 % and 1.08 % for predictions within six months and two years, respectively.
CONCLUSIONS: CNDE can effectively reduce long-form records and extract key content. NurnaNet has a good F1-score in analyzing the data of nursing records, which helps to identify the risk of death of patients after leaving the hospital and adjust the regular follow-up and treatment plan of relevant medical care as soon as possible.
摘要：
背景：ICU再入院和出院后死亡率构成重大挑战。以前的研究使用EHR和机器学习模型，但主要集中在结构化数据上。护理记录包含关键的非结构化信息，但是它们的使用具有挑战性。自然语言处理(NLP)可以从临床文本中提取结构化特征。这项研究提出了关键护理描述提取器（CNDE）来预测ICU出院后的死亡率，并通过分析电子护理记录来识别计划外再入院的高风险患者。
目的：开发了一种能够感知护理记录的深度神经网络（NurnaNet），结合生物临床医学预训练语言模型(BioClinicalBERT)分析MIMICIII数据集中的电子健康记录(EHR),以预测患者在6个月和2年内的死亡风险.
方法：采用队列和系统开发设计。
方法：基于从MIMIC-III中提取的数据，在2001年至2012年美国危重病数据库中,对结果进行了分析.
方法：我们使用MIMIC数据集的入院时间和出生日期信息计算患者年龄。18岁以下或89岁以上的患者，或是死在医院的人,被排除在外。我们分析了ICU住院患者的16,973份护理记录。
方法：我们开发了一种称为关键护理描述提取器（CNDE）的技术，从文本中提取关键内容。我们使用对数似然比来提取关键词并结合BioClinicalBERT。我们预测出院患者六个月和两年后的生存率，并使用精度评估模型的性能，召回，F1得分，接收器工作特性曲线(ROC曲线)，曲线下面积（AUC），和精度-召回曲线(PR曲线)。
结果：研究结果表明，NurnaNet在六个月和两年内获得了良好的F1得分（0.67030，0.70874）。与单独使用BioClinicalBERT相比，六个月和两年内的预测表现分别提高了2.05%和1.08%，分别。
结论：CNDE可以有效减少长格式记录并提取关键内容。NurnaNet在分析护理记录数据方面具有良好的F1评分，这有助于识别患者出院后的死亡风险，并尽快调整相关医疗的定期随访和治疗计划。
目的：开发了一种能够感知护理记录的深度神经网络（NurnaNet），结合生物临床医学预训练语言模型(BioClinicalBERT)分析MIMICIII数据集中的电子健康记录(EHR),以预测患者在6个月和2年内的死亡风险.
方法：采用队列和系统开发设计。
方法：基于从MIMIC-III中提取的数据，在2001年至2012年美国危重病数据库中,对结果进行了分析.
方法：我们使用MIMIC数据集的入院时间和出生日期信息计算患者年龄。18岁以下或89岁以上的患者，或是死在医院的人,被排除在外。我们分析了ICU住院患者的16,973份护理记录。
方法：我们开发了一种称为关键护理描述提取器（CNDE）的技术，从文本中提取关键内容。我们使用对数似然比来提取关键词并结合BioClinicalBERT。我们预测出院患者六个月和两年后的生存率，并使用精度评估模型的性能，召回，F1得分，接收器工作特性曲线(ROC曲线)，曲线下面积（AUC），和精度-召回曲线(PR曲线)。
结果：研究结果表明，NurnaNet在六个月和两年内获得了良好的F1得分（0.67030，0.70874）。与单独使用BioClinicalBERT相比，六个月和两年内的预测表现分别提高了2.05%和1.08%，分别。
结论：CNDE可以有效减少长格式记录并提取关键内容。NurnaNet在分析护理记录数据方面具有良好的F1评分，这有助于识别患者出院后的死亡风险，并尽快调整相关医疗的定期随访和治疗计划。
