基于自然语言处理的肿瘤专科病历质控系统建设

目的/意义 通过建立电子病历内涵质控系统,实现病历书写标准化与规范化,提高医院病历质量。方法/过程 基于医院医疗数据搭建智能中台,结合自然语言处理、机器学习技术形成具有肿瘤专科特色的知识库、规则库,实现电子病历“前置审核、全面覆盖、过程监管、闭环管理”的全新质控模式。结果/结论 应用基于自然语言处理的肿瘤专科病历质控系统后,质控覆盖率由1%提升至100%,甲级病案率提升至96%以上,具有较好的实时性与准确率,为医院病历高质量发展奠定坚实的信息化基础。     

HARVEST,a longitudinal patient record summarizer

Objective To describe HARVEST, a novel point-of-care patient summarization and visualization tool, and to conduct a formative evaluation study to assess its effectiveness and gather feedback for iterative improvements.Materials and methods HARVEST is a problem-based, interactive, temporal visualization of longitudinal patient records. Using scalable, distributed natural language processing and problem salience computation, the system extracts content from the patient notes and aggregates and presents information from multiple care settings. Clinical usability was assessed with physician participants using a timed, task-based chart review and questionnaire, with performance differences recorded between conditions (standard data review system and HARVEST).Results HARVEST displays patient information longitudinally using a timeline, a problem cloud as extracted from notes, and focused access to clinical documentation. Despite lack of familiarity with HARVEST, when using a task-based evaluation, performance and time-to-task completion was maintained in patient review scenarios using HARVEST alone or the standard clinical information system at our institution. Subjects reported very high satisfaction with HARVEST and interest in using the system in their daily practice.Discussion HARVEST is available for wide deployment at our institution. Evaluation provided informative feedback and directions for future improvements.Conclusions HARVEST was designed to address the unmet need for clinicians at the point of care, facilitating review of essential patient information. The deployment of HARVEST in our institution allows us to study patient record summarization as an informatics intervention in a real-world setting. It also provides an opportunity to learn how clinicians use the summarizer, enabling informed interface and content iteration and optimization to improve patient care.     

Effects of personal identifier resynthesis on clinical text de-identification

Objective

De-identified medical records are critical to biomedical research. Text de-identification software exists, including “resynthesis” components that replace real identifiers with synthetic identifiers. The goal of this research is to evaluate the effectiveness and examine possible bias introduced by resynthesis on de-identification software.

Design

We evaluated the open-source MITRE Identification Scrubber Toolkit, which includes a resynthesis capability, with clinical text from Vanderbilt University Medical Center patient records. We investigated four record classes from over 500 patients'' files, including laboratory reports, medication orders, discharge summaries and clinical notes. We trained and tested the de-identification tool on real and resynthesized records.

Measurements

We measured performance in terms of precision, recall, F-measure and accuracy for the detection of protected health identifiers as designated by the HIPAA Safe Harbor Rule.

Results

The de-identification tool was trained and tested on a collection of real and resynthesized Vanderbilt records. Results for training and testing on the real records were 0.990 accuracy and 0.960 F-measure. The results improved when trained and tested on resynthesized records with 0.998 accuracy and 0.980 F-measure but deteriorated moderately when trained on real records and tested on resynthesized records with 0.989 accuracy 0.862 F-measure. Moreover, the results declined significantly when trained on resynthesized records and tested on real records with 0.942 accuracy and 0.728 F-measure.

Conclusion

The de-identification tool achieves high accuracy when training and test sets are homogeneous (ie, both real or resynthesized records). The resynthesis component regularizes the data to make them less “realistic,” resulting in loss of performance particularly when training on resynthesized data and testing on real data.     

基于BERT的电子病历实体关系联合抽取研究

分析中文电子病历数据实体关系提取常用方法,提出一种基于双向编码器表征的实体关系联合抽取算法,使用级联解码器以及指针标注方法完成实体关系抽取及实体识别,实验结果证明该方法可有效抽取电子病历实体关系。     

Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions

Background

Electronic health record (EHR) users must regularly review large amounts of data in order to make informed clinical decisions, and such review is time-consuming and often overwhelming. Technologies like automated summarization tools, EHR search engines and natural language processing have been shown to help clinicians manage this information.

Objective

To develop a support vector machine (SVM)-based system for identifying EHR progress notes pertaining to diabetes, and to validate it at two institutions.

Materials and methods

We retrieved 2000 EHR progress notes from patients with diabetes at the Brigham and Women''s Hospital (1000 for training and 1000 for testing) and another 1000 notes from the University of Texas Physicians (for validation). We manually annotated all notes and trained a SVM using a bag of words approach. We then used the SVM on the testing and validation sets and evaluated its performance with the area under the curve (AUC) and F statistics.

Results

The model accurately identified diabetes-related notes in both the Brigham and Women''s Hospital testing set (AUC=0.956, F=0.934) and the external University of Texas Faculty Physicians validation set (AUC=0.947, F=0.935).

Discussion

Overall, the model we developed was quite accurate. Furthermore, it generalized, without loss of accuracy, to another institution with a different EHR and a distinct patient and provider population.

Conclusions

It is possible to use a SVM-based classifier to identify EHR progress notes pertaining to diabetes, and the model generalizes well.     

缩微数字化：信息时代纸质病案管理的科学手段——兼谈中国人民解放军第二五一医院病案管理新模式

着眼于信息化条件下病案信息管理信息化、数字化、异质备份缩微化的＂三化＂发展趋势,以中国人民解放军第二五一医院病案管理经验为依托,介绍了医院纸质病案缩微数字化经验和医疗病案管理模式,研究了统筹管理电子病案与纸质病案方式,探讨了历史病案与电子病案纸质部分缩微数字化的新方法。     

Named entity recognition of follow-up and time information in 20 000 radiology reports

Objective

To develop a system to extract follow-up information from radiology reports. The method may be used as a component in a system which automatically generates follow-up information in a timely fashion.

Methods

A novel method of combining an LSP (labeled sequential pattern) classifier with a CRF (conditional random field) recognizer was devised. The LSP classifier filters out irrelevant sentences, while the CRF recognizer extracts follow-up and time phrases from candidate sentences presented by the LSP classifier.

Measurements

The standard performance metrics of precision (P), recall (R), and F measure (F) in the exact and inexact matching settings were used for evaluation.

Results

Four experiments conducted using 20 000 radiology reports showed that the CRF recognizer achieved high performance without time-consuming feature engineering and that the LSP classifier further improved the performance of the CRF recognizer. The performance of the current system is P=0.90, R=0.86, F=0.88 in the exact matching setting and P=0.98, R=0.93, F=0.95 in the inexact matching setting.

Conclusion

The experiments demonstrate that the system performs far better than a baseline rule-based system and is worth considering for deployment trials in an alert generation system. The LSP classifier successfully compensated for the inherent weakness of CRF, that is, its inability to use global information.     

面向淋巴水肿疾病的电子病历命名实体识别应用研究

目的/意义 探讨人工智能技术应用于淋巴水肿患者电子病历非结构化文本数据的关键实体识别问题。方法/过程 阐述样本稀缺背景下模型微调训练的解决方案,选取首都医科大学附属北京世纪坛医院淋巴外科既往收治患者594例为研究对象,依据临床医生标注的15种关键实体类别,微调GlobalPointer模型的预测层,借助其全局指针识别嵌套和非嵌套的关键实体。分析实验结果的准确性和临床应用可行性。结果/结论 微调后模型总体精准率、召回率和 Macro_F1均值分别为0.795、0.641和0.697,为淋巴水肿电子病历数据精准挖掘奠定基础。     

电子病历归档存储系统的设计与应用

随着医院信息系统的不断发展和深入应用,对病人信息的需求也越来越大.在信息化水平较高的医院里,实施电子病历更好地服务临床,提高效率,同时也为医院管理层以及医疗相关行业提供检索方法和手段.开发实施了基于医院信息系统全面实施下的电子病历归档存储系统,方便了医患及社会相关部门,解决了电子病历归档的一些问题,取得了良好效果.     

浅谈疗养电子病历系统的设计与实践

电子病历系统作为我院信息化建设的组成部分,是保证我院信息系统完整性的重要手段。本文就疗养电子病历的设计与实践,与大家共作探讨。     

基于NLP构建病历后结构化专病数据库探索与实践

目的/意义 建设基于结构化电子病历的专病数据库,提高专病数据库规范性和可用性,提高临床科研工作效率。方法/过程 采用模板化输入、自然语言处理等技术,将非结构化电子病历转化为结构化电子病历,基于结构化电子病历构建专病数据库。结果/结论 龙华医院基于结构化电子病历建设的银屑病专病数据库分中心,为临床科研人员提供结构化科研数据源,辅助提升分析效率;同时有效支撑上海申康 “基于多中心的银屑病专病大数据临床科研随访一体化平台”建设,有助于专病数据库高质量、规模化发展。     

电子病案发展过程中存在的一些问题

评述实施电子病案(EletronicMedicalRecord,简称EMR)亟需注意和解决的问题。问题包括:EMR的归属、医护人员的态度、EMR系统的选择、标准问题、投入与回报、安全与隐私、EMR实用功能的开发等。这些问题对EMR的应用和推广的影响程度不同,解决途径也不尽相同。     

电子病案实施过程中存在的问题

随着电子病案的广泛应用,它必将成为区域医疗卫生服务体系的重要工具。但目前在对电子病案实施过程中还存在着许多问题有待解决和完善,探讨如何促进电子病案健康发展,使其能更好地为全民健康保障体系服务。     

基于信息平台的电子病历质量管理新模式

针对电子病历使用过程中存在的问题,阐述了基于信息平台的电子病历质量管理新模式,使病历质量从原来的“终末管理”转变为“环节控制”,实现了“智能化、实时的、全过程”控制,从而全方位提升了病历的整体质量。     

基于WEB的电子病历质量管理系统设计与应用

由于电子病历质量管理目前处于开始阶段,对其复杂性认识不全,现有的电子病历系统本身缺乏配套规范,致使一些不恰当处理可能会带来安全隐患,通过电子病历质量与安全管理,电子病历的表面和内在缺陷明显降低,病历质量大幅度提高,大量减少医疗环节错误,从而提高医疗水平、减少医患纠纷。所以电子病历的质量和安全是电子病历建设的重要部分,本项目在已经建立电子病历基础上,在技术上基于web运行,使用j2EE（Java2 Platform Enterprise Edition）框架,研发电子病历质量标准化管理与控制的应用。     

Functional evaluation of out-of-the-box text-mining tools for data-mining tasks

Objective The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. In this paper, we quantify this trade-off among text processing systems that make different trade-offs between speed and linguistic understanding. We tested both types of systems in three clinical research tasks: phase IV safety profiling of a drug, learning adverse drug–drug interactions, and learning used-to-treat relationships between drugs and indications.Materials We first benchmarked the accuracy of the NCBO Annotator and REVEAL in a manually annotated, publically available dataset from the 2008 i2b2 Obesity Challenge. We then applied the NCBO Annotator and REVEAL to 9 million clinical notes from the Stanford Translational Research Integrated Database Environment (STRIDE) and used the resulting data for three research tasks.Results There is no significant difference between using the NCBO Annotator and REVEAL in the results of the three research tasks when using large datasets. In one subtask, REVEAL achieved higher sensitivity with smaller datasets.Conclusions For a variety of tasks, employing simple term recognition methods instead of advanced NLP methods results in little or no impact on accuracy when using large datasets. Simpler dictionary-based methods have the advantage of scaling well to very large datasets. Promoting the use of simple, dictionary-based methods for population level analyses can advance adoption of NLP in practice.     

支持自然语言智能答疑系统的设计与实现

通过分析国内现有智能答疑系统存在的问题,提出了一个智能答疑系统的模型,并给出系统中的关键实现技术,同时指出支持自然语言提问的智能答疑系统是网络教育的发展方向。