An NLP Approach to Generating Patient Record Summaries

生成患者记录摘要的 NLP 方法

基本信息

批准号：
7635002
负责人：
NOEMIE ELHADAD
金额：
$ 45.56万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-15 至 2011-09-14
项目状态：
已结题

项目摘要

: The long-term goal of this proposal is to enhance the manner in which physicians access, process and marshal medical information by providing them with an automatically generated, comprehensive, and up-to date summary of the information appearing in a patient record. At the point of patient care, physicians must often rapidly process a potentially overwhelming quantity of information pertaining to a patient. Failure to do so effectively may lead to provision of suboptimal care. Some electronic health record systems provide an automatically produced “cover sheet” geared to help physicians with a broad overview of a given patient, but the information is derived from the structured data fields in the patient record, ignoring the valuable narrative text entered by clinicians over time. We are building upon our prior work in summarization and natural language processing and leveraging our expertise in cognitive research studying information needs and decision making of clinicians to build a patient record summarizer that gathers information narrative (unstructured) as well as structured parts in the record. We focus on producing a summary for patients with kidney disease, as they often have a complex medical history with numerous conditions, procedures and medications. Providing a holistic, up-to-date summary of their chart would prove valuable to physicians in general and nephrologists in particular. The following three aims will be carried out: (1) conduct a formative study to determine how physicians prioritize and mentally represent relevant information when reviewing a patient chart; (2) create a set of automated methods to select salient pieces of information in the patient record and organize them into a coherent summary; and (3) evaluate the efficacy, efficiency and physician-user satisfaction associated with the use of the summarizer. A primary strength of this proposal is that we are addressing the problem of information overload, a bottleneck in the use of electronic health records, and evaluate the impact of our solution on clinicians’ actions and patients’ health outcomes. Furthermore, we propose to use novel natural language processing, knowledge-based and data mining methods to extract and organize salient information. Finally, we contribute to informatics research by extending the electronic health record functionalities to go beyond a simple documentation-entry system towards a useful reference and decision-making tool for physicians

：该提案的长期目标是提高医生获取、处理和处理信息的方式通过向他们提供自动生成的、全面的、最新的患者记录中出现的信息摘要来整理医疗信息在患者护理时，医生通常必须快速处理与患者有关的潜在大量信息。如果不能有效地做到这一点，可能会导致提供次优的护理。一些电子健康记录系统提供自动生成的“封面表”，旨在帮助医生全面了解特定患者，但该信息来自结构化数据字段。在患者记录中，忽略了患者输入的有价值的叙述文本随着时间的推移，我们正在总结和自然语言处理方面的先前工作基础上，并利用我们在研究信息需求和冠军决策方面的认知研究专业知识来构建患者记录摘要器，以收集信息叙述（非结构化）以及结构化部分。我们专注于为肾病患者提供一份总结，因为他们通常有复杂的病史，有多种病情、手术和药物，提供全面、最新的图表总结对于医生来说很有价值。普通科和肾脏病专家具体而言，将实现以下三个目标：（1）进行形成性研究，以确定医生在审查患者图表时如何优先考虑并在心理上表示相关信息；（2）创建一套自动化方法来选择其中的显着信息。患者记录并将其组织成连贯的摘要；以及（3）评估与摘要器的使用相关的功效、效率和医生用户满意度。该提案的主要优点是我们正在解决信息过载的问题。电子健康记录使用的瓶颈，并评估此外，我们建议使用新颖的自然语言处理、基于知识的数据挖掘方法来提取和组织显着信息，最后，我们通过扩展电子健康为信息学研究做出贡献。记录功能超越简单的文档输入系统，成为医生有用的参考和决策工具