Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system.
Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system.
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作者：
开发和测试一个可扩展、高性能和基于规则的模型，用于从电子健康记录(EHR)的非结构化数据中识别社会需求的三个主要领域(居住不稳定、食品不安全和交通问题)。我们纳入了在2016年7月至2021年6月期间在约翰霍普金斯卫生系统(JHHS)接受护理的18岁或18岁以上的患者，并在研究期间的EHR中至少有1条非结构化(自由文本)注释。我们使用了手动词典精选和半自动词典创建的组合来进行功能开发。我们开发了一个初步的基于规则的管道(Match Pipeline)，为每个社会需求领域使用2个关键字集。我们对不同的词典执行了基于规则的关键字匹配，并使用包含192名患者的注释数据集测试了算法。从一组专家识别的关键字开始，我们通过评估标签数据集中识别的假阳性和阴性来测试调整。我们使用精确度、召回率和F1分数来评估算法的性能。识别居住不稳定的算法具有最好的整体性能，识别无家可归患者的精确度、召回率和F1得分的加权平均值分别为0.92、0.84和0.92，识别住房不安全的患者的加权平均得分为0.84、0.82和0.79。食品不安全算法的指标很高，但交通问题算法的总体性能指标最低。JHHS在确定社会需求方面的NLP算法表现相对较好，将为在医疗保健系统中实施提供机会。在该项目中开发的NLP方法可以在医疗保健系统的常规数据流程中进行调整，并有可能实施。
To develop and test a scalable, performant, and rule-based model for identifying 3 major domains of social needs (residential instability, food insecurity, and transportation issues) from the unstructured data in electronic health records (EHRs). We included patients aged 18 years or older who received care at the Johns Hopkins Health System (JHHS) between July 2016 and June 2021 and had at least 1 unstructured (free-text) note in their EHR during the study period. We used a combination of manual lexicon curation and semiautomated lexicon creation for feature development. We developed an initial rules-based pipeline (Match Pipeline) using 2 keyword sets for each social needs domain. We performed rule-based keyword matching for distinct lexicons and tested the algorithm using an annotated dataset comprising 192 patients. Starting with a set of expert-identified keywords, we tested the adjustments by evaluating false positives and negatives identified in the labeled dataset. We assessed the performance of the algorithm using measures of precision, recall, and F1 score. The algorithm for identifying residential instability had the best overall performance, with a weighted average for precision, recall, and F1 score of 0.92, 0.84, and 0.92 for identifying patients with homelessness and 0.84, 0.82, and 0.79 for identifying patients with housing insecurity. Metrics for the food insecurity algorithm were high but the transportation issues algorithm was the lowest overall performing metric. The NLP algorithm in identifying social needs at JHHS performed relatively well and would provide the opportunity for implementation in a healthcare system. The NLP approach developed in this project could be adapted and potentially operationalized in the routine data processes of a healthcare system.
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影响因子：
4.1
作者：
Anzaldi LJ;Davison A;Boyd CM;Leff B;Kharrazi H
通讯作者：
Kharrazi H
DOI：
10.1016/j.jaci.2019.12.897
发表时间：
2020-02
期刊：
The Journal of allergy and clinical immunology
影响因子：
--
作者：
Juhn Y;Liu H
通讯作者：
Liu H
影响因子：
5.5
作者：
Hatef, Elham;Searle, Kelly M.;Weiner, Jonathan P.
通讯作者：
Weiner, Jonathan P.
影响因子：
3.4
作者：
Berkowitz, Seth A.;Basu, Sanjay;Seligman, Hilary K.
通讯作者：
Seligman, Hilary K.
影响因子：
5.7
作者：
Kushel, MB;Gupta, R;Haas, JS
通讯作者：
Haas, JS