...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>BMC Medical Informatics and Decision Making >Automatically identifying social isolation from clinical narratives for patients with prostate Cancer
【24h】

Automatically identifying social isolation from clinical narratives for patients with prostate Cancer

机译：从前列腺癌患者的临床叙述中自动识别社会隔离

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Medicine recently recommended that social isolation be documented in electronic health records (EHR). However, social isolation usually is not recorded or obtained as coded data but rather collected from patient self-report or documented in clinical narratives. This study explores the feasibility and effectiveness of natural language processing (NLP) strategy for identifying patients who are socially isolated from clinical narratives. We used data from the Medical University of South Carolina (MUSC) Research Data Warehouse. Patients 18?years-of-age or older who were diagnosed with prostate cancer between January 1, 2014 and May 31, 2017 were eligible for this study. NLP pipelines identifying social isolation were developed via extraction of notes on progress, history and physical, consult, emergency department provider, telephone encounter, discharge summary, plan of care, and radiation oncology. Of 4195 eligible prostate cancer patients, we randomly sampled 3138 patients (75%) as a training dataset. The remaining 1057 patients (25%) were used as a test dataset to evaluate NLP algorithm performance. Standard performance measures for the NLP algorithm, including precision, recall, and F-measure, were assessed by expert manual review using the test dataset. A total of 55,516 clinical notes from 3138 patients were included to develop the lexicon and NLP pipelines for social isolation. Of those, 35 unique patients (1.2%) had social isolation mention(s) in 217 notes. Among 24 terms relevant to social isolation, the most prevalent were “lack of social support,” “lonely,” “social isolation,” “no friends,” and “loneliness”. Among 1057 patients in the test dataset, 17 patients (1.6%) were identified as having social isolation mention(s) in 40 clinical notes. Manual review identified four false positive mentions of social isolation and one false negatives in 154 notes from randomly selected 52 controls. The NLP pipeline demonstrated 90% precision, 97% recall, and 93% F-measure. The major reasons for a false positive included the ambiguities of the experiencer of social isolation, negation, and alternate meaning of words. Our NLP algorithms demonstrate a highly accurate approach to identify social isolation.
机译：社会隔离是影响患者健康结果和死亡率的重要社会决定因素。美国国家医学科学院最近建议在电子健康记录（EHR）中记录社会隔离。但是，社会隔离通常不会被记录或获得为编码数据，而是从患者的自我报告中收集或记录在临床叙述中。这项研究探讨了自然语言处理（NLP）策略用于识别与临床叙述无关的患者的可行性和有效性。我们使用了南卡罗来纳医科大学（MUSC）研究数据仓库中的数据。在2014年1月1日至2017年5月31日期间被诊断患有前列腺癌的18岁或18岁以上的患者符合这项研究的条件。通过提取有关进展，历史和身体状况，咨询，急诊部门提供者，电话遭遇，出院摘要，护理计划和放射肿瘤学的注释，开发了识别社会隔离的NLP管道。在4195名合格的前列腺癌患者中，我们随机抽取3138名患者（占75％）作为训练数据集。其余1057名患者（占25％）用作测试数据集，以评估NLP算法的性能。 NLP算法的标准性能指标，包括精度，召回率和F指标，是通过使用测试数据集的专家手动审核进行评估的。纳入了3138名患者的55,516份临床记录，以开发用于社会隔离的词典和NLP管道。在这些患者中，有217个记录中有35个独特的患者（占1.2％）被提及具有社会隔离。在与社会隔离相关的24个术语中，最普遍的是“缺乏社会支持”，“孤独”，“社会隔离”，“没有朋友”和“孤独”。在测试数据集中的1057位患者中，有40位临床笔记中有17位患者（1.6％）被确定为具有社交隔离的提及。人工审查发现了来自随机选择的52个对照组的154个笔记中有4个对社会隔离的错误肯定提及和1个错误否定。 NLP管道显示出90％的精度，97％的召回率和93％的F量度。误报的主要原因包括社交孤立，否定和单词的其他含义的经历者的歧义。我们的NLP算法展示了一种高度准确的识别社会隔离的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号