Natural language processing for abstraction of cancer treatment toxicities: accuracy versus human experts.
Natural language processing for abstraction of cancer treatment toxicities: accuracy versus human experts.
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DOI：
10.1093/jamiaopen/ooaa064
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发表时间：
2020-12
期刊：
影响因子：
2.1
通讯作者：
Tenenbaum JD
中科院分区：
文献类型：
--
作者：
Hong JC;Fairchild AT;Tanksley JP;Palta M;Tenenbaum JD
急性毒性的专家提取在肿瘤学研究中至关重要，但劳动密集型且可变。我们评估了自然语言处理（NLP）管道与医生相比从临床笔记中提取症状的准确性。两名独立审查员从放射治疗期间随机选择的100份治疗访视记录中确定了当前和否定的国家癌症研究所不良事件通用术语标准（CTCAE）v5.0症状，并由第三名审查员裁定。开发了基于Apache临床文本分析知识提取系统的NLP管道，并用于提取CTCAE术语。通过精确度、召回率和F1评估准确度。NLP管道对常见的医生抽象症状表现出很高的准确性，如放射性皮炎（F1 0.88），疲劳（0.85）和恶心（0.88）。NLP对阴性症状的敏感性较差。NLP可准确检测记录的CTCAE症状子集，但仅限于阴性症状。它可能有助于制定在癌症治疗期间更一致地识别毒性的策略。
Expert abstraction of acute toxicities is critical in oncology research but is labor-intensive and variable. We assessed the accuracy of a natural language processing (NLP) pipeline to extract symptoms from clinical notes compared to physicians. Two independent reviewers identified present and negated National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 symptoms from 100 randomly selected notes for on-treatment visits during radiation therapy with adjudication by a third reviewer. A NLP pipeline based on Apache clinical Text Analysis Knowledge Extraction System was developed and used to extract CTCAE terms. Accuracy was assessed by precision, recall, and F1. The NLP pipeline demonstrated high accuracy for common physician-abstracted symptoms, such as radiation dermatitis (F1 0.88), fatigue (0.85), and nausea (0.88). NLP had poor sensitivity for negated symptoms. NLP accurately detects a subset of documented present CTCAE symptoms, though is limited for negated symptoms. It may facilitate strategies to more consistently identify toxicities during cancer therapy.
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DOI：
10.1136/jamia.2009.001560
发表时间：
2010-09-01
影响因子：
6.4
作者：
Savova, Guergana K.;Masanz, James J.;Chute, Christopher G.
通讯作者：
Chute, Christopher G.
DOI：
10.1093/jamia/ocy169
发表时间：
2019-04-01
影响因子：
6.4
作者：
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Basch, Ethan M.
影响因子：
3.7
作者：
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通讯作者：
Celi LA
影响因子：
11.2
作者：
Savova GK;Tseytlin E;Finan S;Castine M;Miller T;Medvedeva O;Harris D;Hochheiser H;Lin C;Chavan G;Jacobson RS
通讯作者：
Jacobson RS
影响因子：
19.7
作者：
Hripcsak, G;Austin, JHM;Friedman, C
通讯作者：
Friedman, C