@文章{信息:doi/10.2196/26777，作者=“Lu, Zhaohua和Sim, Jin-ah和Wang, Jade X和Forrest, Christopher B和Krull, Kevin R和Srivastava, Deokumar和Hudson, Melissa M和Robison, Leslie L和Baker, Justin N和Huang, I-Chan”，标题=“描述非结构化患者报告结果的自然语言处理和机器学习方法:，期刊="J Med Internet Res"，年="2021"，月="11"，日="3"，卷="23"，数="11"，页数="e26777"，关键词="自然语言处理";机器学习;优点;背景:通过临床接触中的访谈或对话来评估患者报告的结果(PROs)，可以提供关于生存率的深刻信息。目的:本研究旨在测试自然语言处理(NLP)和机器学习(ML)算法在识别儿童和青少年癌症幸存者所经历的疼痛干扰和疲劳症状的不同属性方面的有效性，而不是作为验证NLP/ML算法的金标准的PRO内容专家的判断。方法:这项横断面研究的重点是8至17岁的儿童和青少年癌症幸存者以及护理人员，从中生成了391个疼痛干扰域的意义单位和423个疲劳域的意义单位用于分析。数据来自圣裘德儿童研究医院治疗完成后诊所。通过深度访谈报告经历过的疼痛干扰和疲劳症状。逐字转录后，可分析的句子(即意义单位)由2名内容专家对每个属性(物理、认知、社会或未分类)进行语义标记。 Two NLP/ML methods were used to extract and validate the semantic features: bidirectional encoder representations from transformers (BERT) and Word2vec plus one of the ML methods, the support vector machine or extreme gradient boosting. Receiver operating characteristic and precision-recall curves were used to evaluate the accuracy and validity of the NLP/ML methods. Results: Compared with Word2vec/support vector machine and Word2vec/extreme gradient boosting, BERT demonstrated higher accuracy in both symptom domains, with 0.931 (95{\%} CI 0.905-0.957) and 0.916 (95{\%} CI 0.887-0.941) for problems with cognitive and social attributes on pain interference, respectively, and 0.929 (95{\%} CI 0.903-0.953) and 0.917 (95{\%} CI 0.891-0.943) for problems with cognitive and social attributes on fatigue, respectively. In addition, BERT yielded superior areas under the receiver operating characteristic curve for cognitive attributes on pain interference and fatigue domains (0.923, 95{\%} CI 0.879-0.997; 0.948, 95{\%} CI 0.922-0.979) and superior areas under the precision-recall curve for cognitive attributes on pain interference and fatigue domains (0.818, 95{\%} CI 0.735-0.917; 0.855, 95{\%} CI 0.791-0.930). Conclusions: The BERT method performed better than the other methods. As an alternative to using standard PRO surveys, collecting unstructured PROs via interviews or conversations during clinical encounters and applying NLP/ML methods can facilitate PRO assessment in child and adolescent cancer survivors. ", issn="1438-8871", doi="10.2196/26777", url="//www.mybigtv.com/2021/11/e26777", url="https://doi.org/10.2196/26777", url="http://www.ncbi.nlm.nih.gov/pubmed/34730546" }