This study demonstrates that adverse events (AEs) extracted using natural language processing (NLP) from clinical texts reflect the known frequencies of AEs associated with anticancer drugs. Using data from 44,502 cancer patients at a single hospital, we identified cases prescribed anticancer drugs (platinum, PLT; taxane, TAX; pyrimidine, PYA) and compared them to non-treatment (NTx) group using propensity score matching. Over 365 days, AEs (peripheral neuropathy, PN; oral mucositis, OM; taste abnormality, TA; appetite loss, AL) were extracted from clinical text using an NLP tool. The hazard ratios (HRs) for the anticancer drugs were: PN, 1.15–1.95; OM, 3.11–3.85; TA, 3.48-4.71; and AL, 1.98–3.84; the HRs were significantly higher than that of the NTx group. Sensitivity analysis revealed that the HR for TA may have been underestimated; however, the remaining three types of AEs extracted from clinical text by NLP were consistently associated with the three anticancer drugs.

这项研究表明，使用自然语言处理 （NLP） 从临床文本中提取的不良事件 （AE） 反映了与抗癌药物相关的 AE 的已知频率。使用来自一家医院的 44,502 名癌症患者的数据，我们确定了开具抗癌药物 （铂类、PLT、紫杉烷、TAX、嘧啶、PYA） 的病例，并使用倾向评分匹配将它们与非治疗 （NTx） 组进行比较。在 365 天内，使用 NLP 工具从临床文本中提取 AE （周围神经病变，PN;口腔粘膜炎，OM;味觉异常，TA;食欲不振，AL）。抗癌药物的风险比 （HR） 为： PN，1.15-1.95;OM，3.11-3.85;技术分析，3.48-4.71;和 AL，1.98-3.84;HRs 显著高于 NTx 组。敏感性分析显示，TA 的 HR 可能被低估了;然而，NLP 从临床文本中提取的其余 3 种类型的 AEs 与三种抗癌药物一致相关。