利用电子病历的自然语言处理技术对抗癌药物进行上市后监测。

Post-marketing surveillance of anticancer drugs using natural language processing of electronic medical records.

作者信息

Kawazoe Yoshimasa, Shimamoto Kiminori, Seki Tomohisa, Tsuchiya Masami, Shinohara Emiko, Yada Shuntaro, Wakamiya Shoko, Imai Shungo, Hori Satoko, Aramaki Eiji

机构信息

Artificial Intelligence and Digital Twin in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan.

出版信息

NPJ Digit Med. 2024 Nov 9;7(1):315. doi: 10.1038/s41746-024-01323-1.

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.

本研究表明，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已知AE发生频率。利用一家医院44502例癌症患者的数据，我们确定了开具抗癌药物（铂类，PLT；紫杉烷类，TAX；嘧啶类，PYA）的病例，并使用倾向得分匹配将其与未治疗（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；口腔黏膜炎，OM；味觉异常，TA；食欲减退，AL）。抗癌药物的风险比（HR）为：PN，1.15 - 1.95；OM，3.11 - 3.85；TA，3.48 - 4.71；AL，1.98 - 3.84；这些HR显著高于NTx组。敏感性分析显示，TA的HR可能被低估；然而，通过NLP从临床文本中提取的其余三种类型的AE与三种抗癌药物始终相关。