Gnjidic Danijela, Pearson Sallie-Anne, Hilmer Sarah N, Basilakis Jim, Schaffer Andrea L, Blyth Fiona M, Banks Emily
Faculty of Pharmacy, University of Sydney, NSW, Australia; Sydney Medical School, University of Sydney, NSW, Australia
Faculty of Pharmacy, University of Sydney, NSW, Australia; Sydney School of Public Health, University of Sydney, NSW, Australia.
Public Health Res Pract. 2015 Mar 30;25(2):e2521518. doi: 10.17061/phrp2521518.
Increasingly, automated methods are being used to code free-text medication data, but evidence on the validity of these methods is limited.
To examine the accuracy of automated coding of previously keyed in free-text medication data compared with manual coding of original handwritten free-text responses (the 'gold standard').
A random sample of 500 participants (475 with and 25 without medication data in the free-text box) enrolled in the 45 and Up Study was selected. Manual coding involved medication experts keying in free-text responses and coding using Anatomical Therapeutic Chemical (ATC) codes (i.e. chemical substance 7-digit level; chemical subgroup 5-digit; pharmacological subgroup 4-digit; therapeutic subgroup 3-digit). Using keyed-in free-text responses entered by non-experts, the automated approach coded entries using the Australian Medicines Terminology database and assigned corresponding ATC codes.
Based on manual coding, 1377 free-text entries were recorded and, of these, 1282 medications were coded to ATCs manually. The sensitivity of automated coding compared with manual coding was 79% (n = 1014) for entries coded at the exact ATC level, and 81.6% (n = 1046), 83.0% (n = 1064) and 83.8% (n = 1074) at the 5, 4 and 3-digit ATC levels, respectively. The sensitivity of automated coding for blank responses was 100% compared with manual coding. Sensitivity of automated coding was highest for prescription medications and lowest for vitamins and supplements, compared with the manual approach. Positive predictive values for automated coding were above 95% for 34 of the 38 individual prescription medications examined.
Automated coding for free-text prescription medication data shows very high to excellent sensitivity and positive predictive values, indicating that automated methods can potentially be useful for large-scale, medication-related research.
越来越多地使用自动化方法对自由文本药物数据进行编码，但关于这些方法有效性的证据有限。
将先前键入的自由文本药物数据的自动编码准确性与原始手写自由文本回复的人工编码（“金标准”）进行比较。
从参与“45岁及以上研究”的500名参与者中随机抽取样本（475名在自由文本框中有药物数据，25名没有）。人工编码由药物专家键入自由文本回复并使用解剖学治疗化学（ATC）代码进行编码（即化学物质7位级别；化学亚组5位；药理亚组4位；治疗亚组3位）。使用非专家键入的自由文本回复，自动化方法使用澳大利亚药品术语数据库对条目进行编码并分配相应的ATC代码。
基于人工编码，记录了1377条自由文本条目，其中1282种药物被人工编码为ATC。与人工编码相比，自动编码在精确ATC级别编码条目的灵敏度为79%（n = 1014），在ATC的5位、4位和3位级别分别为81.6%（n = 1046）、83.0%（n = 1064）和83.8%（n = 1074）。与人工编码相比，自动编码对空白回复的灵敏度为100%。与人工方法相比，自动编码对处方药的灵敏度最高，对维生素和补充剂的灵敏度最低。在所检查的38种单独处方药中，34种的自动编码阳性预测值高于95%。
自由文本处方药数据的自动编码显示出非常高到优异的灵敏度和阳性预测值，表明自动方法可能对大规模药物相关研究有用。
