Background Accurate estimation of daily dosage and duration of medication use is essential to pharmacoepidemiological studies using electronic healthcare databases. However, such information is not directly available in many prescription databases, including the Swedish Prescribed Drug Register. Objective To develop and validate an algorithm for predicting prescribed daily dosage and treatment duration from free-text prescriptions, and apply the algorithm to ADHD medication prescriptions. Methods We developed an algorithm to predict daily dosage from free-text prescriptions using 8000 ADHD medication prescriptions as the training sample, and estimated treatment periods while taking into account several features including titration, stockpiling and non-perfect adherence. The algorithm was implemented to all ADHD medication prescriptions from the Swedish Prescribed Drug Register in 2013. A validation sample of 1000 ADHD medication prescriptions, independent of the training sample, was used to assess the accuracy for predicted daily dosage. Findings In the validation sample, the overall accuracy for predicting daily dosage was 96.8%. Specifically, the natural language processing model (NLP1 and NLP2) have an accuracy of 99.2% and 96.3%, respectively. In an application to ADHD medication prescriptions in 2013, young adult ADHD medication users had the highest probability of discontinuing treatments as compared with other age groups. The daily dose of methylphenidate use increased with age substantially. Conclusions The algorithm provides a flexible approach to estimate prescribed daily dosage and treatment duration from free-text prescriptions using register data. The algorithm showed a good performance for predicting daily dosage in external validation. Clinical implications The structured output of the algorithm could serve as basis for future pharmacoepidemiological studies evaluating utilization, effectiveness, and safety of medication use, which would facilitate evidence-based treatment decision-making. Data may be obtained from a third party and are not publicly available. The electronic health records record patient identifiable information and therefore cannot be shared publicly. The data can be used and reused by applying via Socialstyrelsen ()

中文翻译：

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从自由文本处方预测治疗剂量和持续时间：在瑞典处方药登记册中对 ADHD 药物的应用

背景 准确估计每日用药剂量和用药持续时间对于使用电子医疗保健数据库进行药物流行病学研究至关重要。然而，这些信息在许多处方数据库中并不直接可用，包括瑞典处方药注册。目的 开发和验证从自由文本处方中预测每日处方剂量和治疗持续时间的算法，并将该算法应用于 ADHD 药物处方。方法 我们开发了一种算法，使用 8000 个 ADHD 药物处方作为训练样本，从自由文本处方中预测每日剂量，并在考虑滴定、储存和非完美依从性等几个特征的同时估计治疗时间。该算法于 2013 年应用于瑞典处方药登记处的所有 ADHD 药物处方。独立于训练样本的 1000 个 ADHD 药物处方的验证样本用于评估预测每日剂量的准确性。结果 在验证样本中，预测每日剂量的总体准确率为 96.8%。具体来说，自然语言处理模型（NLP1 和 NLP2）的准确率分别为 99.2% 和 96.3%。在 2013 年的 ADHD 药物处方申请中，与其他年龄组相比，年轻成人 ADHD 药物使用者停止治疗的可能性最高。哌醋甲酯的日剂量随着年龄的增长而显着增加。结论 该算法提供了一种灵活的方法来使用注册数据从自由文本处方中估计规定的每日剂量和治疗持续时间。该算法在外部验证中显示了预测每日剂量的良好性能。临床意义 该算法的结构化输出可以作为未来药物流行病学研究评估药物使用的利用率、有效性和安全性的基础，这将有助于基于证据的治疗决策。数据可能从第三方获得，并且不公开。电子健康记录记录患者身份信息，因此不能公开共享。通过 Socialstyrelsen ( 该算法在外部验证中显示了预测每日剂量的良好性能。临床意义 该算法的结构化输出可以作为未来药物流行病学研究评估药物使用的利用率、有效性和安全性的基础，这将有助于基于证据的治疗决策。数据可能从第三方获得，并且不公开。电子健康记录记录患者身份信息，因此不能公开共享。通过 Socialstyrelsen ( 该算法在外部验证中显示了预测每日剂量的良好性能。临床意义 该算法的结构化输出可以作为未来药物流行病学研究评估药物使用的利用率、有效性和安全性的基础，这将有助于基于证据的治疗决策。数据可能从第三方获得，并且不公开。电子健康记录记录患者身份信息，因此不能公开共享。通过 Socialstyrelsen ( 这将有助于基于证据的治疗决策。数据可能从第三方获得，并且不公开。电子健康记录记录患者身份信息，因此不能公开共享。通过 Socialstyrelsen ( 这将有助于基于证据的治疗决策。数据可能从第三方获得，并且不公开。电子健康记录记录患者身份信息，因此不能公开共享。通过 Socialstyrelsen ()