Adverse Drug Events (ADEs), harmful medication effects, pose significant healthcare challenges, impacting patient safety and costs. This study evaluates automatic Knowledge-Driven Feature Engineering (aKDFE) for improved ADE prediction from Electronic Health Record (EHR) data, comparing it with automated event-based Knowledge Discovery in Databases (KDD). We investigated how incorporating domain-specific ADE risk scores for prolonged heart QT interval, extracted from the Janusmed Riskprofile (Janusmed) Clinical Decision Support System (CDSS), affects prediction performance using EHR data and medication handling events. Results indicate that, while aKDFE step 1 (event-based feature generation) alone did not significantly improve ADE prediction performance, aKDFE step 2 (patient-centric transformation) enhances the prediction performance. High Area Under the Receiver Operating Characteristic curve (AUROC) values suggest strong feature correlations to the outcome, aligning with the predictive power of patients' prior healthcare history for ADEs. Statistical analysis did not confirm that incorporating the Janusmed information (i) risk scores and (ii) medication route of administration into the model's feature set enhanced predictive performance. However, the patient-centric transformation applied by aKDFE proved to be a highly effective feature engineering approach. Limitations include a single-project focus, potential bias from machine learning pipeline methods, and reliance on AUROC. In conclusion, aKDFE, particularly with patient-centric transformation, improves ADE prediction from EHR data. Future work will explore attention-based models, event feature sequences, and automatic methods for incorporating domain knowledge into the aKDFE framework.

翻译：药物不良事件（ADEs）作为有害的药物治疗效应，对患者安全和医疗成本构成重大挑战，是医疗保健领域的重要难题。本研究评估了自动知识驱动特征工程（aKDFE）在利用电子健康记录（EHR）数据改进ADE预测方面的效果，并将其与基于事件的自动化数据库知识发现（KDD）方法进行比较。我们重点探究了如何将从Janusmed风险分析（Janusmed）临床决策支持系统（CDSS）中提取的、针对QT间期延长的特定领域ADE风险评分，结合EHR数据和药物处理事件，影响预测性能。结果表明：尽管aKDFE步骤1（基于事件的特征生成）本身未能显著提升ADE预测性能，但aKDFE步骤2（以患者为中心的转换）有效增强了预测表现。较高的受试者工作特征曲线下面积（AUROC）值表明特征与结果之间存在强相关性，这与患者既往医疗史对ADE的预测能力相一致。统计分析未能证实将Janusmed信息（i）风险评分和（ii）给药途径纳入模型特征集会提升预测性能。然而，aKDFE采用的以患者为中心的转换被证明是一种高效的特征工程方法。研究局限性包括单一项目焦点、机器学习流程方法可能带来的偏差以及对AUROC的依赖。综上所述，aKDFE，特别是结合以患者为中心的转换，能够改进基于EHR数据的ADE预测。未来工作将探索基于注意力的模型、事件特征序列以及将领域知识自动整合到aKDFE框架中的方法。