Treatment pathways are step-by-step plans outlining the recommended medical care for specific diseases; they get revised when different treatments are found to improve patient outcomes. Examining health records is an important part of this revision process, but inferring patients' actual treatments from health data is challenging due to complex event-coding schemes and the absence of pathway-related annotations. This study aims to infer the actual treatment steps for a particular patient group from administrative health records (AHR) - a common form of tabular healthcare data - and address several technique- and methodology-based gaps in treatment pathway-inference research. We introduce Defrag, a method for examining AHRs to infer the real-world treatment steps for a particular patient group. Defrag learns the semantic and temporal meaning of healthcare event sequences, allowing it to reliably infer treatment steps from complex healthcare data. To our knowledge, Defrag is the first pathway-inference method to utilise a neural network (NN), an approach made possible by a novel, self-supervised learning objective. We also developed a testing and validation framework for pathway inference, which we use to characterise and evaluate Defrag's pathway inference ability and compare against baselines. We demonstrate Defrag's effectiveness by identifying best-practice pathway fragments for breast cancer, lung cancer, and melanoma in public healthcare records. Additionally, we use synthetic data experiments to demonstrate the characteristics of the Defrag method, and to compare Defrag to several baselines where it significantly outperforms non-NN-based methods. Defrag significantly outperforms several existing pathway-inference methods and offers an innovative and effective approach for inferring treatment pathways from AHRs. Open-source code is provided to encourage further research in this area.

翻译：治疗路径是针对特定疾病推荐医疗护理的分步计划；当发现不同疗法能改善患者预后时，这些路径会进行修订。审查健康记录是该修订过程的重要环节，但由于复杂的事件编码方案以及缺乏路径相关标注，从健康数据中推断患者的实际治疗过程颇具挑战性。本研究旨在从行政健康记录（AHR）——一种常见的表格化医疗数据——中推断特定患者群体的实际治疗步骤，并填补治疗路径推断研究中若干技术与方法论层面的空白。我们提出Defrag方法，用于分析AHR并推断特定患者群体的真实世界治疗步骤。Defrag能够学习医疗事件序列的语义和时序含义，从而从复杂的医疗数据中可靠地推断治疗步骤。据我们所知，Defrag是首个利用神经网络（NN）的路径推断方法，这一突破得益于一种新颖的自监督学习目标。我们还开发了一套用于路径推断的测试与验证框架，据此描述并评估Defrag的路径推断能力，并与基线方法进行比较。通过在公共医疗记录中识别乳腺癌、肺癌和黑色素瘤的最佳实践路径片段，我们证明了Defrag的有效性。此外，我们使用合成数据实验展示了Defrag方法的特性，并将其与多个基线方法进行对比，结果Defrag显著优于非神经网络方法。Defrag在多项现有路径推断方法中表现优异，为从行政健康记录中推断治疗路径提供了创新且有效的方案。我们提供开源代码，以促进该领域的进一步研究。