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Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks

机译:通过将计算机可解释的指南转换为分层任务网络,自动生成针对患者的电子护理路径

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摘要

OBJECTIVE: This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To this end, a translation from a time-annotated computer-interpretable guideline (CIG) model of a clinical protocol into a temporal hierarchical task network (HTN) planning domain is presented. MATERIALS AND METHODS: The proposed method uses a knowledge-driven reasoning process to translate knowledge previously described in a CIG into a corresponding HTN Planning and Scheduling domain, taking advantage of HTNs known ability to (i) dynamically cope with temporal and resource constraints, and (ii) automatically generate customized plans. The proposed method, focusing on the representation of temporal knowledge and based on the identification of workflow and temporal patterns in a CIG, makes it possible to automatically generate time-annotated and resource-based care pathways tailored to the needs of any possible patient profile. RESULTS: The proposed translation is illustrated through a case study based on a 70 pages long clinical protocol to manage Hodgkin's disease, developed by the Spanish Society of Pediatric Oncology. We show that an HTN planning domain can be generated from the corresponding specification of the protocol in the Asbru language, providing a running example of this translation. Furthermore, the correctness of the translation is checked and also the management of ten different types of temporal patterns represented in the protocol. By interpreting the automatically generated domain with a state-of-art HTN planner, a time-annotated care pathway is automatically obtained, customized for the patient's and institutional needs. The generated care pathway can then be used by clinicians to plan and manage the patients long-term care. CONCLUSION: The described methodology makes it possible to automatically generate patient-tailored care pathways, leveraging an incremental knowledge-driven engineering process that starts from the expert knowledge of medical professionals. The presented approach makes the most of the strengths inherent in both CIG languages and HTN planning and scheduling techniques: for the former, knowledge acquisition and representation of the original clinical protocol, and for the latter, knowledge reasoning capabilities and an ability to deal with complex temporal and resource constraints. Moreover, the proposed approach provides immediate access to technologies such as business process management (BPM) tools, which are increasingly being used to support healthcare processes.
机译:目的:本文介绍了一种方法,该方法可以在考虑到复杂的时间限制和机构资源分配的情况下,为特定医疗过程中个性化患者治疗的计划,可视化和执行提供计算机辅助支持。为此,提出了从临床协议的带时间注释的计算机可解释指南(CIG)模型到时间分层任务网络(HTN)规划域的转换。材料和方法:所提出的方法使用知识驱动的推理过程,将先前在CIG中描述的知识转换为相应的HTN规划和计划域,同时利用HTN的已知能力(i)动态应对时间和资源限制,并且(ii)自动生成定制计划。所提出的方法着重于时间知识的表示,并基于CIG中工作流程和时间模式的识别,使得能够自动生成针对任何可能的患者档案需求量身定制的带有时间注释和基于资源的护理途径。结果:由西班牙儿科肿瘤学会开发的基于70页长的用于管理霍奇金氏病的临床方案的案例研究说明了拟议的翻译。我们展示了可以从相应的Asbru语言协议规范中生成一个HTN规划域,并提供了该翻译的运行示例。此外,检查翻译的正确性,并检查协议中表示的十种不同类型的时间模式的管理。通过使用最新的HTN计划程序解释自动生成的域,可以自动获取时间标注的护理路径,并针对患者和机构的需求进行定制。然后,临床医生可以使用生成的护理路径来计划和管理患者的长期护理。结论:所描述的方法使利用医疗专业人员的专业知识开始的增量知识驱动的工程过程,可以自动生成针对患者的护理途径。所提出的方法充分利用了CIG语言和HTN规划和调度技术所固有的优势:对于前者,知识获取和原始临床协议的表示,对于后者,知识推理能力和处理复杂问题的能力时间和资源限制。此外,提出的方法可立即访问诸如业务流程管理(BPM)工具之类的技术,这些技术正越来越多地用于支持医疗保健流程。

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