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An Intelligent SPN Dialogue Model for Extracting Non-Measurable Pathological Symptoms

机译：提取不可测量的病理症状的智能SPN对话模型

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摘要

The increasing occurrence of chronic conditions among the ageing population and people at risk is one of the major challenges for our society and the high cost for its healthcare systems. Prevention, early detection and efficient management of chronic, long-term conditions contribute radically to the individual wellbeing and the economic sustainability of social and healthcare systems. In response to this need, this paper offers a human-machine interaction (HMI) model using Stochastic Petri Nets (SPNs). This HMI is based on a dialogue model between a virtual medical doctor and a patient for the efficient extraction of non-measurable pathological symptoms. Thus, the goal of such a model is the improvement of life critical situations and long delays (or appointments) for certain categories of people in need, like the elderly or people with disabilities.
机译：人口老龄化和高危人群中慢性病的发生率不断增加，这是对我们社会的主要挑战之一，也是其医疗保健系统的高昂费用。对慢性长期病的预防，早期发现和有效管理从根本上促进了个人福祉以及社会和医疗系统的经济可持续性。为了满足这种需求，本文提供了使用随机Petri网（SPN）的人机交互（HMI）模型。该HMI基于虚拟医生和患者之间的对话模型，用于有效提取不可测量的病理症状。因此，这种模型的目标是改善生活中的危急状况，并改善某些类别的有需要的人（如老人或残疾人）的长期延误（或任命）。

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