Goal-Directed Trajectory Planning of Therapeutic Pathways for Septic Shock Patients Using Fuzzy Granules-Oriented Maps

重症监护病房最常见的入院原因是败血症或败血症1，它会产生脓毒性休克2，这也是一个经常导致多器官衰竭后死亡的过程。脓毒症的机制不仅影响身体感染或触发“侮辱”发生的区域，还会引发一连串的炎症和小血管中不适当的血液凝固，从而扩散到全身，损害许多身体。这段时间最需要支持的两个器官系统是呼吸系统和心血管系统。为了解决这一迫切需要，揭示与呼吸机/患者相互作用和感染性休克治疗相关的潜在现象，需要一个综合的研究策略。因此，该项目的目的是通过首次整合呼吸和心血管功能的各种类型的“知识节点”，“动态”绘制（预测）脓毒症急性期患者的临床状态。这些节点将结合由生理学驱动的机制模型、通过实验数据得出的数据驱动模型、来自临床专家的语言知识以及离散的不连续数据。此动态图表（地图）中包含的信息将特定于患者订阅的治疗方法，但不会针对患者，因为所包含的信息的混合性质将使其自动泛化患者内部和患者间参数可变性后的属性。最终，这些信息将用于设计一个集成的智能决策支持系统，该系统能够合并（融合）各种类型的知识和多源数据，以进行适当和有效的治疗。该系统将基于一个完整的病人建模方法，从病人入院前的病史到随后的逐个临床数据，直到他/她最终出院。当收集到新的患者数据时，患者混合模型将使用“增量学习”策略进行动态更新，该策略仅使用“新”知识补充当前模型信息，而不会破坏原始的优化旧模型。此外，通过在线学习改进决策支持系统，分别对治疗决策的好/坏进行奖惩机制，并进一步借鉴其他类似情况患者的经验。

The most common cause of admission to the intensive care unit is septicaemia or sepsis1, which produces septic shock2, which is also a process that often results in death that follows multi-organ failure. The mechanism of sepsis affects not just the area of the body where infection or a triggering 'insult' occurs, but triggers a cascade of inflammation and inappropriate blood clotting in the small vessels, that can spread throughout the body damaging many body. The two organs systems that typically need most support during this time are the respiratory and cardiovascular systems. In order to address this pressing need to unravel the underlying phenomena associated with ventilator/patient interactions and septic shock treatment there is need for an integrated research strategy. Hence, the aim of this project is to 'dynamically' chart (predict) the clinical state of patients during the acute phase of sepsis by integrating for the first time various types of 'knowledge nodes' from respiratory and cardiovascular functions. Such nodes will combine mechanistic models driven by physiology, data-driven models elicited via experimental data, linguistic knowledge emanating from clinical experts, and discrete discontinuous data. The information included in this dynamic chart (map) will be specific to the treatment therapies subscribed to the patients but will not be patient-specific since the hybrid nature of the information included will lend itself automatically to generalising properties following intra and inter patient parameter variability. Ultimately, this information will be used to design an integrated intelligent decision support system that is able to merge (fuse) the various types of knowledge and multi-source data for appropriate and effective therapy. The system will be based on a through patient modelling approach from the patient's history prior to being admitted to hospital to beat-to-beat clinical data subsequently, until his/her final discharge from hospital. As new patient data is gathered the patient hybrid model will be updated dynamically using an 'incremental learning' strategy which consists of only supplementing the current model information with the 'new' knowledge without disrupting the original optimised old model. In addition, the decision support system is improved through on-line learning with the reward/punishment scheme for good/bad therapy decisions respectively while drawing further experiences with other patients with similar conditions.