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

使用模糊颗粒导向图对感染性休克患者的治疗途径进行目标导向轨迹规划

• 批准号: EP/F02889X/1
• 负责人: Mahdi Mahfouf
• 金额: $ 39.92万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF02889X%2F1
• 关键词: Goal; Directed; Trajectory; Planning; Therapeutic

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.

进入重症监护室的最常见原因是败血症或脓毒症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感染或触发“侮辱”发生的身体区域，而且会引发小血管中的炎症和不适当的血液凝固级联反应，这些炎症和血液凝固可以扩散到整个身体，损害许多身体。在这段时间里，通常需要最多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解开与呼吸机/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联合收割机机械模型、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。该动态图表（图）中包含的信息将特定于患者订阅的治疗疗法，但不是患者特异性的，因为所包含信息的混合性质将自动适用于患者内和患者间参数变异性后的一般化属性。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。该系统将基于从患者入院前的病史到随后的逐搏临床数据，直到他/她最终出院的贯穿患者建模方法。随着新患者数据的收集，患者混合模型将使用“增量学习”策略动态更新，该策略仅包括用“新”知识补充当前模型信息，而不破坏原始优化的旧模型。此外，决策支持系统是通过在线学习与奖励/惩罚计划分别为好/坏的治疗决策，同时进一步借鉴经验，与其他患者类似的条件。

项目成果

Interval type-2 fuzzy sets for Self-Organising Fuzzy Logic based control with on-line PSO Optimisation

用于基于自组织模糊逻辑的在线 PSO 优化控制的间隔类型 2 模糊集

• DOI: 10.1109/fuzz-ieee.2015.7337885
• 发表时间: 2015
• 作者: Ehtiawesh M

XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016

2016 年第十四届地中海医学和生物工程与计算会议

• DOI: 10.1007/978-3-319-32703-7_193
• 发表时间: 2016
• 作者: Cancela J

Biomedical Engineering Systems and Technologies

生物医学工程系统与技术

• DOI: 10.1007/978-3-642-29752-6_14
• 发表时间: 2013
• 作者: Samuri S

Predictive modelling of the granulation process using a systems-engineering approach

使用系统工程方法对造粒过程进行预测建模

• DOI: 10.1016/j.powtec.2016.08.049
• 发表时间: 2016
• 期刊: Powder Technology
• 影响因子: 5.2
• 作者: AlAlaween W

A new computer control system for mental stress management using fuzzy logic

使用模糊逻辑进行精神压力管理的新型计算机控制系统

• DOI: 10.1109/eais.2015.7368785
• 发表时间: 2015
• 作者: El-Samahy E