Goal-Directed Trajectory Planning of Therapeutic Pathways for Septic Shock Patients Using Fuzzy Granules-Oriented Maps
Lead Research Organisation:
University of Sheffield
Department Name: Automatic Control and Systems Eng
Abstract
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.
Organisations
Publications
Abate F
(2013)
Biomedical Engineering Systems and Technologies
AlAlaween W
(2016)
Predictive modelling of the granulation process using a systems-engineering approach
in Powder Technology
Chen J
(2015)
A new holistic systems approach to the design of heat treated alloy steels using a biologically inspired multi-objective optimisation algorithm
in Engineering Applications of Artificial Intelligence
Denai MA
(2009)
The Mk3.5 aEIT System: a Review of Recent Updates
Denaï MA
(2010)
Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends.
in IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society
El-Samahy E
(2015)
A new computer control system for mental stress management using fuzzy logic
Description | Absolute imaging measurements of human lungs as opposed to relative imaging measurements of the respiratory in animals. We have also developed an integrated system that allies imaging of the lungs to blood gas analyses to infer (predict) distressed lung functions in humans when admitted to General (respiratory) Intensive Care Unit (ICU). |
Exploitation Route | The findings of the research will provide gearing towards the further development of 3D-imaging using Electrical Impedance Tomography (EIT) to be routinely used in ICU. |
Sectors | Digital/Communication/Information Technologies (including Software) Electronics Healthcare |
Description | Our newly proposed imaging technique based on Absolute measurements can now be combined with other measurements such as those associated with blood-gas to form a perfectly synergised system for more accurate diagnosis and more effective treatment of critically-ill patients in ICU |
First Year Of Impact | 2012 |
Sector | Digital/Communication/Information Technologies (including Software),Electronics,Healthcare |
Impact Types | Societal Economic |