Adaptive Modelling and Image-Based Monitoring for Artificially Ventilated Patients in the Intensive Care Unit (ICU)

The Intensive Care Unit (ICU) is where the critically-ill are treated. The first 24-hours (‘the golden hours’) of treatment is crucial to determine patient’s recovery and survival, and mechanical ventilation plays a major role as the main life support system in the ICU. The efficiency of mechanical ventilation and its management strategy are assessed by observing the arterial blood gases (ABG), which are sampled every few hours using a catheter inserted into the patient’s artery. This procedure is invasive thus can only be performed a handful of times each day. The ICU also has an abundance of underutilized data which until recently can only be translated by expert clinicians, who unfortunately always have clinical responsibilities to undertake concomitantly. This thesis proposes a series of new fuzzy logic-based models with a new type of fuzzy sets (type-2), which have not been investigated before in this clinical setting, for the relative dead-space (Kd), the carbon-dioxide production (VCO2), and the shunt sub-components for the SOPAVent (Sheffield Simulation of Patients under Artificial Ventilation) system, which performs predictions of arterial blood gases non-invasively and automatically. The Kd model, the VCO2 model and the resulting overall SOPAVent model are validated with retrospective real ICU patient data obtained from the Sheffield Royal Hallamshire Hospital (UK). The SOPAVent model is also validated with newly obtained data from patients diagnosed with Faecal Peritonitis (FP), from the Sheffield Royal Hallamshire Hospital (UK). Results showed an improved prediction accuracy for the Kd and the VCO2 sub-components when compared to existing systems. The prediction capability of SOPAVent is also improved from previous models for arterial blood gases before and after ventilator settings changes are made. A second new simplified model for predicting ABG using ventilator settings is also proposed with excellent prediction outcomes. Additionally, this thesis also looks into Electrical Impedance Tomography (EIT) as a potential bedside monitoring tool for pulmonary functions. EIT has the ability to provide a non-invasive, portable, and a relatively low cost alternative to other medical imaging systems. This thesis details the development of the hardware for a compact 16-electrode EIT measurement system, with the objective for future pulmonary applications. A method to generate three-dimensional (3D) images of the lungs from two-dimensional (2D) medical images of the thorax is also proposed with the estimation of lung volumes being presented

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications