Machine Learning Modelling of Critical Care Patients in the Intensive Care Units

The ICU is a fast-paced data-rich environment which treats the most critically ill patients. On average, over 15 % of patients admitted to the ICU amount in mortality. Therefore, machine learning (ML) is paramount to aiding the optimisation and inference of insight in critical care. In addition, the early and accurate evaluation of the severity at the time of admission is significant for physicians. Such evaluations make patient management more effective as they are more likely to predict whose physical conditions may worsen. Moreover, ML techniques could potentially enhance patients' experience in the clinical setting by providing medical alerts and insight into future events occurring during hospitalisation. The need for interpretable models is crucial in the ICU and clinical setting, as it is vital to explain a decision that leads to any course of action related to an individual patient. This thesis primarily focuses on mortality, length of stay forecasting, and AF classification in critical care. We cover multiple outcomes and modelling methods whilst using multiple cohorts throughout the research. However, the analysis conducted throughout the thesis aims to create interpretable models for each modelling objective. In Chapter 3, we investigate three publicly available critical care databases containing multiple modalities of data and a wide range of parameters. We describe the processes and contemplations which must be considered to create actionable data for analysis in the ICU. Furthermore, we compared the three data sources using traditional statistical and ML methods and compared predictive performance. Based on 24 hours of sequential data, we achieved AUC performances of 79.5% for ICU mortality prediction and a prediction error of approximately 1.3 hours for ICU LOS. In Chapter 4, we investigate a sepsis cohort and conduct three sub-studies. Firstly, we investigated sepsis subtypes and compared biomarkers using traditional modelling methods. Next, we compare our approach to commonly and routinely used scoring systems in the ICU, such as APACHE IV and SOFA. Our tailored approach achieved superior performance with pulmonary and abdominal sepsis (AUC 0.74 and 0.71respectivly), displaying distinct individualities amongst the different sepsis groups. Next, we further expand our analysis by comparing ML methods and inference approaches to our baseline model and ICU acuity scores. We further investigate extending analysis to other outcomes of interest (In-hospital/ICU mortality, In-hospital/ICU LOS) to gain a more holistic view of the sepsis derivatives. This research shows that nonlinear models such as RF and GBM commonly outperformICU scoring, methods such as APACHE IV and SOFA and linear methods such as logistic/linear regression. Lastly, we extend our analysis in a multi-task learning framework for model optimisation and improved predictive performance. Our results showed superior performance with pulmonary, abdominal and renal/UTI sepsis (AUC 0.76, 0.77 and 0.73, respectively). Lastly, Chapter 5 investigates the classification of atrial fibrillation (AF) in long-lead ECG waveforms in sepsis patients. We developed a deep neural network to classify AF ECGs from Non-AF ECG cases in conjunction with refining a method to gain insight from the neural network model. We achieved a predictive performance of 0.99 and 0.89 regarding the test and external validation data. The inference from the model was achieved through the use of saliency maps, dimensionality reduction methods and clustering, utilising the automatic features learned by the developed model. We developed visualisations to help support the inference behind the classification of each ECG prediction. Overall, the research displays a wide range of novelties and unique approaches to solving various outcomes of interest in the ICU. In addition, this research demonstrates the implication of ML applicability in the ICU environment by providing insight and inference to diverse tasks regardless of the level of complexity. With further development, the frameworks and approaches outlined in this thesis have the potential to be used in clinical practice as decision-support tools in the ICU, allowing the automated alert and detection of patient classification, amongst others. The results generated in this thesis resulted in journal publications and medical understanding gained from insight available in the developed ML frameworks

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Electrocardiogram Signal Analysis and Simulations for Non-Invasive Diagnosis - Model-Based and Data-Driven Approaches for the Estimation of Ionic Concentrations and Localization of Excitation Origins

Das Elektrokardiogramm (EKG) ist die Standardtechnik zur Messung der elektrischen Aktivität des Herzens. EKG-Geräte sind verfügbar, kostengünstig und erlauben zudem eine nichtinvasive Messung. Das ist insbesondere wichtig für die Diagnose von kardiovaskulären Erkrankungen (KVE). Letztere sind mit verursachten Kosten von 210 Milliarden Euro eine der Hauptbelastungen für das Gesundheitssystem in Europa und dort der Grund für 3,9 Millionen Todesfälle – dies entspricht 45% aller Todesfälle. Neben weiteren Risikofaktoren spielen chronische Nierenerkrankungen und strukturelle Veränderungen des Herzgewebes eine entscheidende Rolle für das Auftreten von KVE. Deshalb werden in dieser Arbeit zwei Pathologien, die in Verbindung zu KVE stehen, betrachtet: Elektrolytkonzentrationsveränderungen bei chronisch Nierenkranken und ektope Foki, die autonom Erregungen iniitieren. In beiden Projekten ist die Entwicklung von Methoden mithilfe von simulierten Signalen zur Diagnoseunterstützung das übergeordnete Ziel. Im ersten Projekt helfen simulierte EKGs die Signalverarbeitungskette zur EKG-basierten Schätzung der Ionenkonzentrationen von Kalium und Calcium zu optimieren. Die Erkenntnisse dieser Optimierung fließen in zwei patienten-spezifische Methoden zur Kaliumkonzentrationsschätzung ein, die wiederum mithilfe von Patientendaten ausgewertet werden. Die Methoden lieferten im Mittel einen absoluten Fehler von 0,37 mmol/l für einen patienten-spezifischen Ansatz und 0,48 mmol/l für einen globalen Ansatz mit zusätzlicher patienten-spezifischer Korrektur. Die Vorteile der Schätzmethoden werden gegenüber bereits existierender Ansätze dargelegt. Alle entwickelten Algorithmen sind ferner unter einer Open-Source-Lizenz veröffentlicht. Das zweite Projekt zielte auf die Lokalisierung von ektopen Foki mithilfe des EKGs ohne die Nutzung der individuellen Patientengeometrie. 1.766.406 simulierte EKG-Signale (Body Surface Potential Maps (BSPMs)) wurden zum Trainieren von zwei Convolutional Neural Networks (CNNs) erzeugt. Das erste CNN sorgt für die Schätzung von Anfang und Ende der Depolarisation der Ventrikel. Das zweite CNN nutzt die Information der Depolarisation im BSPM zur Schätzung des Erregungsurpsrungs. Der spezielle Aufbau des CNNs ermöglicht die Darstellung mehrerer Lösungen, wie sie durch Mehrdeutigkeiten im BSPM vorliegen können. Der kleinste Median des Lokalisierungsfehlers lag bei 1,54 mm für den Test-Datensatz der simulierten Signale, bzw. bei 37 mm für Patientensignale. Somit erlaubt die Kombination beider CNNs die verlässliche Lokalisierung von ektopen Foki auch anhand von Patientendaten, obwohl Patientendaten vorher nicht im Training genutzt wurden. Die Resultate dieser zwei Projekte demonstrieren, wie EKG-Simulationen zur Entwicklung und Verbesserung von EKG-Signalverarbeitungsmethoden eingesetzt werden und bei der Diagnosefindung helfen können. Zudem zeigt sich das Potential der Kombination von Simulationen und CNNs, um einerseits die zumeist raren klinischen Signale zu ersetzen und andererseits Modelle zu finden, die für mehrere Patienten/-innen gültig sind. Die vorgestellten Methoden bergen die Möglichkeit, die Diagnosestellungen zu beschleunigen und mit hoher Wahrscheinlichkeit den Therapieerfolg der Patienten zu verbessern

A computer vision pipeline for fully automated echocardiogram interpretation

Cardiovascular disease is the leading cause of global mortality and continues to place a significant burden, in economic and resource terms, upon health services. A 2-dimensional transthoracic echocardiogram captures high spatial and temporal images and videos of the heart and is the modality of choice for the rapid assessment of heart function and structure due to it’s non-invasive nature and lack of ionising radiation. The challenging process of analysing echocardiographic images is currently manually performed by trained experts, though this process is vulnerable to intra- and inter-observer variability and is highly time-consuming. Additionally, echocardiographic images suffer from varying degrees of noise and vary drastically in terms of image quality. Exponential advancements in the fields of artificial intelligence, deep learning and computer vision have enabled the rapid development of automated systems capable of high-precision tasks, often out-performing human experts. This thesis aims to investigate the applicability of applying deep learning methods to automate key processes in the modern echocardiographic laboratory. Namely, view classification, quality assessment, cardiac phase detection, segmentation of the left ventricle and keypoint detection on tissue Doppler imaging strips. State-of-the-art deep learning architectures were applied to each task, and evaluated against ground-truth annotations provided by trained experts. The datasets used throughout each Chapter are diverse and, in some cases, have been made public for the benefit of the research community. To encourage transparency and openness, all code and model weights have been published. Should automated deep learning systems, both online (in terms of providing real-time feedback) and offline (behind the scenes), become integrated within clinical practice, there is great potential for improved accuracy and efficiency, thus improving patient outcomes. Furthermore, health services could save valuable resources such as time and money

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity