Cardiac health risk stratification system (CHRiSS): A Bayesian-based decision support system for left ventricular assist device (LVAD) therapy

This study investigated the use of Bayesian Networks (BNs) for left ventricular assist device (LVAD) therapy; a treatment for end-stage heart failure that has been steadily growing in popularity over the past decade. Despite this growth, the number of LVAD implants performed annually remains a small fraction of the estimated population of patients who might benefit from this treatment. We believe that this demonstrates a need for an accurate stratification tool that can help identify LVAD candidates at the most appropriate point in the course of their disease. We derived BNs to predict mortality at five endpoints utilizing the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) database: containing over 12,000 total enrolled patients from 153 hospital sites, collected since 2006 to the present day, and consisting of approximately 230 pre-implant clinical variables. Synthetic minority oversampling technique (SMOTE) was employed to address the uneven proportion of patients with negative outcomes and to improve the performance of the models. The resulting accuracy and area under the ROC curve (%) for predicted mortality were 30 day: 94.9 and 92.5; 90 day: 84.2 and 73.9; 6 month: 78.2 and 70.6; 1 year: 73.1 and 70.6; and 2 years: 71.4 and 70.8. To foster the translation of these models to clinical practice, they have been incorporated into a web-based application, the Cardiac Health Risk Stratification System (CHRiSS). As clinical experience with LVAD therapy continues to grow, and additional data is collected, we aim to continually update these BN models to improve their accuracy and maintain their relevance. Ongoing work also aims to extend the BN models to predict the risk of adverse events post-LVAD implant as additional factors for consideration in decision making

Study and development of a sensorized platform for the monitoring of LVAD-implanted patients

In the industrialized countries, heart failure is the most frequent cause of death. The ageing of the world population and the low availability of heart donors with respect to the demand have led to the develop and experimentation of device therapy for patients with heart failure, not only as bridge to transplant, but also as destination therapy. Nowadays different mechanical ventricular assist devices (VADs) are in use but, in order to use them as alternative to transplant, an approach could be the development of a implantable platform integrating, on the VAD, miniaturized innovative flow and pressure sensors and implementing a continuous monitoring strategy, with the purpose to optimize and personalize the heart unloading degree. The main components of the implantable platform are: the LVAD, the monitoring flow and pressure sensors embedded on the pump, a transcutaneous energy transfer (TET) system, a telemetry (TEL) system and a central control unit (ARU) for the wireless transfer of data collected by the implanted sensors and the control of the VAD status. This work of thesis is about the integration of the pressure sensors on a in-vitro platform, emulating the implantable platform, the contribution on the ARU development and pc-based graphical user interface and the evaluation of the biocompatibility and efficiency of the TET and TEL systems

Investigation of the use of Electro Active Polymer as a Pediatric VAD Driver

Background Heart failure is one of the principal causes of death and disability. The causes of heart failure are many, and a number of technologies have been developed to address this issue by providing support to the failing heart, both as a permanent solution and as a bridge to recovery. These options are called Mechanical Circulatory Support Devices, a particular branch of these devices is the Ventricular Assist Devices, which have been under intense development over the recent years offering a promising solution for this major problem. However, these devices are still bulky, and heavy designed to support failing hearts in the adult population. On the other hand, little has been done in recent times on the development of implantable solutions for heart failure or insufficiency in children. There are many reasons for this, but primarily the relatively small number of children requiring these procedures, the challenges associated with growth, and the lack of physical space for such implantable circulatory support technologies in children are fundamental limitations to the development and deployment of these technologies. Aims of the project The primary purpose of this project was to investigate the development of a new miniaturised self-power VAD that is suitable for paediatrics implantation. This project suggested the use of the newly developed Artificial Muscles to create a mesh that envelops the heart and works as an external assisting circulation mechanism. The same materials could be used to generate electricity when deformed, which can be used to power the proposed device. Critically, the project was to focus on optimising the materials with regard to their operating efficiency to ascertain whether they represent a viable option for VAD production. Materials and Methods A full review of the current available Artificial Muscles was performed to choose the most suitable type for this project. Then different fabrication protocols were developed to make IPMC Artificial Muscles using platinum and palladium coatings. A series of characterization tests were conducted on the fabricated Ionic Polymeric Metal Composites (IPMC) to ensure their quality. Finally, the mechanical and electrical properties were tested and compared with the proposed device requirements. Results The review of Artificial Muscles showed that IPMC would be the best candidate to use in this application. The characterisation tests showed as well that the produced IPMC Artificial Muscles were fabricated to the same standards as those commercially available, and the reported by other investigators. However, these materials showed very low mechanical output with high electrical power consumption, which made them far from practical and not suitable for the proposed application. On the other hand, IPMCs showed promising results as an option to generate electricity to power low consumption implantable devices

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Bio-Inspired Soft Artificial Muscles for Robotic and Healthcare Applications

Soft robotics and soft artificial muscles have emerged as prolific research areas and have gained substantial traction over the last two decades. There is a large paradigm shift of research interests in soft artificial muscles for robotic and medical applications due to their soft, flexible and compliant characteristics compared to rigid actuators. Soft artificial muscles provide safe human-machine interaction, thus promoting their implementation in medical fields such as wearable assistive devices, haptic devices, soft surgical instruments and cardiac compression devices. Depending on the structure and material composition, soft artificial muscles can be controlled with various excitation sources, including electricity, magnetic fields, temperature and pressure. Pressure-driven artificial muscles are among the most popular soft actuators due to their fast response, high exertion force and energy efficiency. Although significant progress has been made, challenges remain for a new type of artificial muscle that is easy to manufacture, flexible, multifunctional and has a high length-to-diameter ratio. Inspired by human muscles, this thesis proposes a soft, scalable, flexible, multifunctional, responsive, and high aspect ratio hydraulic filament artificial muscle (HFAM) for robotic and medical applications. The HFAM consists of a silicone tube inserted inside a coil spring, which expands longitudinally when receiving positive hydraulic pressure. This simple fabrication method enables low-cost and mass production of a wide range of product sizes and materials. This thesis investigates the characteristics of the proposed HFAM and two implementations, as a wearable soft robotic glove to aid in grasping objects, and as a smart surgical suture for perforation closure. Multiple HFAMs are also combined by twisting and braiding techniques to enhance their performance. In addition, smart textiles are created from HFAMs using traditional knitting and weaving techniques for shape-programmable structures, shape-morphing soft robots and smart compression devices for massage therapy. Finally, a proof-of-concept robotic cardiac compression device is developed by arranging HFAMs in a special configuration to assist in heart failure treatment. Overall this fundamental work contributes to the development of soft artificial muscle technologies and paves the way for future comprehensive studies to develop HFAMs for specific medical and robotic requirements

Deep Learning Applications for Biomedical Data and Natural Language Processing

The human brain can be seen as an ensemble of interconnected neurons, more or less specialized to solve different cognitive and motor tasks. In computer science, the term deep learning is often applied to signify sets of interconnected nodes, where deep means that they have several computational layers. Development of deep learning is essentially a quest to mimic how the human brain, at least partially, operates.In this thesis, I will use machine learning techniques to tackle two different domain of problems. The first is a problem in natural language processing. We improved classification of relations within images, using text associated with the pictures. The second domain is regarding heart transplant. We created models for pre- and post-transplant survival and simulated a whole transplantation queue, to be able to asses the impact of different allocation policies. We used deep learning models to solve these problems.As introduction to these problems, I will present the basic concepts of machine learning, how to represent data, how to evaluate prediction results, and how to create different models to predict values from data. Following that, I will also introduce the field of heart transplant and some information about simulation

An intelligent risk detection model to improve decision efficiency in healthcare contexts: the case of paediatric congenital heart disease

Objectives: Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of a real time intelligent risk detection decision support systems using business analytics and data science technologies. To illustrate the power and potential of business analytics and data science technologies in healthcare decision making scenarios, the use of an Intelligent Risk Detection (IRD) Model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes. The main aim of this research is reducing burden of complex surgeries in patients, their family and society through early detecting of surgical risk factors prior to surgery. The research question is: How can an intelligent risk detection (IRD) Model be developed in the healthcare contexts? Method: This study is exploratory in nature and endeavours to explore the main components, barriers, issues and requirement to design and develop an Intelligent Risk Detection framework to be applied to healthcare contexts. In this research a qualitative approach using an exemplar data site as a single case study is adapted to address research objectives and to answer the research question. Data collection is through semi-structured interviews, questionnaires, observation and the analysis of documents, files and data bases from the study site. After conducting the data collection phase thematic analysis is applied to analyse all collected qualitative data. Results: This study has a significant contribution to practice and theory; namely confirming a role for business analytics and data science technologies in healthcare contexts. Also, this research serves to demonstrate that the selection of risk detection, prediction by data mining tools as one of the data science techniques and then decision support are very important for decision making in the complex surgeries. IRD, in practice, can also be used as a training tool to train nurses and medical students to detect the CHD surgery risk factors and their impact on surgery outcomes. Moreover, it can also provide decision support to assist doctors to make better clinical and surgical decisions or at least provide a second opinion. Furthermore, IRD can be used as a knowledge sharing and information transferring tools between clinicians, between clinicians and patients or their families and also between patients with the other patients. In this study also main components, barriers, issues and requirement to design and develop an Intelligent Risk Detection solution are explored and a comprehensive real time Intelligent Risk Detection Model in the healthcare context is designed