Protocolos para la seguridad de la información en eHealth: Criptografía en entornos mHeath

Abstract. The advance of technology has brought with it the evolution of tools in various fields, among which the medical field stands out. Today’s medicine has tools that 30 years ago were unthinkable making its functioning completely different. Thanks to this fusion of medicine and technology new terms concerning this symbiosis, such as eHealth or mHealth, may be found in our daily lives. Both users and all the areas that work in the protection and performance of health and safety benefit from it. In this doctoral thesis we have worked in several lines with the aim of improving information security in several mHealth systems trying to make the proposed solutions extrapolable to other environments. Firstly, a tool, supported by an expert system and using identity-based encryption for the protection of patient information, for the diagnosis, treatment and monitoring of children with attention deficit disorder is proposed. Second, a solution focused on geared towards enhancing solutions for two of the fundamental problems of medical data information security: the secure management of patient information and the identification of patients within the hospital environment, is included. The solution proposed for the identification problem is based on the use of NFC bracelets that store an identifier associated with the patient and is generated through an HMAC function. In the third work, the problem of identification is again analyzed, but this time in emergency environments where no stable communication networks are present. It also proposes a system for the classification of victims whose objective is to improve the management of health resources in these scenarios. The fourth contribution is a system for improving the traceability and management of small emergencies and everyday events based on the use of blockchains. To conclude with the contributions of this thesis, a cryptographic scheme which improves security in healthcare devices with little computing capacity is presented. The general aim of this thesis is providing improvements in current medicine through mHealth systems, paying special attention to information security. Specifically, measures for the protection of data integrity, identification, authentication and nonrepudiation of information are included. The completion of this doctoral thesis has been funded through a pre-doctoral FPI grant from the Canary Islands Government.El avance de la tecnología ha traído consigo la evolución de herramientas en diversos ámbitos, entre ellos destaca el de la medicina. La medicina actual posee unas herramientas que hace 30 años eran impensables, lo que hace que su funcionamiento sea completamente diferente. Gracias a esta fusión de medicina y tecnología encontramos en nuestro día a día nuevos términos, como eHealth o mHealth, que hacen referencia a esta simbiosis, en la que se benefician tanto los usuarios, como todas las áreas que trabajan en la protección y actuación de la salud y seguridad de las mismas. En esta tesis doctoral se ha trabajado en varias líneas con el objetivo de mejorar la seguridad de la información en varios sistemas mHealth intentando que las soluciones propuestas sean extrapolables a otros entornos. En primer lugar se propone una herramienta destinada al diagnóstico, tratamiento y monitorización de niños con trastorno de déficit de atención que se apoya en un sistema experto y usa cifrado basado en identidad para la protección de la información de los pacientes. En segundo lugar, se incluye una solución centrada en aportar mejoras en dos de los problemas fundamentales de la seguridad de la información de los datos médicos: la gestión segura de la información de los pacientes y la identificación de los mismos dentro del entorno hospitalario. La solución planteada para el problema de identificación se basa en la utilización de pulseras NFC que almacenan un identificador asociado al paciente y que es generado a través de una función HMAC. En el tercer trabajo se analiza de nuevo el problema de identificación de las personas pero esta vez en entornos de emergencia en los que no se cuenta con redes de comunicaciones estables. Además se propone un sistema de clasificación de víctimas en dichos entornos cuyo objetivo es mejorar la gestión de recursos sanitarios en estos escenarios. Como cuarta aportación se presenta un sistema de mejora de la trazabilidad y de la gestión de pequeñas emergencias y eventos cotidianos basada en el uso de blockchain. Para terminar con las aportaciones de esta tesis, se presenta un esquema criptográfico que mejora los esquemas actuales de seguridad utilizados para dispositivos del entorno sanitario que poseen poca capacidad computacional. La finalidad general perseguida en esta tesis es aportar mejoras al uso de la medicina actual a través de sistemas mHealth en los que se presta especial atención a la seguridad de la información. Concretamente se incluyen medidas para la protección de la integridad de los datos, identificación de personas, autenticación y no repudio de la información. La realización de esta tesis doctoral ha contando con financiación del Gobierno de Canarias a través de una beca predoctoral FPI

An algorithm for augmenting cancer registry data for epidemiological research applied to oesophageal cancers

Oesophageal cancer is an important cancer with short survival, but the relationship between pre-diagnosis health behaviour and post-diagnosis survival remains poorly understood. Cancer registries can provide a high quality census of cancer cases but do not record pre-diagnosis exposures. The aim of this thesis is to document relationships between pre-diagnosis health behaviours on post-diagnosis survival times in oesophageal cancer, developing new methods as required. A systematic review and meta-analysis conducted in 2014, and updated in 2021, to investigate the association between pre-diagnosis health behaviours and oesophageal cancer. Visualising health behaviour variables as part of the cancer registry data set, with 100% missing data, led to the development of new approaches for augmenting US oesophageal cancer registry data with health behaviour data from a US national health survey Firstly, the health survey data were used to create logistic regression models of the probability of each behaviour relative to demographic characteristics and then these models were applied to cancer cases to estimate their probability of each behaviour. Secondly, cold-deck imputation such that two randomly selected but demographically similar health survey respondents both donated their health behaviour to the matching cancer case. The agreement between these two imputed values was used as an estimate of the misclassification and corrected for during the analyses. The logistic regression imputation-based analyses returned accurate point estimates, with wide confidence intervals, if the behaviour occurred in more than approximately 5% of cases. Our reviews and analyses confirmed that pre-diagnosis smoking decreased survival in oesophageal cancer (hazard ratio (HR) 1.08, 95% confidence interval (CI) 1.00-1.17) particularly squamous cell carcinoma when comparing highest to lowest lifetime exposure ( and HR 1.55, 95%CI 1.25-1.94); with similar associations for alcohol consumption. Pre-diagnosis leisure time physical activity was found to be associated with reduced hazard (HR 0.25, 95%CI 0.03,0.81) overall. Findings from these analyses can assist in modelling the impact of current changes in community health behaviour, as well as informing prognosis and treatment decisions at the individual level. This novel method of augmenting cancer registry data with pre-diagnosis variables appears to be effective and will benefit from further validation. This thesis has significantly progressed both issues and identified future opportunities for research and development

Lifestyle factors and colorectal cancer: The Norwegian Women and Cancer Study

Colorectal cancer (CRC) is a major global disease. The incidence rate among Norwegian women is currently the highest in the world. Lifestyle factors have a substantial influence on CRC susceptibility. However, it is not clear whether these factors are responsible for the high incidence in Norwegian women, or whether they play a role in CRC survival. This doctoral project investigated lifestyle factors in relation to CRC incidence and survival. We used self-reported information from the Norwegian Women and Cancer (NOWAC) Study, linked with Cancer Registry of Norway and Statistics Norway. We used Cox proportional hazards models to calculate hazard ratios for CRC risk by physical activity levels. We used the Karlson, Holm, and Breen method of decomposition to examine the extent to which the risk factors accounted for the observed geographical differences in CRC incidence. We performed competing mortality risks analyses to determine the associations between pre-diagnostic lifestyle factors and CRC survival. We found no association between physical activity level and the risk of CRC. Adult height, being a former smoker, or a current smoker, were associated with increased CRC risk; and a duration of education of >12 years, and a fruit and vegetable intake of >300 g/day were associated with reduced CRC risk. However, these factors combined, did not account for the geographical variations in CRC incidence. Finally, we found that a pre-diagnostic vitamin D intake of >10 μg/day was associated with 25% reduction in CRC death. Our data suggest that women may need to look further than physical activity in order to reduce their risk of CRC; and lifestyle factors did not explain geographical variations in CRC incidence in Norwegian women. A pre-diagnostic vitamin D intake could improve CRC survival

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care

DEVELOPMENT OF PIEZORESISTIVE SILICON NANOWIRES (SINWS) AND THE APPLICATIONS IN NEMS TECHNOLOGY.

Ph.DDOCTOR OF PHILOSOPH

The potential role of statins in the treatment and prevention of oesophageal adenocarcinoma

Oesophageal adenocarcinoma (OAC) is an aggressive malignancy with a very poor prognosis overall. Barrett’s oesophagus (BO) is the only known precursor lesion. Emerging preclinical evidence indicates statins, medications commonly used in the primary and secondary prevention of cardiovascular disease, inhibit proliferation, promote apoptosis and limit invasiveness of OAC. Inhibition of the mevalonate pathway depletes downstream products involved in candidate growth-signalling cascades. This research aimed to determine: (1) associations between statin use after diagnosis of oesophageal carcinoma (OC) and mortality outcomes; (2) the feasibility of assessing adjuvant statin therapy in patients with operable OAC in a future phase III randomised controlled trial; and (3) associations between statin use and malignant progression to high-grade dysplasia (HGD)/OAC in BO populations. In a cohort of 4445 patients with OC in a large primary care dataset, the General Practice Research Database, post-diagnostic statin use was associated with significant reductions in OC-specific and all-cause mortality. Significant associations were demonstrated in patients with OAC but not in oesophageal squamous cell carcinoma. A multi-centre, double-blind, parallel group, randomised, placebo-controlled feasibility trial of adjuvant statin therapy recruited patients with operable OAC. In total, 32 patients were randomised (1:1) to simvastatin (40mg) or matched placebo. Treatment started from the date of discharge following surgery and continued for up to one year. The trial estimated recruitment, retention, drug absorption, adherence, safety, quality of life, generalisability, and mortality outcomes. The feasibility of a future phase III trial was demonstrated; and derived feasibility estimates inform its design and conduct. A nested case-control analysis of a cohort with BO registered with the United Kingdom National Barrett’s Oesophagus Registry (UKBOR) demonstrated no significant associations between statin use and malignant progression. Significant dose and duration-response relationships were not demonstrated

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