Generating conflict-free treatments for patients with comorbidity using ASP

Conflicts in recommended medical interventions regularly arise when multiple treatments are simultaneously needed for patients with comorbid diseases. An approach that can automatically repair such inconsistencies and generate conflict-free combined treatments is thus a valuable aid for clinicians. In this paper we propose an answer set programming based method that detects and repairs conflicts between treatments. The answer sets of the program directly correspond to proposed treatments, accounting for multiple possible solutions if they exist. We also include the possibility to take preferences based on drug-drug interactions into account while solving inconsistencies. We show in a case study that our method results in more preferred treatments

An Unexpected Journey: Towards Runtime Verification of Multiagent Systems and Beyond

The Trace Expression formalism derives from works started in 2012 and is mainly used to specify and verify interaction protocols at runtime, but other applications have been devised. More specically, this thesis describes how to extend and apply such formalism in the engineering process of distributed articial intelligence systems (such as Multiagent systems). This thesis extends the state of the art through four dierent contributions: 1. Theoretical: the thesis extends the original formalism in order to represent also parametric and probabilistic specications (parametric trace expressions and probabilistic trace expressions respectively). 2. Algorithmic: the thesis proposes algorithms for verifying trace expressions at runtime in a decentralized way. The algorithms have been designed to be as general as possible, but their implementation and experimentation address scenarios where the modelled and observed events are communicative events (interactions) inside a multiagent system. 3. Application: the thesis analyzes the relations between runtime and static verication (e.g. model checking) proposing hybrid integrations in both directions. First of all, the thesis proposes a trace expression model checking approach where it shows how to statically verify LTL property on a trace expression specication. After that, the thesis presents a novel approach for supporting static verication through the addition of monitors at runtime (post-process). 4. Implementation: the thesis presents RIVERtools, a tool supporting the writing, the syntactic analysis and the decentralization of trace expressions

Exploring the use of routine healthcare data through process mining to inform the management of musculoskeletal diseases

Healthcare informatics can help address some of the challenges faced by both healthcare providers and patients. The medical domain is characterised by inherently complex and intricate issues, data can often be of poor quality and novel techniques are required. Process mining is a discipline that uses techniques to extract insights from event data, generated during the execution of processes. It has had good results in various branches of medical science but applications to musculoskeletal diseases remain largely unexplored. This research commenced with a review of the healthcare and technical literature and applied a variety of process mining techniques in order to investigate approaches to the healthcare plans of patients with musculoskeletal conditions. The analysis involved three datasets from: 1) a private hospital in Boston, US, where data was used to create disease trajectory models. Results suggest the method may be of interest to healthcare researchers, as it enables a more rapid modelling and visualisation; 2) a mobile healthcare application for patients receiving physiotherapy in Sheffield, UK, where data was used to identify possible indicators for health outcomes. After evaluation of the results, it was found that the indicators identified may be down to chance; and 3) the population of Wales to explore knee pain surgery pathways. Results suggest that process mining is an effective technique. This work demonstrates how routine healthcare data can be analysed using process mining techniques to provide insights that may benefit patients suffering with musculoskeletal conditions. This thesis explores how strict criteria for analysis can be performed. The work is intended to expand the breadth of process mining methods available to the data science community and has contributed by making recommendations for service utilisation within physiotherapy at Sheffield Hospital and helped to define a roadmap for a leading healthcare software company

Role of physical exercise in reducing depression and improving mental health in cancer survivors

Cancer survivors are more than twice as likely as the general population to suffer the debilitating effects of depression. This comorbid condition is associated with several negative consequences, such as reduction in compliance with cancer treatments, and hastened mortality. Recent research has examined the therapeutic effect of exercise on depression and reported excellent results of similar magnitude to those achieved with pharmacotherapy or psychological intervention. However, no research, to date, has examined the effectiveness of exercise on reducing depression in depressed cancer survivors. In order to address this important question this thesis reviewed previous literature in the area of cancer and depression, explored the exercise preferences of depressed cancer survivors living in Australia, and completed the first exercise intervention with a cohort of depressed cancer survivors. Two descriptive reviews provided background information about the types of exercise programs that have been prescribed for cancer survivors as well as the exercise preferences of many cancer survivors. These reviews identified that there was a lack of available information relating to the optimal exercise program to improve depression and, further, that no information was available to determine the preferred exercise options of depressed cancer survivors and whether this group would be interested in engaging in exercise. The first research study was a cross-sectional comparison of the physical activity habits and exercise preferences of depressed (n = 158) and non-depressed (n = 650) Australian cancer survivors. It was found that depressed survivors were less active (-48 mins/wk; p \u3c .05), yet a greater number of depressed cancer survivors (78.5% vs 71.6%) expressed a desire to participate in an exercise program (p = .044). Contrary to expectations, depressed survivors were more interested in supervised sessions (p \u3c .001), and were also more willing to attend their local fitness centre (p = .049). These findings suggest that being depressed does influence the preferred exercise program and that these differences should be considered before prescribing exercise to cancer survivors. Secondary analysis of the dataset was conducted to examine the differences between metropolitan and regional cancer survivors, living in New South Wales, Australia. The role of exercise may be even more important for isolated regional survivors who lack access to traditional form of psychological support and service providers. Results obtained from 366 participants revealed that the incidence of depression (~21%) was not related to location of residence. There was no difference in physical activity participation between metropolitan (n = 236) and regional (n = 130) survivors, with only 40% of all participants being sufficiently active. No differences were found for the primary perceived benefits of improving aerobic fitness, controlling weight, and improving overall health. However, metropolitan residents were more interested in the performing exercise that elicited strength and functional improvements (p = .041). Furthermore, there were no differences in barriers to exercise. These findings indicate that many cancer survivors, including those living in regional locations, are familiar with health benefits of exercise, yet remain insufficiently active to obtain these positive outcomes. The final study was a longitudinal controlled trial that aimed to examine whether exercise was indeed able to produce an antidepressant effect in depressed cancer survivors. Eligible participants were allocated to a clinic-based, supervised exercise group (n = 10), an unsupervised, home-based exercise group (n = 8), or a usual care control group (n = 14). The supervised exercise group completed two sessions of combined resistance and aerobic training per week for the duration of the 12-week program. The home-based group were provided with printed material about the benefits of exercise and were encouraged to complete 150 minutes of exercise per week, but received no specific prescriptive information about the form of exercise they should undertake. The control group received no exercise or printed material, and were encouraged not to alter their usual activity. Intention-to-treat analysis, with the last response carried forward, found that both exercise groups improved depression, with a greater response seen in the supervised program (-56%; p = .002) compared to the home-based group (-48%; p = .016). No significant differences were found when comparing the results between the two exercise groups. When per-protocol analysis was used to examine the responses in depression, it was found that the home-based group decrease depression more rapidly, measured at week 6, and to a greater extent, than the supervised group (d = 0.50). At the final assessment (week 12) the home-based group and supervised group produced a similar response for a reduction in depression. These findings are the first to indicate the antidepressant effect of exercise in depressed cancer patients and the outcomes match those previously reported for people living with depression. Combined with the fact that no adverse effects were reported, the results should be used to promote exercise as a therapeutic treatment option for cancer survivors experiencing comorbid depression. In summary, this research has demonstrated that depressed cancer survivors are able to obtain antidepressant benefits from commencing a well-designed, structured, and supervised exercise program. Despite willingness to engage in exercise, and knowledge of the perceived benefits, almost 60% of depressed cancer survivors are not performing enough physical activity. Therefore, there is need for future research to examine how to increase the uptake of exercise. Engagement is likely to increase if recommendations to commence exercise are made by the survivors’ referring specialist, and, therefore, it is suggested that referral for specialised exercise should be included within best practice cancer care, to remediate comorbid depression

A Systems Medicine approach to multimorbidity. Towards personalised care for patients with Chronic Obstructive Pulmonary Disease

[eng] BACKGROUND: Multimorbidity (i.e. the presence of more than one chronic disease in the same patient) and comorbidity (i.e. the presence of more than one chronic disease in the presence of an index disease) are main sources of dysfunction in chronic patients and avoidable costs in conventional health systems worldwide. By affecting a majority of elderly population worldwide, multimorbidity prompts the need for revisiting the single disease approach followed by contemporary clinical practice and elaborate strategies that target shared mechanisms of associated diseases with the potential of preventing, decelerating or even halting multimorbid disease progression. However, our current understanding on disease interactions is rather limited, and although many disorders have been associated based on their shared molecular traits and their observed co-occurrence in different populations, no comprehensive approach has been outlined to translate this knowledge into clinical practice. The advent of novel measurement technologies (e.g. omics) and recent initiatives on digital health (e.g. registries, electronic health records) are facilitating access to an enormous amount of patient-related information from whole populations to molecular levels. State-of-the art computational models and machine learning tools demonstrate high potential for health prediction and together with systems biology are shaping the practicalities of systems medicine. Given the extremely long and expensive bench to clinics cycles of the biomedical sector, systems medicine promises a fast track approach where scientific evidence support clinical care, while simultaneously collected insights from daily clinical practice promote new scientific discoveries and optimize healthcare. The PhD thesis aims to explore multimorbidity from a systems medicine perspective on the concrete and practical use case of chronic obstructive pulmonary disease (COPD). COPD constitutes an ideal use case due to several factors, including: i) its high impact on healthcare and its ever-increasing burden; ii) its heterogeneous disease manifestations, and progress, often involving extra-pulmonary effects, including highly prevalent comorbidities (e.g. type 2 diabetes mellitus, cardiovascular disorders, anxiety-depression and lung cancer); and, iii) its well described systemic effects that are suggested associations with comorbidities in terms of underlying mechanisms. HYPOTHESIS: The central hypothesis of the PhD thesis builds on the emerging biological evidence that clustering of comorbid conditions, a phenomenon seen in complex chronic patients, could be due to shared abnormalities in relevant biological pathways (i.e. bioenergetics, inflammation and tissue remodelling). It is assumed that a systems understanding of the patient conditions may help to uncover the molecular mechanisms and lead to the design of preventive and targeted therapeutic strategies aiming at modulating patient prognosis. The PhD thesis focuses on non-pulmonary phenomena of COPD; that is, systemic effects and comorbidities, often observed in patients with COPD as a paradigm of complex chronic disease. OBJECTIVES: The general objective of the PhD thesis is threefold: i) to investigate molecular disturbances at body systems level that may lead to a better understanding of characteristic systemic effects and comorbidities of patients with COPD; ii) to analyse population level patterns of COPD comorbidities and investigate their role in the health risk of patients with COPD; and, iii) to explore technological strategies and tools that facilitate the transfer of the collected knowledge on comorbidity into clinical practice. MAIN FINDINGS: Firstly, the PhD thesis introduced a novel knowledge management tool for targeted molecular analysis of underlying disease mechanisms of skeletal muscle dysfunction in patients with COPD. Second, a network analysis approach was outlined to further study this systemic effect, as well as the causes of abnormal adaptation of COPD muscle to exercise training. Furthermore, this work together with three other studies also aimed to reveal the general underlying causes of comorbidity clustering in COPD, using different modelling approaches. Overarching outcome of these studies indicates abnormalities in the complex co-regulation of core biological pathways (i.e. bioenergetics, inflammation, oxidative stress and tissue remodelling) both on muscle and body systems level (blood, lung), which paves the way for the development of novel pharmacological and non-pharmacological preventive interventions on non- pulmonary phenomena in patients with COPD. Furthermore, results indicated strong relation of muscle related dysregulations to aerobic capacity, in opposed to pulmonary severity of COPD. These findings have far reaching potential in COPD care, starting from defining the need for better characterization of exercise performance in the clinic practice and the promotion of physical activity from early stages of the disease. This PhD thesis also generated outcomes with respect to the risk of multimorbidity in patients with COPD using a population health approach. The thesis validated that patients with COPD are in increased risk to co-occur with other diseases compared to non-COPD patients, regardless of the population and healthcare system specificities of different regions (i.e. Catalonia, US). These findings indicated the potential role of multimorbidity as a risk factor for COPD, that was evaluated in the PhD thesis by constructing health risk assessment models to predict unexpected medical events in patients with COPD. The promising performance of the models and the prominent role of multimorbidity in these models presented a powerful argument for its role in clinical staging of the disease and their potential in clinical decision support. CONCLUSIONS: The PhD thesis achieved main points of the general objectives, namely: i) to perform a systems analysis of patients with COPD by investigating molecular disturbances at body systems level leading to a better understanding of characteristic systemic effects and comorbidities of patient with COPD; ii) to analyse population level patterns of COPD comorbidities and investigate their role in the health risk of patients with COPD; and iii) to explore technological strategies and tools that facilitate the transfer of the collected knowledge on comorbidity into clinical practice. Accordingly, the following conclusions arise: 1. Non-pulmonary manifestations in patients with Chronic Obstructive Pulmonary Disease (COPD) have a major negative impact on: highly relevant clinical events, use of healthcare resources and prognosis. Accordingly, the following indications were made: a. Actionable insights on non-pulmonary phenomena should be included in the clinical staging of these patients in an operational manner. b. Management of patients with COPD should be revisited to incorporate an integrative approach to non-pulmonary phenomena. c. Innovative cost-effective interventions, and pharmacological and non- pharmacological treatments targeting prevention of non-pulmonary manifestations in patients with COPD should be developed, and properly assessed. 2. Abnormal co-regulation of core biological pathways (i.e. bioenergetics, inflammation, tissue remodelling and oxidative stress), both in skeletal muscle and at body systems level, are common characteristics of patients with COPD, which potentially play a major role in comorbidity clustering. 3. Consistent relationships between cardiovascular health, skeletal muscle dysfunction and clinical outcomes in patients with COPD was identified, which makes it a priority to characterize patient exercise performance and physical activity in the clinic, and to adopt early cardiopulmonary rehabilitation strategies to modulate prognosis and prevent comorbidity clustering in these patients. 4. Multimorbidity is a strong predictor of unplanned medical events in patients with COPD and shows high potential to be used for personalized health risk prediction and service workflow selection. 5. Personalized health risk prediction was identified as a high potential tool for the integration and transfer of scientific evidence on multimorbidity to daily clinical practice. Limiting factors of its present applicability were explored and implementation strategies based on cloud computing solutions were proposed.[cat] INTRODUCCIÓ: Tant la multimorbiditat (la presència de més d'una malaltia crònica en el mateix pacient), com la comorbiditat (la presència de més d'una malaltia crònica quan hi ha una malaltia de referència) són una font important de disfuncions en l’atenció sanitària dels pacients crònics i generen importants despeses evitables en sistemes de salut arreu del món. La multimorbiditat/comorbiditat afecta la majoria de població de més de 65 anys. El seu gran impacte sanitari i social fa necessària la revisió d’aspectes essencials de la pràctica mèdica convencional, molt enfocada al tractament de cada malaltia de forma aïllada. En aquest sentit, cal elaborar estratègies que considerin els mecanismes biològics comuns entre patologies, per tal de prevenir, retardar o fins i tot aturar la progressió del fenomen. Malauradament, el poc coneixement dels mecanismes biològics que modulen les interaccions entre malalties és un factor limitant important. Hi ha estudis sobre els mecanismes moleculars comuns entre malalties i s’han realitzat anàlisis poblacionals de la multimorbiditat, però no existeix encara una aproximació holística per tal de traduir aquest coneixement a la pràctica clínica. L’aparició de noves tecnologies òmiques, així com iniciatives recents en l’àmbit de la salut digital, han facilitat l'accés a una quantitat enorme d'informació dels pacients, tant a nivell poblacional com a nivell molecular. A més, les eines computacionals i d'aprenentatge automàtic existents estan demostrant un gran potencial predictiu que, conjuntament amb les metodologies de la biologia de sistemes, estan conformant els aspectes pràctics del desplegament de la medicina de sistemes. De forma progressiva, aquesta última esdevé una via efectiva per accelerar el rol de l’evidència científica com a suport a la atenció clínica. De forma recíproca, la digitalització sistemàtica de la pràctica clínica diària, permet la generació de noves descobertes científiques i la optimització de l’assistència sanitària. Aquesta tesis doctoral pretén explorar la multimorbiditat des d’una perspectiva de medicina de sistemes, considerant com a cas d'ús concret i pràctic la malaltia pulmonar obstructiva crònica (MPOC). La MPOC constitueix un cas d'ús ideal a causa de diversos factors: i) el seu alt impacte a nivell sanitari; ii) la heterogeneïtat en quant a manifestacions i progrés, sovint amb efectes extra-pulmonars, incloent de forma freqüent comorbiditats com la diabetis mellitus tipus 2, trastorns cardiovasculars, l'ansietat-depressió i el càncer de pulmó; i, iii) els efectes sistèmics de la malaltia pulmonar, que podrien presentar mecanismes biològics comuns a algunes comorbiditats. HIPÒTESIS: La hipòtesi central d’aquesta tesis doctoral considera que la multimorbiditat podria explicar-se per alteracions en les xarxes de regulació de mecanismes biològics rellevants com la bioenergètica, inflamació i remodelació de teixits. En aquest sentit, l’anàlisi holística del problema podria millorar la comprensió dels mecanismes moleculars que modulen les associacions entre malalties i, per tant, facilitar el disseny d'estratègies terapèutiques preventives i dirigides a modular el pronòstic dels pacients. Aquesta tesis doctoral estudia els fenòmens extra-pulmonars de la MPOC; és a dir, efectes sistèmics (disfunció del múscul esquelètic) i comorbiditats, com a paradigma de malalties cròniques complexes. OBJECTIUS: L'objectiu general d’aquesta tesis doctoral és triple: i) l’anàlisi holístic de pacients amb MPOC amb focus en la disfunció muscular i les comorbiditats; ii) avaluar el paper de les comorbiditats en el risc de salut dels pacients amb MPOC, tant a nivell poblacional com individual; i, iii) explorar estratègies tecnològiques i eines de salut digital que facilitin la transferència de coneixement a la pràctica clínica diària. RESULTATS: El primer manuscrit de la tesi descriu una nova eina de gestió del coneixement per l’anàlisi molecular dels mecanismes de disfunció del múscul esquelètic en pacients amb MPOC. També dins el primer objectiu de la tesi, s’efectua un anàlisi de xarxes orientat a la identificació de mòduls biològics explicatius de la disfunció muscular i de l’adaptació anòmala d’aquests malalts a l’entrenament físic, tal com es descriu en el segon manuscrit. Els tres articles següents exploren, des de diferents perspectives, l’impacte i mecanismes de les comorbiditats en els pacients amb MPOC. Els principals resultats d'aquests estudis indiquen una complexa i anormal regulació de vies biològiques principals, com es el cas de la bioenergètica, inflamació, estrès oxidatiu i remodelació de teixits, tant a nivell del múscul com a nivell sistèmic (sang, pulmó). Aquests resultats obren noves vies per a intervencions preventives, tant farmacològiques com no farmacològiques, sobre els fenòmens no pulmonars que presenten els pacients amb MPOC. Els resultats indiquen una associació de les alteracions musculars amb la capacitat aeròbica, i no pas amb la gravetat de la malaltia pulmonar. Aquestes troballes tenen un gran potencial en la millora de la gestió dels pacients amb MPOC, començant per la necessitat d’una millor caracterització de la capacitat aeròbica en la pràctica clínica i la promoció d'activitat física des de les primeres etapes de la malaltia. La tesi també ha generat resultats d’interès en relació amb el risc de multimorbiditat en pacients amb MPOC, mitjançant un enfocament de salut poblacional. Els resultats evidencien que els pacients amb MPOC presenten un risc mes elevat de comorbiditat que els pacients sense MPOC, independentment de les especificitats de la població i del sistema sanitari de les àrees analitzades (Catalunya, EUA). La tesi també demostra el paper de la multimorbiditat com a factor modulador del risc clínic dels pacients amb MPOC. Aquests resultats indiquen l’interès de l’ús de la multimobiditat en l’estadiatge dels pacients amb MPOC i en l’elaboració d’eines de suport al procés de decisió clínica. CONCLUSIONS: Aquesta tesi doctoral ha assolit els objectius generals plantejats i proposa les següents conclusions: 1. Les manifestacions no pulmonars en els pacients amb malaltia pulmonar obstructiva crònica (MPOC) tenen un impacte negatiu respecte a esdeveniments de gran rellevància clínica, ús de recursos sanitaris i pronòstic. En conseqüència, es fan les següents recomanacions: a. Els fenòmens no pulmonars de la MPOC s’haurien d’incloure de manera operativa en l’estadiatge d'aquests pacients. b. S’hauria de redefinir la gestió clínica dels pacients amb MPOC tot incorporant un enfocament holístic dels fenòmens no pulmonars. c. S’haurien de desenvolupar i avaluar correctament noves intervencions, farmacològiques i no farmacològiques, per a la prevenció de les manifestacions no pulmonars en pacients amb MPOC. 2. Les alteracions de la regulació de vies biològiques rellevants com la bioenergètica, inflamació, estrès oxidatiu i la remodelació de teixits a nivell del múscul esquelètic, i també a nivell sistèmic, s’observa en els pacients amb MPOC i pot tenir un paper important en les co-morbiditats. 3. Les relacions entre alteracions cardiovasculars, disfunció del múscul esquelètic i altres aspectes clínics dels pacients amb MPOC, indiquen la necessitat de caracteritzar la capacitat aeròbica i els nivells d'activitat física en la pràctica clínica, així com la implementació d’estratègies de rehabilitació cardiopulmonar en les primeres etapes de la malaltia, per tal de modular la prognosis dels malalts i prevenir l’aparició de comorbiditats. 4. La multimorbiditat és un bon predictor d’esdeveniments clínics rellevants en pacients amb MPOC i mostra un gran potencial per a personalitzar l’estimació de risc i la selecció de serveis. 5. La predicció de risc de forma personalitzada s’ha identificat com una eina amb molt potencial per a la gestió de la multimorbiditat en la pràctica clínica diària. S’han explorat els factors limitants de la seva aplicabilitat i s’han proposat estratègies d'implementació d’eines predictives adients, basades en solucions de computació en el núvol.[spa] INTRODUCCIÓN: Tanto la multimorbilidad (la presencia de más de una enfermedad crónica en un mismo paciente) como la comorbilidad (la presencia de más de una enfermedad crónica en presencia de una enfermedad de referencia) son una fuente importante de disfunciones en la atención sanitaria de los pacientes crónicos y generan importantes costes evitables en los sistemas de salud de todo el mundo. La multimorbilidad/comorbilidad afecta a la mayoría de la población de más de 65 años. Debido a su gran impacto sanitario y social, resulta necesaria la revisión de aspectos esenciales de la práctica médica convencional, muy enfocada en el tratamiento de cada enfermedad de forma aislada. En este sentido, es necesario elaborar estrategias que consideren mecanismos biológicos comunes entre patologías, con el fin de prevenir, retrasar o incluso detener la progresión del fenómeno. Desgraciadamente, el escaso conocimiento de los mecanismos biológicos que modulan las interacciones entre enfermedades es un factor limitante importante. Existen estudios sobre los mecanismos moleculares comunes entre enfermedades y se han realizados análisis poblaciones de la multimorbilidad, pero no existe aún una aproximación holística que permita traducir este conocimiento a la práctica clínica. La aparición de nuevas tecnologías ómicas, así como recientes iniciativas en el ámbito de la salud digital, han facilitado el acceso a una cantidad enorme de información sobre los pacientes, tanto a nivel poblacional como a nivel molecular. Además, las herramientas computacionales y de aprendizaje automático existentes demuestran un gran potencial predictivo que, conjuntamente con las metodologías de biología de sistemas, están conformando los aspectos prácticos de la medicina de sistemas. De manera progresiva esta última se está convirtiendo en una vía efectiva para acelerar el papel de la evidencia científica como soporte a la atención clínica. De forma recíproca, la digitalización sistemática de la práctica clínica diaria permite la generación de nuevos descubrimientos científicos y la optimización de la asistencia sanitaria. Esta tesis doctoral pretende explorar la multimorbilidad desde una perspectiva de medicina de sistemas, considerando como caso de uso concreto y práctico la enfermedad pulmonar obstructiva crónica (EPOC). La EPOC constituye un caso de uso ideal debido a diversos factores: i) su alto impacto a nivel sanitario; ii) la heterogeneidad en cuanto a manifestaciones y progreso, a menudo con efectos extra pulmonares, incluyendo de forma frecuente comorbilidades como la diabetes mellitus tipo 2, trastornos cardiovasculares, la ansiedad-depresión y el cáncer de pulmón; y, iii) los efectos sistémicos de la enfermedad pulmonar, que podrían presentar mecanismos biológicos comunes a algunas comorbilidades. HIPÓTESIS: La hipótesis central de esta tesis doctoral considera que la multimorbilidad podría explicarse por alteraciones en las redes de regulación de mecanismos biológicos relevantes como la bioenergética, inflamación y remodelación de tejidos. En este sentido, el análisis holístico del problema podría mejorar la comprensión de los mecanismos moleculares que modulan las asociaciones entre enfermedades y, por tanto, facilitar el diseño de estrategias terapéuticas preventivas y dirigidas a modular el pronóstico de los pacientes. Esta tesis doctoral estudia los fenómenos extra pulmonares de la EPOC; es decir, efectos sistémicos (disfunción del músculo esquelético) y comorbilidades, como paradigma de enfermedades crónicas complejas. OBJETIVOS: El objetivo general de esta tesis doctoral es triple: i) el análisis holístico de pacientes con EPOC focalizando en la disfunción muscular y la comorbilidades; ii) evaluar el papel de las comorbilidades en el riesgo de salud de los pacientes con EPOC, tanto a nivel poblacional como individual; y, iii) explorar estrategias tecnológicas y herramientas de salud digital que faciliten la transferencia de conocimiento a la práctica clínica diaria. RESULTADOS: El primer manuscrito de la tesis describe una nueva herramienta de gestión del conocimiento para el análisis molecular de los mecanismos de disfunción del músculo esquelético en pacientes con EPOC. Incluido en el primer objetivo de la tesis, se efectúa un análisis de redes orientado a la identificación de módulos biológicos que explican la disfunción muscular y la adaptación anómala de estos pacientes al entrenamiento físico, tal y cómo se describe en el segundo manuscrito. Los tres artículos siguientes exploran, desde perspectivas diferentes, el impacto y mecanismos de las comorbilidades en los pacientes con EPOC. Los principales resultados de estos estudios indican una compleja y anormal regulación de vías biológicas principales, como es el caso de la bioenergética, inflamación, estrés oxidativo y remodelación de tejidos, tanto a nivel del músculo como a nivel sistémico (sangre, pulmón). Estos resultados abren nuevas vías para intervenciones preventivas, tanto farmacológicas como no farmacológicas, sobre los fenómenos no pulmonares que presentan los pacientes con E

Tackling Dierent Business Process Perspectives

Business Process Management (BPM) has emerged as a discipline to design, control, analyze, and optimize business operations. Conceptual models lie at the core of BPM. In particular, business process models have been taken up by organizations as a means to describe the main activities that are performed to achieve a specific business goal. Process models generally cover different perspectives that underlie separate yet interrelated representations for analyzing and presenting process information. Being primarily driven by process improvement objectives, traditional business process modeling languages focus on capturing the control flow perspective of business processes, that is, the temporal and logical coordination of activities. Such approaches are usually characterized as \u201cactivity-centric\u201d. Nowadays, activity-centric process modeling languages, such as the Business Process Model and Notation (BPMN) standard, are still the most used in practice and benefit from industrial tool support. Nevertheless, evidence shows that such process modeling languages still lack of support for modeling non-control-flow perspectives, such as the temporal, informational, and decision perspectives, among others. This thesis centres on the BPMN standard and addresses the modeling the temporal, informational, and decision perspectives of process models, with particular attention to processes enacted in healthcare domains. Despite being partially interrelated, the main contributions of this thesis may be partitioned according to the modeling perspective they concern. The temporal perspective deals with the specification, management, and formal verification of temporal constraints. In this thesis, we address the specification and run-time management of temporal constraints in BPMN, by taking advantage of process modularity and of event handling mechanisms included in the standard. Then, we propose three different mappings from BPMN to formal models, to validate the behavior of the proposed process models and to check whether they are dynamically controllable. The informational perspective represents the information entities consumed, produced or manipulated by a process. This thesis focuses on the conceptual connection between processes and data, borrowing concepts from the database domain to enable the representation of which part of a database schema is accessed by a certain process activity. This novel conceptual view is then employed to detect potential data inconsistencies arising when the same data are accessed erroneously by different process activities. The decision perspective encompasses the modeling of the decision-making related to a process, considering where decisions are made in the process and how decision outcomes affect process execution. In this thesis, we investigate the use of the Decision Model and Notation (DMN) standard in conjunction with BPMN starting from a pattern-based approach to ease the derivation of DMN decision models from the data represented in BPMN processes. Besides, we propose a methodology that focuses on the integrated use of BPMN and DMN for modeling decision-intensive care pathways in a real-world application domain

A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care

Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases. The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS. The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include: (a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology. (b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patients’ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems. (c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation. A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution. The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems. The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies