A novel ontology and machine learning driven hybrid cardiovascular clinical prognosis as a complex adaptive clinical system

PurposeThis multidisciplinary industrial research project sets out to develop a hybrid clinical decision support mechanism (inspired by ontology and machine learning driven techniques) by combining evidence, extrapolated through legacy patient data to facilitate cardiovascular preventative care.MethodsThe proposed cardiovascular clinical decision support framework comprises of two novel key components: (1) Ontology driven clinical risk assessment and recommendation system (ODCRARS) (2) Machine learning driven prognostic system (MLDPS). State of the art machine learning and feature selection methods are utilised for the prognostic modelling purposes. The ODCRARS is a knowledge-based system which is based on clinical expert’s knowledge, encoded in the form of clinical rules engine to carry out cardiac risk assessment for various cardiovascular diseases. The MLDPS is a non knowledge-based/data driven system which is developed using state of the art machine learning and feature selection techniques applied on real patient datasets. Clinical case studies in the RACPC, heart disease and breast cancer domains are considered for the development and clinical validation purposes. For the purpose of this paper, clinical case study in the RACPC/chest pain domain will be discussed in detail from the development and validation perspective.ResultsThe proposed clinical decision support framework is validated through clinical case studies in the cardiovascular domain. This paper demonstrates an effective cardiovascular decision support mechanism for handling inaccuracies in the clinical risk assessment of chest pain patients and help clinicians effectively distinguish acute angina/cardiac chest pain patients from those with other causes of chest pain.ConclusionThe new clinical models, having been evaluated in clinical practice, resulted in very good predictive power, demonstrating general performance improvement over benchmark multivariate statistical classifiers. Various chest pain risk assessment prototypes have been developed and deployed online for further clinical trials

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

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

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

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

A Sociomaterial Analysis of Team-based Care for Patients with Advanced Heart Failure

This dissertation explores team-based care for patients with advanced heart failure (HF), especially care practices associated with integrating palliative care. Its three integrated articles explore advanced HF care as a team-based health care phenomena, focusing on both objects and people as integral for team activity. The dissertation utilizes the theoretical framework of sociomaterialism to explore the overarching research question: how do materials mediate and influence the integration of palliative and cardiac care for patients with HF? Study data take the form of 15 team sampling units (TSU), each of which consists of a patient interview and an interview with at least one caregiver and one health professional identified by that patient as members of their care team. Data were analyzed through the lens of actor-network theory (ANT). The first study delves into the sociomaterial underpinnings of HF self-care, focusing on patients and caregivers involved with fluid management in their domestic lives. The study highlights the material effects of fluid and its management for patients and caregivers, as well as the way these material effects build into wider network effects. The manuscript discusses how ANT can enrich recent calls in the HF self-care literature for increased attention to `context\u27 in HF self-care. The second study explores how our understanding of health care teamwork shifts when materials, like fluid, are described as actors on the HF team. The paper describes the agency of fluid, a heretofore taken-for-granted material in descriptions of team-based HF care. Fluid is a matter of concern at the centre of interprofessional collaboration between cardiologists and nephrologists. The study advances one of the core theoretical insights of my dissertation,---collaborative entanglement---an innovative construct for understanding and reconceptualizing phenomena that researchers and policymakers frequently label `interprofessional collaborative tensions\u27. The third study directly engages the ideas and values of palliative care integration for HF. The paper argues that while sociological explanations are crucial to policy programming around palliative care (PC) integration, they are insufficient for fully appreciating the challenges faced by HF care teams who are actively trying to integrate PC. The paper attempts to answer the question: why does palliative care integration fail even when human roles are clear, local culture is supportive, and team knowledge is accurate? ANT is invoked to re-frame the discussion about `under-utilization\u27 of PC services for patients with HF. Each of these three analytic projects contributes to a growing interest in sociomaterialism amongst medical education researchers in Europe and North America. In the final chapter, key ideas from the overarching analysis are explored alongside methodological reflections. While the focus of this study is fluid, hopefully these results and insights can inspire further innovative sociomaterial inquiry in medical education

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic