Mining health knowledge graph for health risk prediction

Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients’ health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health

Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data

Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of Kentucky healthcare facilities, we explore data mining and machine learning methods for association rule (AR) mining and predictive modeling with mood and anxiety disorders as use-cases. Our first work involves analysis of existing quantitative measures of rule interestingness to assess how they align with a practicing psychiatrist’s sense of novelty/surprise corresponding to ARs identified from EMRs. Our second effort involves mining causal ARs with depression and anxiety disorders as target conditions through matching methods accounting for computationally identified confounding attributes. Our final effort involves efficient implementation (via GPUs) and application of contrast pattern mining to predictive modeling for mental conditions using various representational methods and recurrent neural networks. Overall, we demonstrate the effectiveness of rule mining methods in secondary analyses of EMR data for identifying causal associations and building predictive models for diseases

Dust, Cadmium and Rheumatoid Arthritis

Background Rheumatoid arthritis (RA) is a systemic, inflammatory disease with an estimated global prevalence of 0.3–1.0%. Evidence suggests that RA is initiated in the lungs. Cigarette smoking and various occupations associated with vapour, gas, dust, and fume (VGDF) inhalation can increase the risk of RA development. The association of VGDF, smoking, development of rheumatoid autoantibodies such as rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA) and their relationship to RA disease development is poorly understood. Structure There are seven chapters in the dissertation. Chapter 1 introduces the dissertation reasoning and hypothesis. Chapter 2 is a published review of literature on RA and inhaled occupational exposures. Chapters 3 and 4 are published empirical studies analysing the clustering pattern of RF and ACPA, suggesting a potential common autoantigen in RA. Chapter 5 is a published empirical study analysing the pattern of autoantibody development with inhalational exposures to smoking and VGDF in male RA. Chapter 6 analyses the role of cadmium (as a common factor in smoking and VGDF), in relation to autoantibody development in nodular and non-nodular RA. Chapter 7 discusses further the strengths, limitations, unanswered questions and future direction of research. Conclusions Overall, this research provides evidence that RA, particularly in males, is precipitated by inhaled environmental exposures and RA patients with multiple inhalational insults are likely to have higher RF and ACPA levels. Empirical and laboratory evidence suggests a common autoantigen in RA to explain autoantibody clustering. Nodular RA patients demonstrate higher rheumatoid autoantibody levels, and significantly higher cadmium levels were found in female nodular RA patients. A model of heavy metal adsorption onto VGDF particles in vitro is proposed, stimulating pulmonary nodule formation and generating autoantibodies in response to a common autoantigen: post-translationally modified heavy chain fragments of immunoglobulin G

Smoking assessment and work ability trends in asthma patients – prospective and retrospective study approach

Smoking increases the risk of asthma and impairs the prognosis of the disease and therapeutic response. Smoking cessation is an essential part of the treatment of asthma. The comprehensive treatment of asthma is also important for the patient’s work ability. The prevalence of asthma has grown, and an increasing number of workers have to cope with the disease in their working lives. The present study aimed to evaluate how reliably asthmatics reported their smoking status and the changes in smoking habits over the last 15 years. We investigated how actively physicians discuss and document patient’s smoking status. The study also examined the development of the work ability score (WAS) in asthma patients to find risk factors for poor development of WAS. This study included two cohorts. The Finnish obstructive airway disease (CAD) cohort included 1,329 asthma patients and 959 chronic obstructive pulmonary disease patients. Their smoking habits, work ability, and general health were followed by questionnaires during 10-years. The register-based cohort included 35,650 patients, whose electronic health records (EHR) were analysed with a combination of rule-and deep learning (ULMFiT)-based algorithms. Only 6% of asthmatics had unreliability in the self-reported smoking data. Pack years can be considered only a rough estimate of the comprehensive consumption of tobacco products. Based on the algorithmic analysis, 61% of asthma patients had documented smoking status, and 55% of current smokers had discussed smoking cessation with the clinician during the two-year follow-up. In the future, smoking cessation care should be activated in hospitals. The performance of the ULMFiT-based classifier was good and showed that deep-learning-based models can create efficient tools for utilising the Finnish EHR. Over 90% of the patients’ WAS remained stable throughout the 10-year study period, but 8% of the patients who had more severe asthma, higher BMI, and multiple comorbidities showed significantly poorer outcomes. To support asthma patients’ work ability, comprehensive treatment of asthma and comorbidities, regular controls, and weight management are needed.Tupakoinnin arviointi ja työkyvyn trendit astmapotilailla – prospektiivinen ja retrospektiivinen lähestymistapa Tupakointi lisää astmariskiä, heikentää sairauden ennustetta ja terapeuttista vastetta. Tupakoinnin lopettaminen on tärkeä osa astman hoitoa. Astman kokonaisvaltainen hoito on oleellista myös potilaan työkyvyn kannalta. Astman esiintyvyys on kasvanut ja yhä useamman täytyy selviytyä sairauden kanssa työelämässä. Tutkimuksen tavoitteena oli selvittää kuinka luotettavasti astmaatikot raportoivat tupakointitietojaan ja mitkä ovat tupakointitottumusten muutokset viimeisten 15 v aikana. Tutkimme myös kuinka aktiivisesti lääkärit keskustelevat tupakoinnista ja dokumentoivat potilaan tupakointistatuksen sairaskertomukseen. Lisäksi tavoitteena oli tutkia työkykypisteiden (WAS) kehitystä astmapotilailla, jotta löydettäisiin riskitekijöitä työkyvyn heikolle kehitykselle. Tutkimus sisälsi kaksi kohorttia. Astman ja keuhkoahtaumataudin yksilöllinen hoito -tutkimuskohortti (AST) koostui 1329 astma- ja 959 keuhkoahtauma-tautipotilaasta. Heidän tupakointitapojaan, työkykyään ja yleistä terveyttään seurattiin 10 vuoden ajan kyselylomakkeiden avulla. Rekisteripohjainen kohortti koostui 35 650 aikuispotilaasta, joiden sairauskertomustekstejä analysoitiin sääntöpohjaisten ja syväoppimiseen (ULMFiT) perustuvien algoritmien avulla. Vain 6%:lla astmapotilaista itseraportoidut tupakkatiedot olivat epäluotettavia. Askivuosia voidaan käyttää vain karkeana arviona tupakointitaakasta. Algoritmisten analyysien pohjalta 61%:lla astmapotilaista oli tupakointistatus merkittynä sairauskertomukseen ja 55% nykyisistä tupakoitsijoista oli keskustellut lopetta-misesta lääkärin kanssa. Tulevaisuudessa tupakka- ja nikotiiniriippuvuuden hoitoa tulee aktivoida sairaaloissa. ULMFiT:iin perustuvan tupakointiluokittelijan toimivuus oli hyvä ja osoitti, että syväoppimiseen perustuvat mallit voivat luoda tehokkaita työkaluja suomalaisen sairauskertomuksen hyödyntämiseen. Yli 90%:lla potilaista työkykypistemäärä pysyi vakaana 10 vuoden seuranta-ajan, mutta 8%:lla potilaista, joilla oli vaikeampi astma ja enemmän oheissairauksia, tulokset olivat selkeästi heikommat. Astmapotilaiden työkyvyn tukemiseksi tarvitaan astman ja oheissairauksien kokonaisvaltaista hoitoa sekä ohjausta painonhallinnan

Heart failure – risk factors and the validity of diagnoses

Heart failure (HF) is a global health problem. HF risk factors remain understudied. The roles that diabetes and sodium consumption play in HF remain unknown. Furthermore, the validity of HF diagnoses in the Finnish Hospital Discharge Register (FHDR) has not been thoroughly evaluated. This thesis aims to discover sodiumand diabetes-related HF risk factors, validate FHDR-based HF diagnoses, and investigate if the subtyping of register-based HF diagnoses could be improved through electronic health record (EHR) data mining. A 24-hour urinary sodium excretion (mean 183 mmol/d) was measured from 4,630 individuals to assess the relationship between salt intake and incident HF (Study I). We used data from 3,834 diabetic and 90,177 nondiabetic individuals to evaluate the diabetes status-related differences in risk factors and mediators of HF (Study II). Medical records of 120 HF cases and 120 controls were examined to study the validity of HF diagnoses (Study III). We drew data from 33,983 patients to assess if HF diagnoses could be subtyped more accurately through EHR data mining (Study IV) and validated the mining-based versus clinical subtyping in 100 randomly selected patients. In Study I, we observed that high sodium intake was associated with incident coronary artery disease (CAD) and diabetes, but not HF. In Study II, the risk of HF was 2.7-fold in individuals with diabetes compared to nondiabetic participants. Conventional cardiovascular disease risk factors and biomarkers for cardiac strain, myocardial injury, and inflammation were associated with incident HF in both groups. The strongest mediators of HF in diabetes were the direct effect of diabetes and the indirect effects mediated by obesity, cardiac strain/volume overload, and hyperglycemia. In studies III and IV, HF diagnoses of the FHDR had good predictive values (NPV 0.83, PPV 0.85), even when patients with preexisting heart conditions were used as controls. With additional EHR-mined data, the accuracy of our algorithm to correctly classify individuals into HF subtypes versus clinical assessment was 86 %. The findings in this thesis show that register-based HF is an accurate endpoint and that EHR data mining can improve this accuracy. Our results also elucidate the role of sodium and diabetes as HF risk factors.Sydämen vajaatoiminta: riskitekijät ja diagnoosien validiteetti Sydämen vajaatoiminta on maailmanlaajuinen terveysongelma, jonka riskitekijät ovat osin epäselviä. Suolan käytön yhteyttä ja diabeteksen aiheuttamaa korkeaa riskiä vajaatoimintaan ei ole riittävästi tutkittu. Vajaatoimintadiagnoosien validiteettia Hoitoilmoitusjärjestelmä (HILMO)-sairaalarekisterissä ei tiedetä. Tässä väitöskirjatyössä tutkittiin suolaan ja diabetekseen liittyviä sydämen vajaatoiminnan riskitekijöitä, validoitiin HILMO-pohjaiset vajaatoimintadiagnoosit ja selvitettiin, voidaanko vajaatoimintaa alatyypittää tekstinlouhintaa käyttämällä. Suolan saannin ja vajaatoiminnan välisen suhteen arvioimiseksi (tutkimus I) tutkittiin 4 630 henkilön vuorokausivirtsan natrium (keskimäärin 183 mmol/d). Diabetekseen liittyvien sydämen vajaatoiminnan riskitekijöiden selvittämiseksi (tutkimus II) käytiin läpi 3 834 diabeetikon ja 90 177 verrokin tiedot. Vajaatoimintadiagnoosien validiteettia (tutkimus III) varten tutkimme 120 vajaatoimintatapauksen ja 120 verrokin (joilla oli muu sydänsairaus) potilastiedot ja tarkempaa alatyypitystä (tutkimus IV) varten keräsimme tietoja 33 983 potilaasta ja validoimme tiedonlouhintaan perustuvan alatyypityksen 100 satunnaisella potilaalla. Tutkimuksessa I suolan saanti oli yhteydessä sepelvaltimotaudin ja diabeteksen kehittymiseen, mutta tulokset eivät olleet merkitseviä vajaatoiminnan osalta. Tutkimuksessa II diabeetikoiden vajaatoimintariski oli 2,7-kertainen verrokkeihin verrattuna. Molemmilla tavanomaiset riskitekijät ja sydämen venyvyyden, sydänvaurion ja tulehduksen merkkiaineet olivat yhteydessä vajaatoimintaan. Merkittävimmät diabeteksen vajaatoimintaa välittävät muuttujat olivat diabeteksen suora vaikutus sekä epäsuorat ylipainon, sydämen venymisen ja hyperglykemian vaikutukset. Tutkimuksissa III ja IV HILMO-rekisterin vajaatoimintadiagnoosin prediktiiviset arvot olivat hyviä (NPV 0,83, PPV 0,85) verrattuna muihin sydänsairaisiin potilaihin ja tiedonlouhinnan alatyypityksen tarkkuus verrattuna kliiniseen oli 86 %. Tämä väitöskirja osoittaa, että HILMO-pohjaiset vajaatoimintadiagnoosit toimivat tieteellisenä päätetapahtumana ja että vajaatoiminnan alatyyppiä voidaan tarkentaa tekstilouhinnalla, sekä tuo uutta tietoa suolasta ja diabeteksesta vajaatoiminnan riskitekijöinä

Doctor of Philosophy

dissertationWith the growing national dissemination of the electronic health record (EHR), there are expectations that the public will benefit from biomedical research and discovery enabled by electronic health data. Clinical data are needed for many diseases and conditions to meet the demands of rapidly advancing genomic and proteomic research. Many biomedical research advancements require rapid access to clinical data as well as broad population coverage. A fundamental issue in the secondary use of clinical data for scientific research is the identification of study cohorts of individuals with a disease or medical condition of interest. The problem addressed in this work is the need for generalized, efficient methods to identify cohorts in the EHR for use in biomedical research. To approach this problem, an associative classification framework was designed with the goal of accurate and rapid identification of cases for biomedical research: (1) a set of exemplars for a given medical condition are presented to the framework, (2) a predictive rule set comprised of EHR attributes is generated by the framework, and (3) the rule set is applied to the EHR to identify additional patients that may have the specified condition. iv Based on this functionality, the approach was termed the ‘cohort amplification' framework. The development and evaluation of the cohort amplification framework are the subject of this dissertation. An overview of the framework design is presented. Improvements to some standard associative classification methods are described and validated. A qualitative evaluation of predictive rules to identify diabetes cases and a study of the accuracy of identification of asthma cases in the EHR using frameworkgenerated prediction rules are reported. The framework demonstrated accurate and reliable rules to identify diabetes and asthma cases in the EHR and contributed to methods for identification of biomedical research cohorts

Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care

Background and aims: Millions of Americans are discharged from hospitals to home health every year and about third of them return to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of services. Early targeted allocation of services for patients who need them the most, have the potential to decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to identify high-risk patients in home health. This dissertation study aimed to (1) identify factors associated with priority for the first home health nursing visit and (2) to construct and validate a decision support tool for patient prioritization. I recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care coordination to identify factors supporting home health care prioritization. Methods: This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included sociodemographics, diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability, self-rated health, depression, functional status, living arrangement, caregiver availability and ability and first home health visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the final model. Results: The final model identified five factors associated with first home health visit priority. A cutpoint for decisions on low/medium versus high priority was derived with a sensitivity of 80% and specificity of 57.9%, area under receiver operator curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid condition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04). Discussion: This dissertation study developed one of the first clinical decision support tools for home health, the PREVENT - Priority for Home Health Visit Tool. Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes

Predictive Medicine for Chronic Patients in an Integrated Care Scenario. Chronic Obstructive Pulmonary Disease as Use Case

[cat] INTRODUCCIÓ La proliferació de les malalties no contagioses i la creixent necessitat de reduir costos està desencadenant una remodelació estructural de l’atenció sanitària envers el model d’atenció a crònics, involucrant la implementació de serveis d’atenció integrada (SAI) amb el suport de les tecnologies de la informació i la comunicació (SAI-TIC). En aquest escenari, la emergent medicina de sistemes, amb un aproximació holística basada en els mecanismes de les malalties, juga un rol rellevant a l’avaluació del risc per la salut i la estratificació de pacients. L’objectiu principal de Synergy-COPD ha estat la exploració del potencial de una aproximació de la medicina de sistemes per tal de millorar el coneixement dels mecanismes subjacents a la heterogeneïtat de la malaltia pulmonar obstructiva crònica (MPOC). Fent èmfasi als efectes sistèmics de la malaltia, així com en la comorbiditat. La transferència de nous coneixements a l’atenció sanitària ha estat també un dels objectius principals d’aquest projecte. A més, Synergy-COPD ha explorat noves interaccions envers la investigació biomèdica i l’atenció sanitària, amb la darrera finalitat de promoure la medicina 4P (predictiva, preventiva, personalitzada i participatòria) per a pacients amb malalties cròniques. Aquesta tesi doctoral contribueix amb Synergy-COPD en dos aspectes específics: 1. Un anàlisi quantitatiu de la relació entre la oxigenació cel•lular i la producció de radicals lliures d’oxigen (ROS) a nivell mitocondrial. 2. Diversos desenvolupament tecnològics adreçats a la transferència de coneixement biomèdic envers a l’atenció sanitària i la investigació biomèdica. HIPÒTESIS La hipòtesi general d’aquesta tesi doctoral és que una personalització de l’avaluació del risc per a la salut i la estratificació de pacients ha de desencadenar en una atenció sanitària més eficient i orientada envers pacient. Específicament aquesta tesi doctoral planteja la hipòtesi de que el modelatge mecanicista del sistema de transport i utilització d’oxigen, tenint en compte la funció mitocondrial, pot contribuir a avaluar els efectes biològics d ela hipòxia cel•lular i el seu paper a la disfunció del múscul esquelètic a la MPOC. D’altra banda, aquesta tesi doctoral planteja també la hipòtesi referent a que un disseny holístic basat en les TIC pot contribuir a un desplegament efectiu de SAI-TIC per a pacients crònics, fomentant l’aplicació dels assoliments de la investigació orientada a sistemes a l’assistència sanitària. OBJECTIUS La integració de un modelatge fisiològic del sistema de transport i utilització d’oxigen amb el modelatge bioquímic de la generació mitocondrial de ROS, amb la finalitat d’analitzar les relacions entre la oxigenació del múscul esquelètic i la producció mitocondrial de ROS. El desenvolupament d’eines TIC que donin suport a Serveis d’Atenció Integrada (SAI-TIC) per a pacients crònics, i per tal de fomentar la interacció entre la investigació biomèdica basada en la medicina de sistemes i l’atenció sanitària. RESULTATS PRINCIPALS Anàlisi quantitatiu de la relació entre oxigenació cel•lular i la generació mitocondrial de ROS El modelatge realitzat en aquesta tesi doctoral analitza tots els factor determinants del sistema de la cadena de transport d’oxigen. S’ha demostrat que donat un cert grau d’heterogeneïtat al múscul esquelètic es disminueix la transferència global d’oxigen més de lo que la redueix la heterogeneïtat pulmonar. D’altra banda, la heterogeneïtat observada actualment a nivell pulmonar es major que la observada al múscul, per tant la heterogeneïtat pulmonar en general té un impacte més gran sobre la transferència total d’oxigen. A més, es demostra que la heterogeneïtat muscular incrementa el rang de nivells d’oxigenació cel•lular (PmO2), i a regions del múscul esquelètic amb una major aportació sanguínia en comparació a la capacitat metabòlica, els valors de PmO2 poden excedir els valors d’oxigenació venosa mixta. Malauradament, la mesura del nivell d’heterogeneïtat funcional al múscul esquelètic és molt insuficient degut a les limitacions tecnològiques. El model indica que la relació entre la capacitat de transport d’oxigen i utilització d’oxigen determina principalment els valors d’oxigenació cel•lular PmO2. Aquest fenomen, pot ser molt rellevant després d’un procés d’entrenament d’alta intensitat a pacients MPOC amb limitacions de transport d’oxigen degut a la malaltia pulmonar. Les simulacions utilitzant dades mesurades en subjectes sans realitzant exercici màxim han desvelat que l’altitud desencadena una alta producció de ROS mitocondrial a les regions del múscul esquelètic amb una altra capacitat mitocondrial però amb una limitada capacitat d’aportació d’oxigen. Aquesta observació és evident a partir d’una altitud corresponent a uns 5000 metres sobre el nivell del mar. Per sobre d’aquesta altitud no existeix cap assentament humà permanentment habitat i els humans experimenten una pèrdua inexorable de massa corporal. Però, es conclou que l’ús del model integrat en condicions de malaltia requereix una millor estimació dels paràmetres mitocondrials. Suport TIC per al desplegament de serveis d’atenció integrada (SAI-TIC) i la interacció entre l’atenció sanitària i la investigació biomèdica basada en la medicina de sistemes S’ha desenvolupat una plataforma tecnològica modular que proporciona un conjunt bàsic d’eines i tecnologies per donar suport a la implementació de SAI-TIC per a pacients crònics. Aquesta plataforma tecnològica ha suportat de manera eficient els quatre SAI dissenyats i avaluats en el context del projecte europeu NEXES (2008-2013, www.nexeshealth.eu) a un dels districtes sanitaris de Barcelona, amb un total de 540.000 habitants, i ha mostrat potencial d’escalabilitat a nivell regional. El concepte de “Digital Health Framework (DHF)” ha estat articulat amb la finalitat d’enllaçar l’atenció sanitària i a la investigació biomèdica basada en la medicina de sistemes. La base de coneixement de Synergy-COPD ha estat desenvolupada com a un component d’investigació del DHF per tal de fomentar la transició envers una medicina 4P. CONCLUSIONS 1. El model que integra els determinants fisiològics de la cadena de transport d’oxigen i els elements bioquímics moduladors de la formació a nivell mitocondrial de ROS, ha proporcionat, per primera vegada, un anàlisi quantitatiu de la relació entre la oxigenació cel•lular i la producció mitocondrial de ROS. El model genera resultats consistents en salut, però una millor estimació dels paràmetres mitocondrials és necessària quan s’aplica a MPOC. 2. La plataforma tecnològica per al suport de serveis d’atenció integrada (SAI) per a pacients crònics ha cobert de forma efectiva els requisits funcionals per al desplegament d’un entorn amb un únic proveïdor. Els reptes que cal afrontar per a un desplegament a nivell regional de SAI han estat identificats i s’han proposat estratègies per a la seva adopció. 3. El concepte de “Digital Health Framework (DHF)” representa un escenari on l’enllaç entre l’atenció integrada i la investigació biomèdica de medicina de sistemes han de promoure el desplegament de la medicina 4P. S’ha proposat les línies estratègiques per a una correcta adopció del DHF. 4. La base del coneixement específica per a MPOC (COPDkb) ha estat desenvolupada i analitzada en aquesta tesi doctoral, constitueix un component principal de la investigació biomèdica basada en la medicina de sistemes.[eng] BACKGROUND The epidemics of non-communicable diseases and the need for cost-containment are triggering a profound reshaping of healthcare delivery toward adoption of the Chronic Care model, involving deployment of integrated care services (ICS) with the support of information and communication technologies (ICS-ICT). In this scenario, emerging systems medicine, with a holistic mechanism-based approach to diseases, may play a relevant role in health risk assessment and patient stratification. The general aim of Synergy-COPD was to explore the potential of a systems medicine approach to improve knowledge on underlying mechanisms of chronic obstructive pulmonary disease (COPD) heterogeneity, focusing on systemic effects of the disease and co-morbidity clustering. The transfer of acquired knowledge to healthcare was also a core aim of the project. Moreover, Synergy-COPD explored novel cross talk between biomedical research and healthcare to foster deployment of 4P (Predictive, Preventive, Personalized and Participatory) Medicine for patients with chronic disorders. The current PhD thesis contributed to Synergy-COPD focusing on two specific areas: i) a quantitative analysis of the relationships between cellular oxygenation and mitochondrial reactive oxygen species (ROS) generation; and, ii) different ICT developments addressing transfer of knowledge to healthcare and the interplay with biomedical research. HYPOTHESIS The overarching hypothesis of this PhD thesis is that subject-specific health risk assessment and stratification may lead to novel and a more efficient patient-oriented healthcare delivery. Specifically, the current PhD studies hypothesize that predictive mechanistic modeling integrating oxygen pathway and mitochondrial function can contribute to assess the biological effects of cellular hypoxia and its role on skeletal muscle dysfunction in COPD. Moreover, it is hypothesized that a holistic design of the ICT support may contribute to a successful deployment of ICS-ICT for chronic patients fostering the transfer of the achievements of systems-oriented research into healthcare. OBJECTIVES To integrate physiological modeling of the O2 pathway and biochemical modeling of mitochondrial ROS generation to quantitatively analyze the relationships between skeletal muscle oxygenation and mitochondrial ROS generation. To develop ICT tools supporting Integrated Care Services (ICS-ICT) for chronic patients, as well as innovative cross talk between systems-oriented biomedical research and healthcare. MAIN FINDINGS Quantitative analysis between cellular oxygenation and mitochondrial ROS generation The model analyzed the role of all the physiological determinants of the O2 pathway. It was shown that a given degree of heterogeneity in the skeletal muscle reduces overall O2 transfer more than does lung heterogeneity, but actually observed heterogeneity in lung is greater than in muscle, so that lung heterogeneity has a greater impact on overall O2 transport. In addition, muscle heterogeneity showed to increase the range of skeletal muscle PmO2 values, and in regions with a low ratio of metabolic capacity to blood flow, mitochondrial PO2 (PmO2) could exceed that of mixed tissue venous blood. Unfortunately, assessment of skeletal muscle functional heterogeneities is highly limited due to technological constraints. The model indicates that the ratio between O2 transport capacity and mitochondrial O2 utilization potential determines PmO2. The phenomenon might be highly relevant after high intensity resistance training in COPD patients with limitation of O2 transport due to the pulmonary disease. Simulations using data from healthy subjects during maximal exercise revealed that altitude triggers high mitochondrial ROS production in skeletal muscle regions with high metabolic capacity, but limited O2 delivery, already evident at approx. 17,000 ft. above sea level. This is the altitude above which permanent human habitation does not occur, and the altitude above which humans experience inexorable loss of body mass. However, it is concluded that the use of the integrated model in disease conditions requires further refinement of mitochondrial parameter estimation. ICT-support to the deployment of integrated care services (ICS-ICT) and to the cross talk between healthcare and systems-oriented biomedical research An open and modular platform was developed to provide the common basic set of tools and technologies to support the implementation of ICS-ICT for chronic patients. The platform has effectively covered the four ICS developed and assessed within the NEXES European project (2008-2013, www.nexeshealth.eu) in one of Barcelona’s Health Care Districts accounting for 540.000 inhabitants, and has shown potential for further deployment at regional level. The concept of the Digital Health Framework (DHF) was articulated to provide linkage between healthcare and innovative systems-oriented biomedical research. The Synergy-COPD knowledge base was developed as a component of the DHF-research to enforce the transition toward 4P medicine. CONCLUSIONS 1. The model integrating physiological determinants of the O2 pathway and biochemical modulators of mitochondrial ROS formation provides, for the first time, a quantitative assessment of the relationships between cellular oxygenation and mitochondrial ROS production. The model generates consistent results in health, but parameter estimations when applied to COPD needs refinement. 2. The ICT-platform supporting integrated care services (ICS) for chronic patients effectively covered the functional requirements for deployment within a single-provider environment. The challenges to be faced for regional deployment of the ICS were identified and strategies for adoption have been proposed. 3. The Digital Health Framework (DHF) conceptualizes a scenario for an effective cross talk between integrated care and systems-oriented biomedical research that should foster deployment of 4P medicine. Future steps for adoption of the DHF have been proposed. 4. The COPD-specific knowledge base (COPDkb), developed and assessed in the current PhD thesis, constitutes a pivotal component of systems-oriented biomedical research.[spa] INTRODUCCIÓN La proliferación de enfermedades no transmisibles y la creciente necesidad de contención de costes están desencadenando un profundo rediseño de la atención sanitaria hacia la adopción de un modelo de atención a crónicos, involucrando la implementación de servicios de atención integrada (SAI) con el soporte de las tecnologías de la información y de la comunicación (SAI-TIC). En este escenario, la emergente medicina de sistemas, con una aproximación holística basada en los mecanismos de las enfermedades, juega un papel muy relevante en la evaluación del riesgo para la salud y la estratificación de pacientes. El objetivo principal de Synergy-COPD ha sido la exploración del potencial de una aproximación de medicina de sistemas para mejorar el conocimiento de los mecanismos subyacentes a la heterogeneidad de la enfermedad pulmonar obstructiva crónica (EPOC). Haciendo énfasis en los efectos sistémicos de la enfermedad, así como en la comorbilidad. La transferencia de nuevos conocimiento a la atención sanitaria ha sido también un objetivo principal del proyecto. Por otro lado, Synergy-COPD ha explorado nuevas interacciones entre investigación biomédica y atención sanitaria, con la finalidad última de promover la medicina 4P (Predictiva, Preventiva, Personalidad y Participativa) para pacientes con enfermedades crónicas. Esta tesis doctoral contribuye con Synergy-COPD en dos aspectos específicos: 1. Un análisis cuantitativo de la relación entre la oxigenación celular y la producción de radicales libres de oxígeno (ROS) a nivel mitocondrial. 2. Diversos desarrollos tecnológicos dirigidos a la trasferencia de conocimiento biomédico a la atención sanitaria y la investigación biomédica. HIPÓTESIS La hipótesis general de esta tesis doctoral es que una personalización de la evaluación del riesgo para la salud y la estratificación, tiene que desencadenar una atención sanitaria más eficiente y orientada al paciente. Específicamente, esta tesis doctoral plantea la hipótesis de que un modelado mecanicista del sistema de transporte y utilización de oxígeno, teniendo en cuenta la función mitocondrial, puede contribuir a evaluar los efectos biológicos de la hipoxia celular y su papel en la disfunción del músculo esquelético en la EPOC. Por otra parte, se plantea la hipótesis de que un diseño holístico basado en las TIC puede contribuir a una implementación exitosa de SAI-TIC para los pacientes crónicos, fomentando la transferencia de los logros de la investigación orientada a sistemas en la asistencia sanitaria. OBJETIVOS La integración del modelado fisiológico del sistema de transporte y utilización de oxígeno con el modelado bioquímico de la generación mitocondrial de ROS, con la finalidad de analizar las relaciones entre la oxigenación del músculo esquelético y la producción mitocondrial de ROS. El desarrollo de herramientas TIC que den soporte a servicios de atención integrada (SAI-TIC) para pacientes crónicos, y que fomenten la interacción entre la investigación biomédica basada en la medicina de sistemas y la atención sanitaria. RESULTADOS PRINCIPALES Análisis cuantitativo de la relación entre oxigenación celular y la generación mitocondrial de ROS El modelaje realizado en esta tesis doctoral analiza todos los factores determinantes de la cadena de transporte de oxígeno. Se ha mostrado que un determinado grado de heterogeneidad en el músculo esquelético reduce la transferencia global de oxígeno más de lo que la reduce la heterogeneidad pulmonar. Sin embargo, la heterogeneidad observada actualmente a nivel pulmonar es mayor que la observada en músculo, por lo tanto, la heterogeneidad pulmonar en general tiene un impacto mayor sobre la transferencia total de oxígeno. Por otra parte, hemos mostrado que la heterogeneidad muscular incrementa el rango de niveles de oxigenación celular (PmO2), y en regiones del músculo esquelético con un mayor aporte sanguíneo en comparación con la capacidad metabólica, los valores de PmO2 pueden exceder los correspondientes valores de oxigenación venosa mixta. Desafortunadamente, la medición del nivel de heterogeneidad funcional en músculo esquelético es muy insuficiente debido a las limitaciones tecnológicas. El modelo indica que la relación entre la capacidad de transporte y utilización de oxígeno determina principalmente los valores de oxigenación celular (PmO2). Este fenómeno, puede que sea muy relevante después de un proceso de entrenamiento de alta intensidad en pacientes EPOC con limitaciones de transporte de oxígeno debido a la enfermedad pulmonar. Simulaciones utilizando datos medidos en sujetos sanos realizando ejercicio máximo han desvelado que la altitud desencadena una alta producción de ROS mitocondrial en las regiones del músculo esquelético con una alta capacidad metabólica pero con una limitada capacidad de aporte de oxígeno. Esta observación, es evidente a partir de una altitud correspondiente a 5000 metros sobre el nivel del mar. Por encima de esta altitud no existe ningún asentamiento humano permanentemente habitado y los humanos experimentan una perdida inexorable de masa corporal. Sin embargo, se concluye que el uso del modelo integrado en condiciones de enfermedad requiere una mejor estimación de los parámetros mitocondriales. Soporte TIC para el despliegue de servicios de atención integrada (SAI-TIC) y la interacción entre la atención sanitaria y la investigación biomédica basada en la medicina de sistemas Se ha desarrollado una plataforma tecnológica modular que proporciona un conjunto básico de herramientas y tecnologías para dar soporte a la implantación de SAI-TIC para pacientes crónicos. Esta plataforma tecnológica ha soportado de manera eficiente los cuatro SAI diseñados y evaluados en el contexto del proyecto europeo NEXES (2008-2013, www.nexeshealth.eu) en uno de los distritos sanitarios de Barcelona, con un total de 540.000 habitantes, y ha mostrado potencial de escalabilidad a nivel regional. El concepto de “Digital Health Framework (DHF)” ha sido articulado con el fin de enlazar la atención sanitaria y la investigación biomédica basada en la medicina de sistemas. La basa de conocimiento de Synergy-COPD ha sido desarrollada como un componente de investigación del DHF para fomentar la transición hacia una medicina 4P. CONCLUSIONES 1. El modelo que integra los determinantes fisiológicos de la cadena de transporte de oxígeno y los elementos bioquímicos moduladores de la formación a nivel mitocondrial de ROS, ha proporcionado, por primera vez, un análisis cuantitativo de la relación entre la oxigenación celular y la producción mitocondrial de ROS. El modelo genera resultados consistentes en salud, pero una mejor estimación de los parámetros mitocondriales es necesaria cuando se aplica en EPOC. 2. La plataforma tecnológica para el soporte de servicios de atención integrada (SAI) pra pacientes crónicos ha cubierto de forma efectiva los requisitos funcionales para el despliegue en un entorno con un único proveedor. Los retos que se han afrontar un despliegue regional de SAI, han sido identificados y se han propuesto estrategias para su adopción. 3. El concepto de “Digital Health Framework (DHF)” representa un escenario en el que el enlace entre atención integrada e investigación biomédica de medicina de sistemas debe promover el despliegue de la medicina 4P. Se han propuesto líneas estratégicas para una correcta adopción del DHF. 4. La base de conocimiento específica para EPOC (COPDkb) que ha sido desarrollada y analizada en esta tesis doctoral, constituye un componente principal de la investigación biomédica basada en la medicina de sistemas