Over the past thirty years, clinical research has benefited substantially from the adoption of electronic medical record systems. As deployment has increased, so too has the number of researchers seeking to improve the overall analytical environment by way of tools and models. Although much work has been done, there are still many uninvestigated areas; two of which are explored in this dissertation. The first pertains to the physical storage of the data itself. There are two generally accepted storage models: relational and entity-attribute-value (EAV). For clinical data, EAV systems are preferred due to their natural way of managing many-to-many relationships, sparse attributes, and dynamic processes along with minimal conversion effort and reduction in federation complexities. However, the relational database management systems on which they are implemented, are not intended to organize and retrieve data in this format; eroding their performance gains. To combat this effect, we present the foundation for an EAV Database Management System (EDBMS). We discuss data conversion methodologies, formulate the requisite metadata and partitioned type-sensing index structures, and provide detailed runtime and experimental analysis with five extant methods. Our results show that the prototype, EAVDB, reduces space and conversion requirements while enhancing overall query performance. The second topic concerns query performance in a federated environment. One method used to decrease query execution time, is to pre-compute and store "beneficial" queries (views). The View Selection Problem (VSP) identifies these views subject to resource constraints. A federated model, however, has yet to be developed. In this dissertation, we submit three advances in view materialization. First, a more robust optimization function, the Minimum-Maintenance View Selection Problem (MMVSP), is derived by combining existing approaches. Second, the Federated View Selection Problem (FVSP), built upon the MMVSP, and federated data cube lattice are formalized. The FVSP allows for multiple querying nodes, partial and full materialization, and data propagation constriction. The latter two are shown to greatly reduce the overall number of valid solutions within the solution space and thus a novel, multi-tiered approach is given. Lastly, EAV materialization, which is introduced in this dissertation, is incorporated into an expanded, multi-modal variant of the FVSP. As models and heuristics for both the federated and EAV VSP, to the best of our knowledge, do not exist, this research defines two new branches of data warehouse optimization. Coupled with our EDBMS design, this dissertation confronts two main challenges associated with clinical data warehousing and federation.

In the development process of novel cancer drugs, one important aspect is to identify patient populations with a high risk of early death so that resources can be focused on patients with the highest medical unmet need. Many cancer types are heterogeneous and there is a need to identify patients with aggressive diseases, meaning a high risk of early death, compared to patients with indolent diseases, meaning a low risk of early death. Predictive modeling can be a useful tool for risk stratification in clinical practice, enabling healthcare providers to treat high-risk patients early and progressively, while applying a less aggressive watch-and-wait strategy for patients with a lower risk of death. This is important from a clinical perspective, but also a health economic perspective since society has limited resources, and costly drugs should be given to patients that can benefit the most from a specific treatment. Thus, the goal of predictive modeling is to ensure that the right patient will have access to the right drug at the right time. In the era of personalized medicine, Artificial Intelligence (AI) applied to high-quality data will most likely play an important role and many techniques have been developed. In particular, Graph Neural Network (GNN) is a promising tool since it captures the complexity of high dimensional data modeled as a graph. In this work, we have applied Network Representation Learning (NRL) techniques to predict survival, using pseudonymized patient-level data from national health registries in Sweden. Over the last decade, more health data of increased complexity has become available for research, and therefore precision medicine could take advantage of this trend by bringing better healthcare to the patients. However, it is important to develop reliable prediction models that not only show high performances but take into consideration privacy, avoiding any leakage of personal information. The present study contributes novel insights related to GNN performance in different survival prediction tasks, using population-based unique nationwide data. Furthermore, we also explored how privacy methods impact the performance of the models when applied to the same dataset. We conducted a set of experiments across 6 dataset using 8 models measuring both AUC, Precision and Recall. Our evaluation results show that Graph Neural Networks were able to reach accuracy performance close to the models used in clinical practice and constantly outperformed, by at least 4.5%, the traditional machine learning methods. Furthermore, the study demonstrated how graph modeling, when applied based on knowledge from clinical experts, performed well and showed high resiliency to the noise introduced for privacy preservation. / I utvecklingsprocessen för nya cancerläkemedel är en viktig aspekt att identifiera patientgrupper med hög risk för tidig död, så att resurser kan fokuseras på patientgrupper med störst medicinskt behov. Många cancertyper är heterogena och det finns ett behov av att identifiera patienter med aggressiv sjukdom, vilket innebär en hög risk för tidig död, jämfört med patienter med indolenta sjukdom, vilket innebär lägre risk för tidig död. Prediktiv modellering kan vara ett användbart verktyg för riskstratifiering i klinisk praxis, vilket gör det möjligt för vårdgivare att behandla patienter olika utifrån individuella behov. Detta är viktigt ur ett kliniskt perspektiv, men också ur ett hälsoekonomiskt perspektiv eftersom samhället har begränsade resurser och kostsamma läkemedel bör ges till de patienter som har störst nytta av en viss behandling. Målet med prediktiv modellering är således att möjliggöra att rätt patient får tillgång till rätt läkemedel vid rätt tidpunkt. Framför allt är Graph Neural Network (GNN) ett lovande verktyg eftersom det fångar komplexiteten hos högdimensionella data som modelleras som ett diagram. I detta arbete har vi tillämpat tekniker för inlärning av grafrepresentationer för att prediktera överlevnad med hjälp av pseudonymiserade data från nationella hälsoregister i Sverige. Under det senaste decennierna har mer hälsodata av ökad komplexitet blivit tillgänglig för forskning. Även om denna ökning kan bidra till utvecklingen av precisionsmedicinen är det viktigt att utveckla tillförlitliga prediktionsmodeller som tar hänsyn till patienters integritet och datasäkerhet. Den här studien kommer att bidra med nya insikter om GNNs prestanda i prediktiva överlevnadsmodeller, med hjälp av populations -baserade data. Dessutom har vi också undersökt hur integritetsmetoder påverkar modellernas prestanda när de tillämpas på samma dataset. Sammanfattningsvis, Graph Neural Network kan uppnå noggrannhets -prestanda som ligger nära de modeller som tidigare använts i klinisk praxis och i denna studie preserade de alltid bättre än traditionella maskininlärnings -metoder. Studien visisade vidare hur grafmodellering som utförs i samarbete med kliniska experter kan vara effektiva mot det brus som införs av olika integritetsskyddstekniker.

In this dissertation, we present a collection of manuscripts describing the development of prognostic models designed to assist clinical decision making. This work is motivated by limitations of commonly used techniques to produce accessible prognostic models with easily interpretable and clinically credible results. Such limitations hinder prognostic model widespread utilization in medical practice. Our methodology is based on Rough Set Theory (RST) as a mathematical tool for clinical data anal- ysis. We focus on developing rule-based prognostic models for end-of life care decision making in an effort to improve the hospice referral process. The development of the prognostic models is demonstrated using a retrospective data set of 9,103 terminally ill patients containing physiological characteristics, diagnostic information and neurological function values. We develop four RST-based prognostic models and compare them with commonly used classification techniques including logistic regression, support vector machines, random forest and decision trees in terms of characteristics related to clinical credibility such as accessibility and accuracy. RST based models show comparable accuracy with other methodologies while providing accessible models with a structure that facilitates clinical interpretation. They offer both more insight into the model process and more opportunity for the model to incorporate personal information of those making and being affected by the decision.

In the BDAS project one of the goals is to create a new solution for monitoring bone healing to complement current techniques. Data have been collected in clinical trials from infants treated for Craniosynostosis by a craniotomic surgery. The data are collected with a biomedical sensor based in microwave technology. This sensor could be able to sense changes in the composition of the different tissues in the upper hemisphere of the head, by noticing a difference in the propagation of the microwaves, as the bone injury from the craniectomy heals over time. In this thesis I analyze the validity of a proposed analytical model for the biosensor and extend the clinical data analysis in BDAS project. The validity of the model is analyzed by comparing its outcomes to available measurements from phantoms mimicking living tissues and to numerical simulations. In the data analysis two hypotheses are formulated and tested regarding the location of the measurement points with respect to a positioning grid and the healing over time too. By deriving a set of parameters for each collected dataset in the clinical trials, a distinct pattern was found which shows visible changes over the course of the healing process with this technique.

Pharmacokinetic and pharmacodynamic models can be applied to clinical study data using various modelling approaches depending on the aim of the analysis. In population pharmacokinetics for instance, simple compartmental models can be employed to describe concentration-time data, identify prognostic factors and interpolate within well-defined experimental conditions. The first objective of this thesis was to illustrate such a ‘semi-mechanistic’ pharmacokinetic modelling approach using mavoglurant as an example of a compound under clinical development. In particular, methods to accurately characterise complex oral pharmacokinetic profiles and evaluate the impact of absorption factors were investigated. When the purpose of the model-based analysis is to further extrapolate beyond the experimental conditions in order to guide the design of subsequent clinical trials, physiologically-based pharmacokinetic (PBPK) models are more valuable as they incorporate information not only on the drug but also on the system, i.e. on mammillary anatomy and physiology. The combination of such mechanistic models with statistical modelling techniques in order to analysis clinical data has been widely applied in toxicokinetics but has only recently received increasing interest in pharmacokinetics. This is probably because, due to the higher complexity of PBPK models compared to conventional pharmacokinetic models, additional efforts are required for adequate population data analysis. Hence, the second objective of this thesis was to explore methods to allow the application of PBPK models to clinical study data, such as the Bayesian approach or model order reduction techniques, and propose a general mechanistic modelling workflow for population data analysis. In pharmacodynamics, mechanistic modelling of clinical data is even less common than in pharmacokinetics. This is probably because our understanding of the interaction between therapeutic drugs and biological processes is limited and also because the types of data to analyse are often more complex than pharmacokinetic data. In oncology for instance, the most widely used clinical endpoint to evaluate the benefit of an experimental treatment is survival of patients. Survival data are typically censored due to logistic constraints associated with patient follow-up. Hence, the analysis of survival data requires specific statistical techniques. Longitudinal tumour size data have been increasingly used to assess treatment response for solid tumours. In particular, the survival prognostic value of measures derived from such data has been recently evaluated for various types of cancer although not for pancreatic cancer. The last objective of this thesis was therefore to investigate different modelling approaches to analyse survival data of pancreatic cancer patients treated with gemcitabine, and compare tumour burden measures with other patient clinical characteristics and established risk factors, in terms of predictive value for survival.

Les données cliniques sont produites par différents professionnels de santé, dans divers lieux et sous diverses formes dans le cadre de la pratique de la médecine. Elles présentent par conséquent une hétérogénéité à la fois au niveau de leur nature et de leur structure mais également une volumétrie particulièrement importante et qualifiable de massive. Le travail réalisé dans le cadre de cette thèse s’attache à proposer une méthode de recherche d’information efficace au sein de ce type de données complexes et massives. L’accès aux données cliniques se heurte en premier lieu à la nécessité de modéliser l’informationclinique. Ceci peut notamment être réalisé au sein du dossier patient informatisé ou, dans une plus large mesure, au sein d’entrepôts de données. Je propose dans ce mémoire unepreuve de concept d’un moteur de recherche permettant d’accéder à l’information contenue au sein de l’entrepôt de données de santé sémantique du Centre Hospitalier Universitaire de Rouen. Grâce à un modèle de données générique, cet entrepôt adopte une vision de l’information assimilable à un graphe de données rendant possible la modélisation de cette information tout en préservant sa complexité conceptuelle. Afin de fournir des fonctionnalités de recherche adaptées à cette représentation générique, un langage de requêtes permettant l’accès à l’information clinique par le biais des diverses entités qui la composent a été développé et implémenté dans le cadre de cette thèse. En second lieu, la massivité des données cliniques constitue un défi technique majeur entravant la mise en oeuvre d’une recherche d’information efficace. L’implémentation initiale de la preuve de concept sur un système de gestion de base de données relationnel a permis d’objectiver les limites de ces derniers en terme de performances. Une migration vers un système NoSQL orienté clé-valeur a été réalisée. Bien qu’offrant de bonnes performances d’accès atomique aux données, cette migration a également nécessité des développements annexes et la définition d’une architecture matérielle et applicative propice à la mise en oeuvre des fonctionnalités de recherche et d’accès aux données. Enfin, l’apport de ce travail dans le contexte plus général de l’entrepôt de données de santé sémantique du CHU de Rouen a été évalué. La preuve de concept proposée dans ce travail a ainsi été exploitée pour accéder aux descriptions sémantiques afin de répondre à des critères d’inclusion et d’exclusion de patients dans des études cliniques. Dans cette évaluation, une réponse totale ou partielle a pu être apportée à 72,97% des critères. De plus, la généricité de l’outil a également permis de l’exploiter dans d’autres contextes tels que la recherche d’information documentaire et bibliographique en santé. / Clinical data are produced as part of the practice of medicine by different health professionals, in several places and in various formats. They therefore present an heterogeneity both in terms of their nature and structure and are furthermore of a particularly large volume, which make them considered as Big Data. The work carried out in this thesis aims at proposing an effective information retrieval method within the context of this type of complex and massive data. First, the access to clinical data constrained by the need to model clinical information. This can be done within Electronic Health Records and, in a larger extent, within data Warehouses. In this thesis, I proposed a proof of concept of a search engine allowing the access to the information contained in the Semantic Health Data Warehouse of the Rouen University Hospital. A generic data model allows this data warehouse to view information as a graph of data, thus enabling to model the information while preserving its conceptual complexity. In order to provide search functionalities adapted to this generic representation of data, a query language allowing access to clinical information through the various entities of which it is composed has been developed and implemented as a part of this thesis’s work. Second, the massiveness of clinical data is also a major technical challenge that hinders the implementation of an efficient information retrieval. The initial implementation of the proof of concept highlighted the limits of a relational database management systems when used in the context of clinical data. A migration to a NoSQL key-value store has been then completed. Although offering good atomic data access performance, this migration nevertheless required additional developments and the design of a suitable hardware and applicative architecture toprovide advanced search functionalities. Finally, the contribution of this work within the general context of the Semantic Health Data Warehouse of the Rouen University Hospital was evaluated. The proof of concept proposed in this work was used to access semantic descriptions of information in order to meet the criteria for including and excluding patients in clinical studies. In this evaluation, a total or partial response is given to 72.97% of the criteria. In addition, the genericity of the tool has also made it possible to use it in other contexts such as documentary and bibliographic information retrieval in health.

Purpose – The purpose of this study is to provide a structured process to evaluate data sharing opportunities. In doing so, we provide a three phase process that assists data owners to increase utilisation of their resource, as well as introducing the possibility to scale such a process to other industries through future research. Method – To gain insights, thematic analysis was used on data collected through a single case study in three separate waves of interviews, as well as through observations. A total of 13 respondents were involved, all industry experts from a global pharmaceutical company working actively with the researched question. Findings – The findings resulted in 25 challenges with the current evaluation process, segmented into 11 sub themes and four main themes: Unstructured process for evaluating data sharing, Unclear information gathering requirements, Lack of objective evaluation criteria, and Uncertain decision making. Theoretical contribution – This study contributes to the existing literature by conceptualising challenges with evaluating data sharing opportunities. Furthermore, by applying principles and logic of Stage-Gate methodology, the thesis introduces a more structured way of evaluating data sharing opportunities. Practical contribution – This study introduces a process for data owners and companies within the pharmaceutical industry that facilitate a smoother and more efficient workflow when faced with data sharing opportunities. Our three phase process promotes utilisation to increase development through data sharing. Limitations of the study – The case study was limited to a single company that imposed the risk of bias and misguided focus. We propose future research to trial the recommended process in other companies within the pharmaceutical industry as well as introduce it to other data focused industries. / Syfte - Syftet med den här studien är att tillhandahålla en strukturerad process för att utvärdera möjligheter till datadelning. Därmed tillhandahåller vi en process i tre faser som hjälper dataägare att öka utnyttjandet av sina resurser och som ger möjlighet att skala upp en sådan process till andra branscher genom framtida forskning. Metod - För att få insikter användes tematisk analys av data som samlats in genom en enda fallstudie i tre separata intervjuer samt genom observationer. Totalt deltog 13 respondenter, alla branschexperter från ett globalt läkemedelsföretag som arbetar aktivt med den aktuella forskningsfrågan. Resultat - Resultaten resulterade i 25 utmaningar med den nuvarande utvärderingsprocessen, uppdelade i 11 underteman och fyra huvudteman: Ostrukturerad process för utvärdering av datadelning, otydliga krav på informationsinsamling, brist på objektiva utvärderingskriterier och osäkert beslutsfattande. Teoretiskt bidrag - Den här studien bidrar till den befintliga litteraturen genom att konceptualisera utmaningar med att utvärdera möjligheter till datadelning. Genom att tillämpa principerna och logiken i Stage-Gate-metodiken introducerar avhandlingen dessutom ett mer strukturerat sätt att utvärdera möjligheter till datadelning. Praktiskt bidrag - I denna studie introduceras en process för dataägare och företag inom läkemedelsindustrin som underlättar ett smidigare och effektivare arbetsflöde när de ställs inför möjligheter till datadelning. Vår process i tre faser främjar utnyttjandet för att öka utvecklingen genom datadelning. Begränsningar i studien - Fallstudien var begränsad till ett enda företag, vilket medförde en risk för bias och missriktad fokus. Vi föreslår framtida forskning för att testa den rekommenderade processen i andra företag inom läkemedelsindustrin samt införa den i andra datafokuserade branscher.

Machine learning has the potential of significantly improving daily operations in health care institutions but many persistent barriers are to be faced in order to ensure its wider acceptance. Among such obstacles are the accuracy and reliability. For a decision support system to be entrusted by the medical staff in clinical situations, it must perform with an accuracy comparable to or surpassing that of human medics, as well ashaving a universal applicability and not being subject to any bias. In this paper the MIMIC-IV Clinical Database will be utilized in order to: (1) Predict patient mortality and its associated risk factors in intensive care units (ICU) and: (2) Assess the reliability of utilizing the database as a basis for a clinical decision system. The cohort consisted of 523,740 hospitalizations, matched with each respective admitting diagnoses in ICD-9 format. The diagnoses were then converted from code to text-format, with the most frequently occurring factors (words) observed in deceased and surviving patients being analyzed with an Natural language Processing (NLP) algorithm. The results concluded that many of the observed risk factors were self-evident while others required further explanation, and that the performance was highly by selection of hyperparameters. Finally, the MIMIC-IV database can serve as a stable foundation for a clinical decision system but its reliability and universality shall also be taken into consideration. / Maskininlärninstekniker har en stor potential att gynna sjukvården men står inför ett flertal hinder för att fullständigt kunna tillämpas. Framförallt bör modellernas tolkningsbarhet och reproducerbarhet beaktas. För att att ett kliniskt beslutstodssystem skall vara fullständigt anförtrott av sjukvårdspersonal måste det kunna prestera med en jämförbar eller högre träffsäkerhet än sjukvårdspersonal, samt kunna tillämpas i åtskilliga sammanhang utan någon subjektivitet. Syftet med denna studie är att: (1) Förutspå patientdödsfall i intensivvårdsavdelningar och utreda dess riskfaktorer genom journalförd information från databasen MIMIC-IV och: 2) Bedöma databasens tillförlitlighet som underlag för ett kliniskt beslutstödssystem. Kohorten bestod av 523,740 insjuknanden som matchades med de diagnoser som ställdes vid deras sjukhusintag. Eftersom diagnoserna inskrevs i ICD-9-format omvandlades dessa till ord och de mest förekommande faktorerna (orden) för avlidna och överlevande patienter analyserades med en NLP-model (Natural Language Processing). Resultaten konkluderade att många av de förutspådda riskfaktorerna var uppenbara medan andra krävde ytterligare klargöranden. Dessutom kunde val av hyperparametrar stort påverka modellens kvalitet. MIMIC-IV-databasen kan utgöra ett gediget underlag för ett kliniskt beslutsystem men dess tillförlitlighet och relevans bör även tas i beaktande. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Answers to the most complex biological questions are rarely determined solely from the experimental evidence. It requires subsequent analysis of many data sources that are often heterogeneous. Most biological data repositories focus on providing only one particular type of data, such as sequences, molecular interactions, protein structure, or gene expression. In many cases, it is required for researchers to visit several different databases to answer one scientific question. It is essential to develop strategies to integrate disparate biological data sources that are efficient and seamless to facilitate the discovery of novel associations and validate existing hypotheses. This thesis presents the design and development of different integration strategies of biological and clinical systems. The BioSPIDA system is a data warehousing solution that integrates many NCBI databases and other biological sources on protein sequences, protein domains, and biological pathways. It utilizes a universal parser facilitating integration without developing separate source code for each data site. This enables users to execute fine-grained queries that can filter genes by their protein interactions, gene expressions, functional annotation, and protein domain representation. Relational databases can powerfully return and generate quickly filtered results to research questions, but they are not the most suitable solution in all cases. Clinical patients and genes are typically annotated by concepts in hierarchical ontologies and performance of relational databases are weakened considerably when traversing and representing graph structures. This thesis illustrates when relational databases are most suitable as well as comparing the performance benchmarks of semantic web technologies and graph databases when comparing ontological concepts. Several approaches of analyzing integrated data will be discussed to demonstrate the advantages over dependencies on remote data centers. Intensive Care Patients are prioritized by their length of stay and their severity class is estimated by their diagnosis to help minimize wait time and preferentially treat patients by their condition. In a separate study, semantic clustering of patients is conducted by integrating a clinical database and a medical ontology to help identify multi-morbidity patterns. In the biological area, gene pathways, protein interaction networks, and functional annotation are integrated to help predict and prioritize candidate disease genes. This thesis will present the results that were able to be generated from each project through utilizing a local repository of genes, functional annotations, protein interactions, clinical patients, and medical ontologies.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2017-01-16T10:13:02Z No. of bitstreams: 2 Dissertação - Áurea Valéria Pereira da Silva - 2016.pdf: 2576793 bytes, checksum: 3b3472812df3319818244bcd2e0482d7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-01-16T10:13:30Z (GMT) No. of bitstreams: 2 Dissertação - Áurea Valéria Pereira da Silva - 2016.pdf: 2576793 bytes, checksum: 3b3472812df3319818244bcd2e0482d7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-01-16T10:13:30Z (GMT). No. of bitstreams: 2 Dissertação - Áurea Valéria Pereira da Silva - 2016.pdf: 2576793 bytes, checksum: 3b3472812df3319818244bcd2e0482d7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-12-14 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Introduction: OpenEHR is a standardization of Health Information Systems (HIS) that is mainly concerned with the exchange of Electronic Health Records (EHR). One of the major obstacles to the adoption of openEHR is the lack of database persistence specifications. Objective: The aim of this work is the mapping of storage structures from the Archetype Definition Language (ADL) specifications which describe clinical knowledge structure. Method: This research initially makes a systematic mapping about persistence structures that are derived directly from ADL specifications, i.e. without dependences from the Reference Model (RM) of openEHR. A new persistence strategy is proposed and compared with ones found in the literature. Results: Assumptions, criteria and rules were used to present the proposed approach. Its evaluation considered quantitative aspects of persistence structures. Conclusion: The evaluation revealed some advantages of proposed approach, such as: reductions of reference attributes (referential integrity) and redundancy in clinical database; production of unidentified clinical records in relation to the patient; creation of a single base table for each archetype, regardless of its use in the form of slots; scalability of database schema (stable number of database tables), even in the occurrence of recursion through slots. / Introdução: OpenEHR é uma padronização dos Sistemas de Informação em Saúde (SIS) que se preocupa principalmente com a troca de Registros de Saúde Eletrônicos (RES). Um dos maiores obstáculos à adoção do openEHR é a carência de especificações de persistência de banco de dados. Objetivo: O objetivo deste trabalho é o mapeamento de estruturas de armazenamento a partir das especificações Archetype Definition Language (ADL) que descrevem a estrutura do conhecimento clínico. Métodos: Esta pesquisa inicialmente faz um mapeamento sistemático sobre estruturas de persistência que são derivadas diretamente de especificações ADL, isto é, sem dependências do Modelo de Referência (RM) de openEHR. Uma nova estratégia de persistência é proposta e comparada com as encontradas na literatura. Resultados: Foram utilizados pressupostos, critérios e regras para apresentar a abordagem proposta. Uma avaliação considerou aspectos quantitativos das estruturas de persistência, em comparação com o que foi encontrado na literatura. Conclusões: A avaliação revelou algumas vantagens da abordagem proposta, tais como: reduções de atributos de referência (integridade referencial) e redundância em banco de dados clínicos; produção de registros clínicos não identificados em relação ao paciente; criação de uma tabela de base única para cada arquétipo, independentemente da sua utilização sob a forma de slots; escalabilidade do esquema de banco de dados (número estável de tabelas de banco de dados), mesmo na ocorrência de recursão através de slots.