Data mining framework

The purpose of this document is building a framework for working with clinical data. Vast amounts of clinical records, stored in health repositories, contain information that can be used to improve the quality of health care. However, the information generated from these records depends vastly on the manner, in which the data is arranged. A number of factors need to be considered, before information can be extracted from the patient records. This document deals with the preparation of a framework for the data, before it can be mined.;One of the issues to deal with is information about the patient contained in the clinical records that can be used for identification purposes. A means to create anonymous records is discussed in this document. Once the records have been de-identified, they can be used for data mining. In addition to storing patient records, the document also discusses the possibility of \u27abstracting\u27 information from these documents and storing them in the repository. Information generated from the combination of patient records and abstracted information, could be used to improve the quality of health care.;This document also discusses the possibility of creating a means to query information from the data repository. A prototype application, which provides all these facilities in a form that can be accessed from any remote location, is discussed. In addition, the prospect of using Clinical Document Architecture format to store the clinical records is explored

Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping

Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph

Towards a Learning Health System: a SOA based platform for data re-use in chronic infectious diseases

Abstract Information and Communication Technology (ICT) tools can efficiently support clinical research by providing means to collect automatically huge amount of data useful for the management of clinical trials conduction. Clinical trials are indispensable tools for Evidence-Based Medicine and represent the most prevalent clinical research activity. Clinical trials cover only a restricted part of the population that respond to particular and strictly controlled requirements, offering a partial view of the overall patients\u2019 status. For instance, it is not feasible to consider patients with comorbidities employing only one kind of clinical trial. Instead, a system that have a comprehensive access to all the clinical data of a patient would have a global view of all the variables involved, reflecting real-world patients\u2019 experience. The Learning Health System is a system with a broader vision, in which data from various sources are assembled, analyzed by various means and then interpreted. The Institute of Medicine (IOM) provides this definition: \u201cIn a Learning Health System, progress in science, informatics, and care culture align to generate new knowledge as an ongoing, natural by-product of the care experience, and seamlessly refine and deliver best practices for continuous improvement in health and health care\u201d. The final goal of my project is the realization of a platform inspired by the idea of Learning Health System, which will be able to re-use data of different nature coming from widespread health facilities, providing systematic means to learn from clinicians\u2019 experience to improve both the efficiency and the quality of healthcare delivery. The first approach is the development of a SOA-based architecture to enable data collection from sparse facilities into a single repository, to allow medical institutions to share information without an increase in costs and without the direct involvement of users. Through this architecture, every single institution would potentially be able to participate and contribute to the realization of a Learning Health System, that can be seen as a closed cycle constituted by a sequential process of transforming patient-care data into knowledge and then applying this knowledge to clinical practice. Knowledge, that can be inferred by re-using the collected data to perform multi-site, practice-based clinical trials, could be concretely applied to clinical practice through Clinical Decision Support Systems (CDSS), which are instruments that aim to help physicians in making more informed decisions. With 4 this objective, the platform developed not only supports clinical trials execution, but also enables data sharing with external research databases to participate in wider clinical trials also at a national level without effort. The results of these studies, integrated with existing guidelines, can be seen as the knowledge base of a decision support system. Once designed and developed, the adoption of this system for chronical infective diseases management at a regional level helped in unifying data all over the Ligurian territory and actively monitor the situation of specific diseases (like HIV, HCV and HBV) for which the concept of retention in care assumes great importance. The use of dedicated standards is essential to grant the necessary level of interoperability among the structures involved and to allow future extensions to other fields. A sample scenario was created to support antiretroviral drugs prescription in the Ligurian HIV Network setting. It was thoroughly tested by physicians and its positive impact on clinical care was measured in terms of improvements in patients\u2019 quality of life, prescription appropriateness and therapy adherence. The benefits expected from the employment of the system developed were verified. Student\u2019s T test was used to establish if significant differences were registered between data collected before and after the introduction of the system developed. The results were really acceptable with the minimum p value in the order of 10 125 and the maximum in the order of 10 123. It is reasonable to assess that the improvements registered in the three analysis considered are ascribable to this system introduction and not to other factors, because no significant differences were found in the period before its release. Speed is a focal point in a system that provides decision support and it is highly recognized the importance of velocity optimization. Therefore, timings were monitored to evaluate the responsiveness of the system developed. Extremely acceptable results were obtained, with the waiting times of the order of 10 121 seconds. The importance of the network developed has been widely recognized by the medical staff involved, as it is also assessed by a questionnaire they compiled to evaluate their level of satisfaction

DACTyL:towards providing the missing link between clinical and telehealth data

This document conveys the findings of the Data Analytics, Clinical, Telehealth, Link (DACTyL) project. This nine-month project started at January 2013 and was conducted at Philips Research in the Care Management Solution group and as part of the Data Analysis for Home Healthcare (DA4HH) project. The DA4HH charter is to perform and support retrospective analyses of data from Home Healthcare products, such as Motiva telehealth. These studies will provide valid insights in actual clinical aspects, usage and behavior of installed products and services. The insights will help to improve service offerings, create clinical algorithms for better outcome, and validate and substantiate claims on efficacy and cost-effectiveness. The current DACTyL project aims at developing and implementing an architecture and infrastructure to meet the most demanding need from Motiva telehealth customers on return on investment (ROI). These customers are hospitals that offer Motiva telehealth to their patients. In order to provide the Motiva service cost-effectively, they need to have insight into the actual cost, benefit and resource utilization when it comes to Motiva deployment compared to their usual routine care. Additional stakeholders for these ROI-related data are Motiva customer consultants and research scientists from Philips for strengthening their messaging and service deliveries to arrive at better patient care

Standards for Scalable Clinical Decision Support: Need, Current and Emerging Standards, Gaps, and Proposal for Progress

Despite their potential to significantly improve health care, advanced clinical decision support (CDS) capabilities are not widely available in the clinical setting. An important reason for this limited availability of CDS capabilities is the application-specific and institution-specific nature of most current CDS implementations. Thus, a critical need for enabling CDS capabilities on a much larger scale is the development and adoption of standards that enable current and emerging CDS resources to be more effectively leveraged across multiple applications and care settings. Standards required for such effective scaling of CDS include (i) standard terminologies and information models to represent and communicate about health care data; (ii) standard approaches to representing clinical knowledge in both human-readable and machine-executable formats; and (iii) standard approaches for leveraging these knowledge resources to provide CDS capabilities across various applications and care settings. A number of standards do exist or are under development to meet these needs. However, many gaps and challenges remain, including the excessive complexity of many standards; the limited availability of easily accessible knowledge resources implemented using standard approaches; and the lack of tooling and other practical resources to enable the efficient adoption of existing standards. Thus, the future development and widespread adoption of current CDS standards will depend critically on the availability of tooling, knowledge bases, and other resources that make the adoption of CDS standards not only the right approach to take, but the cost-effective path to follow given the alternative of using a traditional, ad hoc approach to implementing CDS

Doctor of Philosophy

dissertationBiomedical data are a rich source of information and knowledge. Not only are they useful for direct patient care, but they may also offer answers to important population-based questions. Creating an environment where advanced analytics can be performed against biomedical data is nontrivial, however. Biomedical data are currently scattered across multiple systems with heterogeneous data, and integrating these data is a bigger task than humans can realistically do by hand; therefore, automatic biomedical data integration is highly desirable but has never been fully achieved. This dissertation introduces new algorithms that were devised to support automatic and semiautomatic integration of heterogeneous biomedical data. The new algorithms incorporate both data mining and biomedical informatics techniques to create "concept bags" that are used to compute similarity between data elements in the same way that "word bags" are compared in data mining. Concept bags are composed of controlled medical vocabulary concept codes that are extracted from text using named-entity recognition software. To test the new algorithm, three biomedical text similarity use cases were examined: automatically aligning data elements between heterogeneous data sets, determining degrees of similarity between medical terms using a published benchmark, and determining similarity between ICU discharge summaries. The method is highly configurable and 5 different versions were tested. The concept bag method performed particularly well aligning data elements and outperformed the compared algorithms by iv more than 5%. Another configuration that included hierarchical semantics performed particularly well at matching medical terms, meeting or exceeding 30 of 31 other published results using the same benchmark. Results for the third scenario of computing ICU discharge summary similarity were less successful. Correlations between multiple methods were low, including between terminologists. The concept bag algorithms performed consistently and comparatively well and appear to be viable options for multiple scenarios. New applications of the method and ideas for improving the algorithm are being discussed for future work, including several performance enhancements, configuration-based enhancements, and concept vector weighting using the TF-IDF formulas

Automated Injection of Curated Knowledge Into Real-Time Clinical Systems: CDS Architecture for the 21st Century

abstract: Clinical Decision Support (CDS) is primarily associated with alerts, reminders, order entry, rule-based invocation, diagnostic aids, and on-demand information retrieval. While valuable, these foci have been in production use for decades, and do not provide a broader, interoperable means of plugging structured clinical knowledge into live electronic health record (EHR) ecosystems for purposes of orchestrating the user experiences of patients and clinicians. To date, the gap between knowledge representation and user-facing EHR integration has been considered an “implementation concern” requiring unscalable manual human efforts and governance coordination. Drafting a questionnaire engineered to meet the specifications of the HL7 CDS Knowledge Artifact specification, for example, carries no reasonable expectation that it may be imported and deployed into a live system without significant burdens. Dramatic reduction of the time and effort gap in the research and application cycle could be revolutionary. Doing so, however, requires both a floor-to-ceiling precoordination of functional boundaries in the knowledge management lifecycle, as well as formalization of the human processes by which this occurs. This research introduces ARTAKA: Architecture for Real-Time Application of Knowledge Artifacts, as a concrete floor-to-ceiling technological blueprint for both provider heath IT (HIT) and vendor organizations to incrementally introduce value into existing systems dynamically. This is made possible by service-ization of curated knowledge artifacts, then injected into a highly scalable backend infrastructure by automated orchestration through public marketplaces. Supplementary examples of client app integration are also provided. Compilation of knowledge into platform-specific form has been left flexible, in so far as implementations comply with ARTAKA’s Context Event Service (CES) communication and Health Services Platform (HSP) Marketplace service packaging standards. Towards the goal of interoperable human processes, ARTAKA’s treatment of knowledge artifacts as a specialized form of software allows knowledge engineers to operate as a type of software engineering practice. Thus, nearly a century of software development processes, tools, policies, and lessons offer immediate benefit: in some cases, with remarkable parity. Analyses of experimentation is provided with guidelines in how choice aspects of software development life cycles (SDLCs) apply to knowledge artifact development in an ARTAKA environment. Portions of this culminating document have been further initiated with Standards Developing Organizations (SDOs) intended to ultimately produce normative standards, as have active relationships with other bodies.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Uma arquitectura segura e colaborativa para registos de saúde electrónicos com suporte a mobilidade

Doutoramento em InformáticaDurante as ultimas décadas, os registos de saúde eletrónicos (EHR) têm evoluído para se adaptar a novos requisitos. O cidadão tem-se envolvido cada vez mais na prestação dos cuidados médicos, sendo mais pró ativo e desejando potenciar a utilização do seu registo. A mobilidade do cidadão trouxe mais desafios, a existência de dados dispersos, heterogeneidade de sistemas e formatos e grande dificuldade de partilha e comunicação entre os prestadores de serviços. Para responder a estes requisitos, diversas soluções apareceram, maioritariamente baseadas em acordos entre instituições, regiões e países. Estas abordagens são usualmente assentes em cenários federativos muito complexos e fora do controlo do paciente. Abordagens mais recentes, como os registos pessoais de saúde (PHR), permitem o controlo do paciente, mas levantam duvidas da integridade clinica da informação aos profissionais clínicos. Neste cenário os dados saem de redes e sistemas controlados, aumentando o risco de segurança da informação. Assim sendo, são necessárias novas soluções que permitam uma colaboração confiável entre os diversos atores e sistemas. Esta tese apresenta uma solução que permite a colaboração aberta e segura entre todos os atores envolvidos nos cuidados de saúde. Baseia-se numa arquitetura orientada ao serviço, que lida com a informação clínica usando o conceito de envelope fechado. Foi modelada recorrendo aos princípios de funcionalidade e privilégios mínimos, com o propósito de fornecer proteção dos dados durante a transmissão, processamento e armazenamento. O controlo de acesso _e estabelecido por políticas definidas pelo paciente. Cartões de identificação eletrónicos, ou certificados similares são utilizados para a autenticação, permitindo uma inscrição automática. Todos os componentes requerem autenticação mútua e fazem uso de algoritmos de cifragem para garantir a privacidade dos dados. Apresenta-se também um modelo de ameaça para a arquitetura, por forma a analisar se as ameaças possíveis foram mitigadas ou se são necessários mais refinamentos. A solução proposta resolve o problema da mobilidade do paciente e a dispersão de dados, capacitando o cidadão a gerir e a colaborar na criação e manutenção da sua informação de saúde. A arquitetura permite uma colaboração aberta e segura, possibilitando que o paciente tenha registos mais ricos, atualizados e permitindo o surgimento de novas formas de criar e usar informação clínica ou complementar.Since their early adoption Electronic Health Records (EHR) have been evolving to cope with increasing requirements from institutions, professionals and, more recently, from patients. Citizens became more involved demanding successively more control over their records and an active role on their content. Mobility brought also new requirements, data become scattered over heterogeneous systems and formats, with increasing di culties on data sharing between distinct providers. To cope with these challenges several solutions appeared, mostly based on service level agreements between entities, regions and countries. They usually required de ning complex federated scenarios and left the patient outside the process. More recent approaches, such as personal health records (PHR), enable patient control although raises clinical integrity doubts to other actors, such as physicians. Also, information security risk increase as data travels outside controlled networks and systems. To overcome this, new solutions are needed to facilitate trustable collaboration between the diverse actors and systems. In this thesis we present a solution that enables a secure and open collaboration between all healthcare actors. It is based on a service-oriented architecture that deals with the clinical data using a closed envelope concept. The architecture was modeled with minimal functionality and privileges bearing in mind strong protection of data during transmission, processing and storing. The access control is made through patient policies and authentication uses electronic identi cation cards or similar certi cates, enabling auto-enrollment. All the components require mutual authentication and uses cyphering mechanisms to assure privacy. We also present a threat model to verify, through our solution, if possible threats were mitigated or if further re nement is needed. The proposed solution solves the problem of patient mobility and data dispersion, and empowers citizens to manage and collaborate in their personal healthcare information. It also permits open and secure collaboration, enabling the patient to have richer and up to date records that can foster new ways to generate and use clinical or complementary information

UML profile for MIF static models. Version 1.0

HL7 provides standards for interoperability that improve care delivery, optimize workflow, reduce ambiguity and enhance knowledge transfer among all of our stakeholders, including healthcare providers, government agencies, the vendor community, fellow SDOs and patients. In all of our processes we exhibit timeliness, scientific rigor and technical expertise without compromising transparency, accountability, practicality, or our willingness to put the needs of our stakeholders first. HL7 is holding a contest to encourage the development of HL7 tools. This document describes the specification of a UML Profile for MIF Static Models as a particular submission to the HL7 2012-2013 Tooling ChallengePreprin

An Interoperable Clinical Cardiology Electronic Health Record System - a standards based approach for Clinical Practice and Research with Data Reuse

Currently in hospitals, several information systems manage, very often autonomously, the patient’s personal, clinical and diagnostic data. This originates a clinical information management system consisting of a myriad of independent subsystems which, although efficient in their specific purpose, make the integration of the whole system very difficult and limit the use of clinical data, especially as regards the reuse of these data for research purposes. Mainly for these reasons, the management of the Genoese ASL3 decided to commission the University of Genoa to set up a medical record system that could be easily integrated with the rest of the information system already present, but which offered solid interoperability features, and which could support the research skills of hospital health workers. My PhD work aimed to develop an electronic health record system for a cardiology ward, obtaining a prototype which is functional and usable in a hospital ward. The choice of cardiology was due to the wide availability of the staff of the cardiology department to support me in the development and in the test phase. The resulting medical record system has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. In order to achieve interoperability the system is based on the Health Level Seven standards for exchanging information between medical information systems. These standards are widely deployed and allow for the exchange of information in several functional domains. Specific decision support sections for particular aspects of the clinical life were also included. The data collected by this system were the basis for examples of secondary use for the development of two models based on machine learning algorithms. The first model allows to predict mortality in patients with heart failure within 6 months from their admission, and the second is focused on the discrimination between heart failure versus chronic ischemic heart disease in the elderly population, which is the widest population section served by the cardiological ward