2 research outputs found
Arquiteturas federadas para integração de dados biomédicos
Doutoramento Ciências da ComputaçãoThe last decades have been characterized by a continuous adoption of
IT solutions in the healthcare sector, which resulted in the proliferation
of tremendous amounts of data over heterogeneous systems. Distinct
data types are currently generated, manipulated, and stored, in the
several institutions where patients are treated. The data sharing and an
integrated access to this information will allow extracting relevant
knowledge that can lead to better diagnostics and treatments.
This thesis proposes new integration models for gathering information
and extracting knowledge from multiple and heterogeneous biomedical
sources.
The scenario complexity led us to split the integration problem according
to the data type and to the usage specificity. The first contribution is a
cloud-based architecture for exchanging medical imaging services. It
offers a simplified registration mechanism for providers and services,
promotes remote data access, and facilitates the integration of
distributed data sources. Moreover, it is compliant with international
standards, ensuring the platform interoperability with current medical
imaging devices. The second proposal is a sensor-based architecture
for integration of electronic health records. It follows a federated
integration model and aims to provide a scalable solution to search and
retrieve data from multiple information systems. The last contribution is
an open architecture for gathering patient-level data from disperse and
heterogeneous databases. All the proposed solutions were deployed
and validated in real world use cases.A adoção sucessiva das tecnologias de comunicação e de informação
na área da saúde tem permitido um aumento na diversidade e na
qualidade dos serviços prestados, mas, ao mesmo tempo, tem gerado
uma enorme quantidade de dados, cujo valor científico está ainda por
explorar. A partilha e o acesso integrado a esta informação poderá
permitir a identificação de novas descobertas que possam conduzir a
melhores diagnósticos e a melhores tratamentos clínicos.
Esta tese propõe novos modelos de integração e de exploração de
dados com vista à extração de conhecimento biomédico a partir de
múltiplas fontes de dados.
A primeira contribuição é uma arquitetura baseada em nuvem para
partilha de serviços de imagem médica. Esta solução oferece um
mecanismo de registo simplificado para fornecedores e serviços,
permitindo o acesso remoto e facilitando a integração de diferentes
fontes de dados. A segunda proposta é uma arquitetura baseada em
sensores para integração de registos electrónicos de pacientes. Esta
estratégia segue um modelo de integração federado e tem como
objetivo fornecer uma solução escalável que permita a pesquisa em
múltiplos sistemas de informação. Finalmente, o terceiro contributo é
um sistema aberto para disponibilizar dados de pacientes num contexto
europeu. Todas as soluções foram implementadas e validadas em
cenários reais
New insights in echocardiography based left-ventricle dynamics assessment
Cardiovascular diseases affect a high percentage of people worldwide, being currently a major clinical concern. Echocardiograms are useful exams that allow monitoring the heart dynamics. However, their analysis depends on trained physicians with well-developed skills to recognize pathology from morphological and dynamical cues. Furthermore, these exams are often difficult to interpret due to image quality. Therefore, automatic systems able to analyze echocardiographic quantitative parameters in order to convey useful information will provide a great help in clinical diagnosis. A robust dataset was built, comprising variables associated with left-ventricle dynamics, which were studied in order to build a classifier able to discriminate between pathological and non-pathological records. To accomplish this goal, a network classifier based on decision tree was developed, using as input the left ventricle velocity over a complete cardiac cycle. This classifier revealed both sensitivity and specificity over 90% in discriminating non-pathological records, or pathological records (dilated or hypertrophic).publishe