Clinical decision support for screening, diagnosis and assessment of respiratory diseases: chronic obstructive pulmonary disease as a use case
In this thesis we propose a framework for designing, developing, a clinical decision support systems (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), and then demonstrate how these services can be integrated into the work-flo...
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| Tipo de documento: | tese |
| Estado: | Versão publicada |
| Data de publicação: | 2016 |
| País: | España |
| Recursos: | CBUC, CESCA |
| Repositório: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/457000 |
| Acesso em linha: | http://hdl.handle.net/10803/457000 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Sistemes d'ajuda a la decisió Sistemas de soporte a la decisión Decision support systems Malalties respiratòries Enfermedades respiratorias Respiratory diseases Funció pulmonar Función pulmonar Lung function Espirometria Espirometría Spirometry Aprenentatge supervisat Aprendizaje supervisado Supervised learning Garantia de qualitat Garantía de calidad Quality assurance 616.2 62 |
| Resumo: | In this thesis we propose a framework for designing, developing, a clinical decision support systems (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), and then demonstrate how these services can be integrated into the work-flow of healthcare providers. Furthermore, we focus on supporting spirometry, one of the main diagnostic tools in respiratory disease assessment. We present two methods to offer decision support in assuring the quality of a spirometry test that can be easily embedded into the CDSS framework. The first method is a novel algorithm that relies on a set of rules operating on 23 new parameters to define a high quality test. The second is a machine-learning approach, where we optimise the distinction between a good quality spirometry test and a poor one using a set of supervised-learning classifiers and hyper-parameters |
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