RESUMO
O sucesso de um projeto de pesquisa em medicina depende do recrutamento de um número suficiente de participantes de pesquisa. Um dos desafios para atingir a quantidade adequada é como utilizar dados de prontuário eletrônico para acelerar a avaliação de pacientes. Atualmente, isto é feito por revisão manual dos prontuários, um processo demorado e propenso a erros. Neste trabalho, especificamos e implementamos Ontocloud, um sistema de integração de dados baseado reescrita de consultas e em ontologias, com capacidade de inferência, para seleção de pacientes que atendam a critérios clínicos utilizando dados de prontuário eletrônico. Aplicamos este sistema a um estudo clínico real, conduzido no A.C. Camargo Cancer Center, e verificamos que atendeu a todos os critérios especificados e resolveu adequadamente o problema de seleção de participantes de pesquisa. Ainda, mostramos que sua performance é compatível com sistemas de integração similares...
A successful medical research project is entirely dependent on enough subjects being recruited. Among the challenges to achieve recruitment target, using avaliable data from electronic medical records to speed up the patient identification process. In this thesis, we specified and implemented Ontocloud, a query-rewriting, inference capable, ontology based data integration system, for selection of patients meeting clinical criteria using electronic medical records data. We applied it to a real clinical trial conducted at the AC Camargo Cancer Center and verified that it fulfilled all specified requirements, effectively solving the research subject selection problem. Also, we showed that Ontocloud performance is compatible with similar data integration systems...
Assuntos
Humanos , Integração de Sistemas , Inteligência Artificial , Seleção de Pacientes , Sistemas Computadorizados de Registros MédicosRESUMO
PURPOSE: This study was designed to identify genes that could predict response to doxorubicin-based primary chemotherapy in breast cancer patients. EXPERIMENTAL DESIGN: Biopsy samples were obtained before primary treatment with doxorubicin and cyclophosphamide. RNA was extracted and amplified and gene expression was analyzed using cDNA microarrays. RESULTS: Response to chemotherapy was evaluated in 51 patients, and based on Response Evaluation Criteria in Solid Tumors guidelines, 42 patients, who presented at least a partial response (> or =30% reduction in tumor dimension), were classified as responsive. Gene profile of samples, divided into training set (n = 38) and independent validation set (n = 13), were at first analyzed against a cDNA microarray platform containing 692 genes. Unsupervised clustering could not separate responders from nonresponders. A classifier was identified comprising EMILIN1, FAM14B, and PBEF, which however could not correctly classify samples included in the validation set. Our next step was to analyze gene profile in a more comprehensive cDNA microarray platform, containing 4,608 open reading frame expressed sequence tags. Seven samples of the initial training set (all responder patients) could not be analyzed. Unsupervised clustering could correctly group all the resistant samples as well as at least 85% of the sensitive samples. Additionally, a classifier, including PRSS11, MTSS1, and CLPTM1, could correctly distinguish 95.4% of the 44 samples analyzed, with only two misclassifications, one sensitive sample and one resistant tumor. The robustness of this classifier is 2.5 greater than the first one. CONCLUSION: A trio of genes might potentially distinguish doxorubicin-responsive from nonresponsive tumors, but further validation by a larger number of samples is still needed.
