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- TECNOLOGIA E INOVAÇÃO AO SERVIÇO DO EXERCÍCIO E SAÚDE. Exercício. Pandemia COVID-19. Tecnologia Vs. Isolamento SocialPublication . Marlene, Rosa; Morouço, Pedro; Carreira, Igor Filipe Rodrigues; Nogueira, Ana; Oliveira, André; Mendes, Diogo; Ribeiro, Duarte; Homem, Francisca; Zambana, Joaquim; Pereira, Luis; Simões, Sara; Graur, Mariana; Bairrada, Cândida; Azul, Joana; Calado, Miguel; Brito, Sara; Ferreira, Fábio; Castro, Rebeca; Costa, Rodrigo; Góis, Silvane; Fonseca, Edgar; Ferreira, Leonardo; Baltazar, Nuno; Sousa, Raquel; Alves, João
- Text Mining Applied to Electronic Medical RecordsPublication . Pereira, Luis; Rijo, Rui Pedro Charters Lopes; Silva, Catarina; Martinho, RicardoThe analysis of medical records is a major challenge, considering they are generally presented in plain text, have a very specific technical vocabulary and are nearly always unstructured. It is an interdisciplinary work that requires knowledge from several fields. The analysis may have several goals, such as assistance on clinical decision, classification of medical procedures, and to support hospital management decisions. This work presents the concepts involved, the relevant existent related work, and the main open issues for future research within the analysis of electronic medical records, using data and text mining techniques. It provides a comprehensive contextualization to all those who wish to perform an analytical work of medical records, enabling the identification of fruitful research fields. With the digitalization of medical records and the large amount of medical data available, this is an area of wide research potential.
- Predictors of mortality and neurological dysfunction in cardiac arrest: A retrospective single centre studyPublication . Cabral, Margarida; Castro, Diana; Palavras, Maria João; Santos, Flávia; Sequeira, Filipa; Pereira, Luís; Morais, João; Pereira, LuisObjective: The aim of this study was to identify the mortality rate of cardiac arrest in our insti tution and to determine the association between clinical available variables with early mortality and neurological outcomes. Design, setting, and patients: We performed a ret rospective study including all adult patients with the first diagnosis of “cardiac arrest” admitted to the intensive care unit of a Portuguese tertiary hospital, from 2015 to 2020. Outcomes were early mortality, including in-hospital and 1 month after discharge mortality, and neurological function after cardiac arrest as defined by the Cerebral Performance Category score scale. Results: 114 patients were included, 32 suffered from out-of-hospital cardiac arrest, and 82 from in-hospital cardiac arrest. In multivariate logistic analysis, a Glasgow Coma Score after the return of spontaneous circulation less than five and the existence of another cause for cardiac ar rest than ST-segment elevation myocardial infarction demonstrated to be predictive factors of early mortality. The poor neurological outcome was associated with a total cardiopulmonary resuscitation length greater than five minutes and a Glasgow Coma Score after the return of spon taneous circulation less than five. Conclusions: Cardiac arrest is still an important cause of morbimortality in our society. Efforts should be made to optimize its approach, minimizing the cardiorespiratory arrest length to reduce mortality and improve the neurologic prognosis of survivors.
- Decision Support System to Diagnosis and Classification of Epilepsy in ChildrenPublication . Rijo, Rui; Silva, Catarina; Pereira, Luis; Gonçalves, Dulce; Agostinho, MargaridaClinical decision support systems play an important role in organizations. They have a tight relation with the information systems. Our goal is to develop a system to support the diagnosis and the classification of epilepsy in children. Around 50 million people in the world have epilepsy. Epilepsy diagnosis can be an extremely complex process, demanding considerable time and effort from physicians and healthcare infrastructures. Exams such as electroencephalograms and magnetic resonances are often used to create a more accurate diagnosis in a short amount of time. After the diagnosis process, physicians classify epilepsy according to the International Classification of Diseases, ninth revision (ICD-9). Physicians need to classify each specific type of epilepsy based on different data, e.g., types of seizures, events and exams' results. The classification process is time consuming and, in some cases, demands for complementary exams. This work presents a text mining approach to support medical decisions relating to epilepsy diagnosis and ICD-9-based classification in children. We put forward a text mining approach using electronically processed medical records, and apply the K-Nearest Neighbor technique as a white-box multiclass classifier approach to classify each instance, mapping it to the corresponding ICD-9-based standard code. Results on real medical records suggest that the proposed framework shows good performance and clear interpretations, albeit the reduced volume of available training data. To overcome this hurdle, in this work we also propose and explore ways of expanding the dataset.