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SPEAKERS/JULIANA BARCELLOS MATTOS
Juliana Barcellos Mattos

Juliana Barcellos Mattos

Centro de Informática (CIn) - Universidade Federal de Pernambuco

Juliana's lectures

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Juliana Barcellos Mattos · BRACIS 2020

Exceptional Survival Model Mining

The development of treatments based on the patient’s individual characteristics has been an emergent medical approach. The objective is to improve individual responses and overall survival. Thus, there is a need for computational tools able to identify and describe subgroups of patients for which the survival response significantly differs from the overall behaviour. However, there are few algorithms that address this matter. The majority of works of literature aim at building predictive models rather than understanding the characteristics that delineate subgroups with unusual survival. The approaches that provide understanding on factors that interfere in the survival behaviour usually resort to the stratification of the data based on previously known variable’s interactions, lacking the ability to shed light into new, possibly unknown, interactions. In contrast to the existent predictive approaches, we propose the use of supervised descriptive pattern mining in order to discover local patterns able to describe subsets of patients that present unusual survival behaviour. In this presentation, we present the ESM-AM (Exceptional Survival Model Ant Miner) algorithm, an Exceptional Model Mining approach to the discovery of subgroups with exceptional survival functions that explores the use of ant-colony optimization as search heuristic for the pattern mining task.

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Juliana Barcellos Mattos · BRACIS 2020

Clinical risk factors of ICU & fatal COVID-19 cases in Brazil

The Coronavirus disease 2019 (COVID-19) was first detected in China in December 2019. In a few months, the disease got pandemic proportions, overloading health systems all around the world. Risk factors related to the progression and outcome of the disease are still unclear. Moreover, clinical aspects of patients can differ between societies, and other demographic elements may impact survival responses. A better characterisation of local manifestation of COVID-19 is crucial to a better general understanding of the disease, and thus to improve treatment decisions and health systems’ management. In this article, we performed an initial analysis of clinical factors related to admission in ICU or death of SARS-CoV-2 confirmed Brazilian patients, based on 1,138,690 medical records from the Brazilian government. To our knowledge, this study is the first to assess clinical risk factors for disease progression in Brazil. We provide a concise data set of medical registers related to COVID-19 in the whole Brazilian territory, and we describe the baseline comorbidities and symptoms observed in the data collection. Then, we assess the correlation between the manifestation of symptoms/comorbidities and the patients’ survival response through Kaplan-Meier survival estimates. The results here reported are mostly in accordance with findings reported in previous works.