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Exploratory Data Analysis in Electronic Health Records Graphs: Intuitive Features and Visualization Tools

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Cazzolato, Mirela T. ; Gutierrez, Marco Antonio ; Traina, Cactano, Jr. ; Faloutsos, Christos ; Traina, Agma J. M. ; Almeida, JR ; Spiliopoulou, M ; Andrades, JAB ; Placidi, G ; Gonzalez, AR ; Sicilia, R ; Kane, B
Número total de Autores: 12
Tipo de documento: Artigo Científico
Fonte: 2023 IEEE 36TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS; v. N/A, p. 6-pg., 2023-01-01.
Resumo

Given a large, unlabeled set of Electronic Health Records (EHRs) acquired from multiple hospitals, how can we analyze the available entities and identify relationships in the data? Also, how can we perform Exploratory Data Analysis (EDA) over such EHR data? Many medical institutions generate EHRs as tabular data with entities and attributes in common. However, due to a large number of records, attributes, and high cardinality, exploring the different datasets and finding patterns and insights become laborious and prone to errors. In this work, we propose GraF-EDA for EDA over EHR data from different institutions. GraF-EDA models EHRs as time-evolving graphs, allowing the interoperability of such data into a single representation. We extract meaningful features from the graph nodes and provide intuitive visualizations to improve data explainability. We evaluate GraF-EDA with four COVID-19 datasets from hospitals of the Sao Paulo state, Brazil, resulting in million-scale graphs. Our method identified correlations, similarities and dissimilarities among medical treatments, exams, clinics, and outcomes. With the visual tools provided by GraF-EDA, we were able to spot cases of interest and check more details about them. Our results indicate that GraF-EDA is a fast, effective, open-sourced tool for EDA of EHRs from multiple institutions. (AU)

Processo FAPESP: 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD)
Beneficiário:Agma Juci Machado Traina
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 21/11403-5 - Mineração de registros multimodais: descoberta explicável de padrões e anomalias
Beneficiário:Mirela Teixeira Cazzolato
Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado
Processo FAPESP: 20/11258-2 - Consultas por similaridade e interoperabilidade em bases de dados médicos
Beneficiário:Mirela Teixeira Cazzolato
Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado