Benchmarking the BRATECA Clinical Data Collection for Prediction Tasks

Abstract

Expanding the usability of location-specific clinical datasets is an important step toward expanding research into national medical issues, rather than only attempting to generalize hypotheses from foreign data. This means that benchmarking such datasets, thus proving their usefulness for certain kinds of research, is a worth- while task. This paper presents the first results of widely used prediction tasks from data contained within the BRATECA collection, a Brazilian tertiary care data collection, and also results for neural network architec- tures using these newly created test sets. The architectures use both structured and unstructured data to achieve their results. The obtained results are expected to serve as benchmarks for future tests with more advanced models based on the data available in BRATECA