AutoBioLearn: An Automated Data Science Framework for eXplainable Analyses (XAI) of Clinical Datasets - Moreira, Lucas P. B.; Kuniyoshi, Maria L. G.; Wicik, Zofia; Martins-Jr, David C.; Brentani, Helena; Simões, Sérgio N. (Anais do Simpósio Brasileiro de Bioinformática (BSB), 2024)
With the increasing volume of biological and medical data, the application of efficient data science techniques has become essential for analysis. However, healthcare data scientists often need to integrate and analyze multiple datasets simultaneously. Although these analyses share similarities, they require adjustments to various parameters, delaying development and further hindering knowledge discovery. In this paper, we propose a framework that encapsulates all stages of typical data science analyses, from data pre-processing, execution, and evaluation to the interpretation of models. In addition, the framework includes XAI analyses. In tests involving a clinical dataset, the framework achieved a reduction of 92% in lines of code.