pediatric

Analysis of unstructured text-based data using machine learning techniques: the case of pediatric emergency department records in Nicaragua

Five-hundred Random Forests were trained on a set of bootstrap samples of the whole dataset (1789 ED visits) to perform the classification task. MLTs seemed to be a promising opportunity for the exploitation of unstructured information reported in ED records in low- and middle-income Spanish-speaking countries.