  Many analysis and prediction tasks require the extraction of structured data
from unstructured texts. However, an annotation scheme and a training dataset
have not been available for training machine learning models to mine structured
data from text without special templates and patterns. To solve it, this paper
presents an end-to-end machine learning pipeline, Text2Struct, including a text
annotation scheme, training data processing, and machine learning
implementation. We formulated the mining problem as the extraction of metrics
and units associated with numerals in the text. Text2Struct was trained and
evaluated using an annotated text dataset collected from abstracts of medical
publications regarding thrombectomy. In terms of prediction performance, a dice
coefficient of 0.82 was achieved on the test dataset. By random sampling, most
predicted relations between numerals and entities were well matched to the
ground-truth annotations. These results show that Text2Struct is viable for the
mining of structured data from text without special templates or patterns. It
is anticipated to further improve the pipeline by expanding the dataset and
investigating other machine learning models. A code demonstration can be found
at: https://github.com/zcc861007/CourseProject
