The Extraction Module is divided into four components. The first three are pages dedicated to simple extraction processes on various data types (images, text notes, and time series) using pre-trained models and/or Python libraries. The fourth component is MEDimage, which is the implementation of a Python open-source package designed for medical image processing and radiomics features extraction.
In the video below, the "HAIM study" refers to the study of Soenksen et al.
Content
00:00 Overview
00:50 Extraction Images
04:24 Extraction Time Series
08:29 Extraction Text
10:48 Last Word
Data
Physionet : https://physionet.org/
MIMIC-IV Demo database : https://physionet.org/content/mimic-iv-demo/2.2/
MIMIC-IV-Note : https://physionet.org/content/mimic-iv-note/2.2/
MIMIC-IV-CXR : https://physionet.org/content/mimic-cxr-jpg/2.0.0/
Study of Soenksen et al. : https://www.nature.com/articles/s41746-022-00689-4
Extraction tools
TorchXRayVision : https://github.com/mlmed/torchxrayvision
TSfresh : https://tsfresh.readthedocs.io
BioBERT : https://arxiv.org/abs/1901.08746
BioBERT weights : https://github.com/EmilyAlsentzer/clinicalBERT
