README

README.md

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+# Pre-reading radiology reporst
+
+
+## Data
+All data were derived from MIMIC and require signing a data use agreement with Physionet.  None are provided here.
+
+## Services
+
+This repository exposes four components that are useful in a data science proof of concept.
+- A container running Jupyter notebooks with common machine learning libraries (available @ localhost:8888).  Any notebooks will persist in a mounted volume (./volumes/notebooks)
+- A container running Streamlit allows a user to access the predictions from the model based on user inputs (available at localhost:8501)
+
+## Usage
+
+turn on the application 
+```
+docker-compose up 
+```
+
+download the data from physionet, passing any argument downloads the data (no arguments does nothing)
+```
+docker-compose run physionet True 
+```
+
+run the etl migrations
+```
+docker-compose run etl 
+```
+
+train the model
+```
+docker-compose run train
+```
+
+## Structure
+
+```
+|-- containers - code
+|   |-- etl         # transforms raw data from physionet into jsonlines files
+|   |-- jupyter     # interactive notebooks
+|   |-- physionet   # download the MIMIC-CXR and MIMIC-CXR-JPG data from physionet
+|   |-- prerad      # a small streamlit application to demo the model functionality 
+|-- volumes         # persistent data
+|   |-- notebooks   # jupyter notebooks persisted here
+|   |-- physionet   # physionet data is persisted here
+```
+
+## Streamlit User Interface
+
+![](./resources/streamlit.png)  
+
+A small web application can take a clinical indication and a chest x-ray, returns a reasonably templated radiology report.