Skip to content
Automatic generation of diagnostics using a markov model trained over real diagnostics.
Python Jupyter Notebook
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
models
notebooks
src
.gitignore
README.md
requirements.txt

README.md

Binder

Diagnostic Generator

This model is trained over around 2 millions diagnostics to extract the probability distributions of word bigrams, which are groups of 2 words that appear one after the other. There are more frequent bigrams than others, for example the bigram caries dentinaria is more frequent than caries gingival and using these conditional probabilities we can synthesize new diagnostics that appears to be written by humans. The method used to model this process is a First Order Hidden Markov Model.

TODO

Create a Second Order Hidden Markov Model extracting the probability distributions of trigrams to synthesize more realistic diagnostics.

Test the Model by Yourself

Simply launch the binder container and start playing with the model or clone this repository.

Train Your Own Model

All the scripts used to train the model are published in the src folder.

You can’t perform that action at this time.