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Automatic generation of diagnostics using a markov model trained over real diagnostics.
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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.


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.

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