  This paper offers a systematic method for creating medical knowledge-grounded
patient records for use in activities involving differential diagnosis.
Additionally, an assessment of machine learning models that can differentiate
between various conditions based on given symptoms is also provided. We use a
public disease-symptom data source called SymCat in combination with Synthea to
construct the patients records. In order to increase the expressive nature of
the synthetic data, we use a medically-standardized symptom modeling method
called NLICE to augment the synthetic data with additional contextual
information for each condition. In addition, Naive Bayes and Random Forest
models are evaluated and compared on the synthetic data. The paper shows how to
successfully construct SymCat-based and NLICE-based datasets. We also show
results for the effectiveness of using the datasets to train predictive disease
models. The SymCat-based dataset is able to train a Naive Bayes and Random
Forest model yielding a 58.8% and 57.1% Top-1 accuracy score, respectively. In
contrast, the NLICE-based dataset improves the results, with a Top-1 accuracy
of 82.0% and Top-5 accuracy values of more than 90% for both models. Our
proposed data generation approach solves a major barrier to the application of
artificial intelligence methods in the healthcare domain. Our novel NLICE
symptom modeling approach addresses the incomplete and insufficient information
problem in the current binary symptom representation approach. The NLICE code
is open sourced at https://github.com/guozhuoran918/NLICE.
