  Electronic health records contain rich textual data which possess critical
predictive information for machine-learning based diagnostic aids. However many
traditional machine learning methods fail to simultaneously integrate both
vector space data and text. We present a supervised method using Laplacian
eigenmaps to augment existing machine-learning methods with low-dimensional
representations of textual predictors which preserve the local similarities.
The proposed implementation performs alternating optimization using gradient
descent. For the evaluation we applied our method to over 2,000 patient records
from a large single-center pediatric cardiology practice to predict if patients
were diagnosed with cardiac disease. Our method was compared with latent
semantic indexing, latent Dirichlet allocation, and local Fisher discriminant
analysis. The results were assessed using AUC, MCC, specificity, and
sensitivity. Results indicate supervised Laplacian eigenmaps was the highest
performing method in our study, achieving 0.782 and 0.374 for AUC and MCC
respectively. SLE showed an increase in 8.16% in AUC and 20.6% in MCC over the
baseline which excluded textual data and a 2.69% and 5.35% increase in AUC and
MCC respectively over unsupervised Laplacian eigenmaps. This method allows many
existing machine learning predictors to effectively and efficiently utilize the
potential of textual predictors.
