  Acceleration of machine learning research in healthcare is challenged by lack
of large annotated and balanced datasets. Furthermore, dealing with measurement
inaccuracies and exploiting unsupervised data are considered to be central to
improving existing solutions. In particular, a primary objective in predictive
modeling is to generalize well to both unseen variations within the observed
classes, and unseen classes. In this work, we consider such a challenging
problem in machine learning driven diagnosis: detecting a gamut of
cardiovascular conditions (e.g. infarction, dysrhythmia etc.) from limited
channel ECG measurements. Though deep neural networks have achieved
unprecedented success in predictive modeling, they rely solely on
discriminative models that can generalize poorly to unseen classes. We argue
that unsupervised learning can be utilized to construct effective latent spaces
that facilitate better generalization. This work extensively compares the
generalization of our proposed approach against a state-of-the-art deep
learning solution. Our results show significant improvements in F1-scores.
