  Information in electronic health records (EHR), such as clinical narratives,
examination reports, lab measurements, demographics, and other patient
encounter entries, can be transformed into appropriate data representations
that can be used for downstream clinical machine learning tasks using
representation learning. Learning better representations is critical to improve
the performance of downstream tasks. Due to the advances in machine learning,
we now can learn better and meaningful representations from EHR through
disentangling the underlying factors inside data and distilling large amounts
of information and knowledge from heterogeneous EHR sources. In this chapter,
we first introduce the background of learning representations and reasons why
we need good EHR representations in machine learning for medicine and
healthcare in Section 1. Next, we explain the commonly-used machine learning
and evaluation methods for representation learning using a deep learning
approach in Section 2. Following that, we review recent related studies of
learning patient state representation from EHR for clinical machine learning
tasks in Section 3. Finally, in Section 4 we discuss more techniques, studies,
and challenges for learning natural language representations when free texts,
such as clinical notes, examination reports, or biomedical literature are used.
We also discuss challenges and opportunities in these rapidly growing research
fields.
