  Despite the widely reported success of embedding-based machine learning
methods on natural language processing tasks, the use of more easily
interpreted engineered features remains common in fields such as cognitive
impairment (CI) detection. Manually engineering features from noisy text is
time and resource consuming, and can potentially result in features that do not
enhance model performance. To combat this, we describe a new approach to
feature engineering that leverages sequential machine learning models and
domain knowledge to predict which features help enhance performance. We provide
a concrete example of this method on a standard data set of CI speech and
demonstrate that CI classification accuracy improves by 2.3% over a strong
baseline when using features produced by this method. This demonstration
provides an ex-ample of how this method can be used to assist classification in
fields where interpretability is important, such as health care.
