For researchers bridging the gap between machine learning and clinical practice, predictive models drawn from a variety of data streams remain an area of intense interest. In the specialty of psychiatry, the quality of such results is at times limited by type errors, with depression being a particularly egregious example of an overloaded concept. Here, we attempt to disambiguate the notion of depression by exploring its nuances spanning various data types, including diagnosis, mood episode, and symptom axis. A proposed type system resolution is provided for fortification of type safety, in the interest of improved interpretability of predictions in the context of healthcare.