A verbal autopsy is a post-hoc written interview report of the symptoms preceding a person’s death in cases where no official cause of death was determined by a physician.
Current leading automated prediction methods primarily use structured data from verbal autopsies to assign a cause-of-death category.
We present a neural-net-based classification method based on textual features to automatically predict cause-of-death categories from free-text verbal autopsy narratives alone. 
Features used, in addition to lexical cues, include events, temporal sequences, and symptom words. 
We are presently porting the system from English to Hindi.