Dr. Graeme Hirst, University of Toronto
Classifying verbal autopsy records by cause of death using neural networks and temporal reasoning


It is important for public health planning and resource allocation for authorities to have statistics on the varying causes of death in each region. But in developing countries, where people are more likely to die at home than in a hospital and where there are insufficient resources for physical autopsies, the cause of a person's death is frequently never formally established by a physician. To mitigate this problem, the family of the deceased may be interviewed about the circumstances of death, resulting in a so called "verbal autopsy" which will be subsequently coded by physicians with their judgement as to the cause of death. We aim to develop automated text-analysis methods assist in this.

Current automated methods primarily use structured data from the verbal autopsies to assign a cause-of-death category, but the results have been poor. We present a neural-net-based classification method based on textual features to automatically classify 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.

Time: Wednesday, 3rd of July 2019, 6:30 p.m. sharp

Location: Oesterreichisches Forschungsinstitut für Artificial Intelligence (OFAI), Freyung 6, Stiege 6, 1010 Wien