TY - JOUR
T1 - Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour
AU - Song, Xingyi
AU - Downs, Johnny
AU - Velupillai, Sumithra
AU - Holden, Rachel
AU - Kikoler, Maxim
AU - Bontcheva, Kalina
AU - Dutta, Rina
AU - Roberts, Angus
PY - 2020/5/15
Y1 - 2020/5/15
N2 - Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk. We apply a deep neural network based classification model with a lightweight context encoder, to classify sentence level suicidal behaviour in EHRs. We show that incorporating information from sentences to left and right of the target sentence significantly improves classification accuracy. Our approach achieved the best performance when classifying suicidal behaviour in Autism Spectrum Disorder patient records. The results could have implications for suicidality research and clinical surveillance.
AB - Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk. We apply a deep neural network based classification model with a lightweight context encoder, to classify sentence level suicidal behaviour in EHRs. We show that incorporating information from sentences to left and right of the target sentence significantly improves classification accuracy. Our approach achieved the best performance when classifying suicidal behaviour in Autism Spectrum Disorder patient records. The results could have implications for suicidality research and clinical surveillance.
UR - http://www.scopus.com/inward/record.url?scp=85096584439&partnerID=8YFLogxK
M3 - Article
VL - 1296–1303
SP - 1303
EP - 1310
JO - Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)
JF - Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)
ER -