Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour

no code implementations • LREC 2020 • Xingyi Song, Johnny Downs, Sumithra Velupillai, Rachel Holden, Maxim Kikoler, Kalina Bontcheva, Rina Dutta, Angus Roberts

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.

Classification General Classification +1

Time Expressions in Mental Health Records for Symptom Onset Extraction

2 code implementations • WS 2018 • Natalia Viani, Lucia Yin, Joyce Kam, Ayunni Alawi, Andr{\'e} Bittar, Rina Dutta, Rashmi Patel, Robert Stewart, Sumithra Velupillai

Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these.

Temporal Information Extraction

Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health

no code implementations • WS 2018 • Julia Ive, George Gkotsis, Rina Dutta, Robert Stewart, Sumithra Velupillai

In this paper, we apply a hierarchical Recurrent neural network (RNN) architecture with an attention mechanism on social media data related to mental health.

Decision Making Feature Engineering +3

Don't Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records

1 code implementation • WS 2016 • George Gkotsis, Sumithra Velupillai, Anika Oellrich, Harry Dean, Maria Liakata, Rina Dutta

Negation Negation Detection

The language of mental health problems in social media

1 code implementation • WS 2016 • George Gkotsis, Anika Oellrich, Tim Hubbard, Richard Dobson, Maria Liakata, Sumithra Velupillai, Rina Dutta