Papers by Angus Roberts
Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour (2020.lrec-1)
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Xingyi Song, Johnny Downs, Sumithra Velupillai, Rachel Holden, Maxim Kikoler, Kalina Bontcheva, Rina Dutta, Angus Roberts
| Challenge: | Mental health problems are a major risk factor for suicide attempts. |
| Approach: | They propose to integrate information from sentences to left and right of the target sentence into the model to improve classification accuracy. |
| Outcome: | The proposed model was able to classify suicidal behaviour in autism spectrum disorder patient records significantly better than previous approaches. |
A Deep Neural Network Sentence Level Classification Method with Context Information (D18-1)
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| Challenge: | Existing methods that use context for sentence classification are difficult to scale . Usually, sentences are treated as separate instances for the task . however, in many situations the sentence that is the focus of classification appears in a context that can provide additional information. |
| Approach: | They propose a method that uses potentially large contexts to classify sentences . they use an LSTM, and short-span features to classize sentences based on a stacked CNN . |
| Outcome: | The proposed method consistently improves on two different datasets. |
Development of a Corpus Annotated with Medications and their Attributes in Psychiatric Health Records (2020.lrec-1)
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Jaya Chaturvedi, Natalia Viani, Jyoti Sanyal, Chloe Tytherleigh, Idil Hasan, Kate Baird, Sumithra Velupillai, Robert Stewart, Angus Roberts
| Challenge: | Free text fields within electronic health records (EHRs) contain valuable clinical information which is often missed when conducting research using EHR databases. |
| Approach: | They propose to extract medication annotations from mental health records by including contextual information around them. |
| Outcome: | The aim of the study is to provide a more complete picture behind the mention of medications in the health records, by including additional contextual information around them. |