Papers by Sumithra Velupillai
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. |
Exploring Transformer Text Generation for Medical Dataset Augmentation (2020.lrec-1)
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| Challenge: | Natural Language Processing (NLP) is a powerful tool to unlock the vast troves of unstructured data in clinical text. |
| Approach: | They propose a method for augmenting unstructured patient information to allow NLP model development on downstream clinically relevant tasks. |
| Outcome: | The proposed method beats baselines on a downstream classification task and can be used for NLP model development. |
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. |