Search Results for author: Sumithra Velupillai

Found 22 papers, 8 papers with code

Sample Size in Natural Language Processing within Healthcare Research

no code implementations5 Sep 2023 Jaya Chaturvedi, Diana Shamsutdinova, Felix Zimmer, Sumithra Velupillai, Daniel Stahl, Robert Stewart, Angus Roberts

The simulations conducted within this study provide guidelines that can be used as recommendations for selecting appropriate sample sizes and class proportions, and for predicting expected performance, when building classifiers for textual healthcare data.

text-classification Text Classification

Development of a Knowledge Graph Embeddings Model for Pain

1 code implementation17 Aug 2023 Jaya Chaturvedi, Tao Wang, Sumithra Velupillai, Robert Stewart, Angus Roberts

This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task.

Knowledge Graph Embedding Knowledge Graph Embeddings +2

Development of a Corpus Annotated with Medications and their Attributes in Psychiatric Health Records

no code implementations LREC 2020 Jaya Chaturvedi, Natalia Viani, Jyoti Sanyal, Chloe Tytherleigh, Idil Hasan, Kate Baird, Sumithra Velupillai, Robert Stewart, Angus Roberts

The purpose of this analysis was to understand the complexity of medication mentions and their associated temporal information in the free text of EHRs, with a specific focus on the mental health domain.

Exploring Transformer Text Generation for Medical Dataset Augmentation

2 code implementations LREC 2020 Ali Amin-Nejad, Julia Ive, Sumithra Velupillai

Natural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research.

Synthetic Data Generation Text Generation

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

Annotating Temporal Information in Clinical Notes for Timeline Reconstruction: Towards the Definition of Calendar Expressions

1 code implementation WS 2019 Natalia Viani, Hegler Tissot, Ariane Bernardino, Sumithra Velupillai

To automatically analyse complex trajectory information enclosed in clinical text (e. g. timing of symptoms, duration of treatment), it is important to understand the related temporal aspects, anchoring each event on an absolute point in time.

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

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