no code implementations • EMNLP (ClinicalNLP) 2020 • Louise Dupuis, Nicol Bergou, Hegler Tissot, Sumithra Velupillai
Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories.
1 code implementation • EMNLP (ClinicalNLP) 2020 • Zixu Wang, Julia Ive, Sinead Moylett, Christoph Mueller, Rudolf Cardinal, Sumithra Velupillai, John O’Brien, Robert Stewart
To the best of our knowledge, this is the first attempt to distinguish DLB from AD using mental health records, and to improve the reliability of DLB predictions.
no code implementations • 5 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.
1 code implementation • 17 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.
1 code implementation • 3 Apr 2023 • Jaya Chaturvedi, Sumithra Velupillai, Robert Stewart, Angus Roberts
Mental health electronic health records are a good data source to study this overlap.
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.
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.
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.
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.
no code implementations • WS 2019 • Zixu Wang, Julia Ive, Sumithra Velupillai, Lucia Specia
A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility.
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.
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.