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Found 19 papers, 5 papers with code
Relative and Incomplete Time Expression Anchoring for Clinical Text
Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories.
Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer’s Disease (AD) using Mental Health Records: a Classification Approach
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.
Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour
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.
Development of a Corpus Annotated with Medications and their Attributes in Psychiatric Health Records
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
Natural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research.
Annotating Temporal Information in Clinical Notes for Timeline Reconstruction: Towards the Definition of Calendar Expressions
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.
Is artificial data useful for biomedical Natural Language Processing algorithms?
A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility.
Time Expressions in Mental Health Records for Symptom Onset Extraction
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.
Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health
In this paper, we apply a hierarchical Recurrent neural network (RNN) architecture with an attention mechanism on social media data related to mental health.
