Search Results for author:
Found 16 papers, 4 papers with code
Question answering systems for health professionals at the point of care -- a systematic review
Results: We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods.
Sample Size in Natural Language Processing within Healthcare Research
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.
Development of a Knowledge Graph Embeddings Model for Pain
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.
Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach
Mental health electronic health records are a good data source to study this overlap.
Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis.
Comparative Analysis of Text Classification Approaches in Electronic Health Records
Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research.
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.
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.
Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records.
MedCAT -- Medical Concept Annotation Tool
To uncover the potential of biomedical documents we need to extract and structure the information they contain.
Bio-YODIE: A Named Entity Linking System for Biomedical Text
Ever-expanding volumes of biomedical text require automated semantic annotation techniques to curate and put to best use.
A Deep Neural Network Sentence Level Classification Method with Context Information
In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification.
