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This paper presents a deep learning-based framework to detect sarcasm in relation to time. Deep N-gram features generated using the FastText algorithm, combined with temporal handcrafted temporal features are used to train several machine learning classifiers. Experimental results show that Logistic Regression performs the best among all the classifiers. The introduction of the handcrafted temporal features has also whosn to improve overall detection performance when compared to existing works in the field.

BACKGROUND: Malnutrition is a serious health risk facing older people living in residential aged care facilities. Aged care staff record observations and concerns about older people in electronic health records (EHR), including free-text progress notes. These insights are yet to be unleashed. OBJECTIVE: This study explored the risk factors for malnutrition in structured and unstructured electronic health data. METHODS: Data of weight loss and malnutrition were extracted from the de-identified EHR records of a large aged care organization in Australia. A literature review was conducted to ident...

Recent advancements in natural language processing (NLP), particularly contextual word embedding models, have improved knowledge extraction from biomedical and healthcare texts. However, limited comprehensive research compares these models. This study conducts a scoping review and compares the performance of the major contextual word embedding models for biomedical knowledge extraction. From 26 articles identified from Scopus, PubMed, PubMed Central, and Google Scholar between 2017 and 2021, 18 notable contextual word embedding models were identified. These include ELMo, BERT, BioBERT, BlueBER...

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