Background Despite significant advancements in biomedical named entity recognition methods, the clinical application of these systems continues to face many challenges: (1) most of the methods are trained on a limited set of clinical entities; (2) these methods are heavily reliant on a large amount of data for both pre-training and prediction, making their use in production impractical; (3) they do not consider non-clinical entities, which are also related to patient’s health, such as social, economic or demographic factors. Methods In this paper, we develop Bio-Epidemiology-NER (https://pypi.org/project/Bio-Epidemiology-NER/) an open-source Python package for detecting biomedical named entities from the text. This approach is based on a Transformer-based system and trained on a dataset that is annotated with many named entities (medical, clinical, biomedical, and epidemiological). This approach improves on previous efforts in three ways: (1) it recognizes many clinical entity types, such as medical risk factors, vital signs, drugs, and biological functions; (2) it is easily configurable, reusable, and can scale up for training and inference; (3) it also considers non-clinical factors (age and gender, race and social history and so) that influence health outcomes. At a high level, it consists of the phases: pre-processing, data parsing, named entity recognition, and named entity enhancement. Results Experimental results show that our pipeline outperforms other methods on three benchmark datasets with macro-and micro average F1 scores around 90 percent and above. Conclusion This package is made publicly available for researchers, doctors, clinicians, and anyone to extract biomedical named entities from unstructured biomedical texts.
The practice of retrieving and recommending Learning Objects (LOs) to the learners according to their specific needs and requirements has been a very active research area in e-learning. This paper proposes the design and development of a context-aware methodology that comprises a Learning Object Repository (LOR), context-aware recommendation engine and a user-friendly interface. The existing approaches in this regard focus on learners' ratings, history, behavior and interests, rather ignored the knowledge gain and learning outcomes by the learners. The paper contributes in the research in threefold manner. First, a comparative survey of existing research in this area is presented. Secondly, the design and development of context-aware methodology for recommending LOs to the learners is proposed. Third contribution of the research is a mapping algorithm. Finally, it provides directions for the future research in this area.
Background: Despite significant advancements in biomedical named entity recognition methods, the clinical application of these systems continues to face many challenges: (1) most of the methods are trained on a limited set of clinical entities; (2) these methods are heavily reliant on a large amount of data for both pretraining and prediction, making their use in production impractical; (3) they do not consider non-clinical entities, which are also related to patient's health, such as social, economic or demographic factors. Methods: In this paper, we develop Bio-Epidemiology-NER (https://pypi.org/project/Bio-Epidemiology-NER/) an open-source Python package for detecting biomedical named entities from the text. This approach is based on Transformer-based approach and trained on a dataset that is annotated with many named entities (medical, clinical, biomedical and epidemiological). This approach improves on previous efforts in three ways: (1) it recognizes many clinical entity types, such as medical risk factors, vital signs, drugs, and biological functions; (2) it is easily configurable, reusable and can scale up for training and inference; (3) it also considers non-clinical factors (age and gender, race and social history and so) that influence health outcomes. At a high level, it consists of the phases: preprocessing, data parsing, named entity recognition and named entities enhancement. Results: Experimental results show that our pipeline outperforms other methods on three benchmark datasets with macro-and micro average F1 scores around 90 percent and above.
The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the infectious disease. In this paper, we propose a generalizable machine learning approach that improves on previous efforts by recognizing a large number of clinical risk factors and SDoH. The novelty of the proposed method lies in the subtle combination of a number of deep neural networks, including the BiLSTM-CNN-CRF method and a transformer-based embedding layer. Experimental results on a cohort of COVID-19 data prepared from PubMed articles show the superiority of the proposed approach. When compared to other methods, the proposed approach achieves a performance gain of about 1–5% in terms of macro- and micro-average F1 scores. Clinical practitioners and researchers can use this approach to obtain accurate information regarding clinical risks and SDoH factors, and use this pipeline as a tool to end the pandemic or to prepare for future pandemics.
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