Publications by authors named "Omid Ghiasvand"

Background: Clinical trial protocols are the foundation for advancing medical sciences, however, the extraction of accurate and meaningful information from the original clinical trials is very challenging due to the complex and unstructured texts of such documents. Named entity recognition (NER) is a fundamental and necessary step to process and standardize the unstructured text in clinical trials using Natural Language Processing (NLP) techniques.

Methods: In this study we fine-tuned pre-trained language models to support the NER task on clinical trial eligibility criteria.

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Extracting clinical concepts and their relations from clinical narratives is one of the fundamental tasks in clinical natural language processing. Traditional solutions often separate this task into two subtasks with a pipeline architecture, which first recognize the named entities and then classify the relations between any possible entity pairs. The pipeline architecture, although widely used, has two limitations: 1) it suffers from error propagation from the recognition step to the classification step, 2) it cannot utilize the interactions between the two steps.

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The laboratory rat, Rattus norvegicus, is an important model of human health and disease, and experimental findings in the rat have relevance to human physiology and disease. The Rat Genome Database (RGD, http://rgd.mcw.

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Rattus norvegicus, the laboratory rat, has been a crucial model for studies of the environmental and genetic factors associated with human diseases for over 150 years. It is the primary model organism for toxicology and pharmacology studies, and has features that make it the model of choice in many complex-disease studies. Since 1999, the Rat Genome Database (RGD; http://rgd.

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