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Description
Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks such as pharmacovigilance via the use of Natural Language Processing (NLP) techniques. One of the critical steps in information extraction pipelines is Named Entity Recognition (NER), where the mentions of entities such as diseases are located in text and their entity type are identified. However, the language in social media is highly informal, and user-expressed health-related concepts are often non-technical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and advanced machine learning-based NLP techniques have been underutilized. This work explores the effectiveness of different machine learning techniques, and particularly deep learning, to address the challenges associated with extraction of health-related concepts from social media. Deep learning has recently attracted a lot of attention in machine learning research and has shown remarkable success in several applications particularly imaging and speech recognition. However, thus far, deep learning techniques are relatively unexplored for biomedical text mining and, in particular, this is the first attempt in applying deep learning for health information extraction from social media.
This work presents ADRMine that uses a Conditional Random Field (CRF) sequence tagger for extraction of complex health-related concepts. It utilizes a large volume of unlabeled user posts for automatic learning of embedding cluster features, a novel application of deep learning in modeling the similarity between the tokens. ADRMine significantly improved the medical NER performance compared to the baseline systems.
This work also presents DeepHealthMiner, a deep learning pipeline for health-related concept extraction. Most of the machine learning methods require sophisticated task-specific manual feature design which is a challenging step in processing the informal and noisy content of social media. DeepHealthMiner automatically learns classification features using neural networks and utilizing a large volume of unlabeled user posts. Using a relatively small labeled training set, DeepHealthMiner could accurately identify most of the concepts, including the consumer expressions that were not observed in the training data or in the standard medical lexicons outperforming the state-of-the-art baseline techniques.
This work presents ADRMine that uses a Conditional Random Field (CRF) sequence tagger for extraction of complex health-related concepts. It utilizes a large volume of unlabeled user posts for automatic learning of embedding cluster features, a novel application of deep learning in modeling the similarity between the tokens. ADRMine significantly improved the medical NER performance compared to the baseline systems.
This work also presents DeepHealthMiner, a deep learning pipeline for health-related concept extraction. Most of the machine learning methods require sophisticated task-specific manual feature design which is a challenging step in processing the informal and noisy content of social media. DeepHealthMiner automatically learns classification features using neural networks and utilizing a large volume of unlabeled user posts. Using a relatively small labeled training set, DeepHealthMiner could accurately identify most of the concepts, including the consumer expressions that were not observed in the training data or in the standard medical lexicons outperforming the state-of-the-art baseline techniques.
ContributorsNikfarjam, Azadeh (Author) / Gonzalez, Graciela (Thesis advisor) / Greenes, Robert (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2016
ContributorsMarine Biological Laboratory Archives (Publisher) / Arizona Board of Regents (Publisher)
Created1925-19-39 (uncertain)
ContributorsMarine Biological Laboratory Archives (Publisher) / Arizona Board of Regents (Publisher)
Created1934
ContributorsMarine Biological Laboratory Archives (Publisher) / Arizona Board of Regents (Publisher)
Created1926
ContributorsMarine Biological Laboratory Archives (Publisher) / Arizona Board of Regents (Publisher)
Created1926
ContributorsMarine Biological Laboratory Archives (Publisher) / Arizona Board of Regents (Publisher)
Created1926
ContributorsMarine Biological Laboratory Archives (Publisher) / Arizona Board of Regents (Publisher)
Created1928
ContributorsWallace, Charles (Contributor) / Walls, Sarah (Creator) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2022-01-12
Description
Human Papillomavirus, or HPV, is a viral pathogen that most commonly spreads through sexual contact. HPV strains 6 and 11 normally cause genital warts, while HPV strains 16 and 18 commonly cause cervical cancer, which causes cancerous cells to spread in the cervix. Physicians can detect those HPV strains, using a Pap smear, which is a diagnostic test that collects cells from the female cervix.
ContributorsSantora, Emily (Contributor) / Walls, Sarah (Creator) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2021-04-06
Description
Johann Gregor Mendel studied patterns of trait inheritance in plants during the nineteenth century. Mendel, an Augustinian monk, conducted experiments on pea plants at St. Thomas’ Abbey in what is now Brno, Czech Republic. Twentieth century scientists used Mendel’s recorded observations to create theories about genetics.
ContributorsWallace, Charles (Contributor) / Walls, Sarah (Creator) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2022-01-13