Public Biography
Our research is focused on developing artificial intelligence (AI) methods to analyze heterogeneous biomedical big data for translational applications. This ongoing work brings together two branches of AI: knowledge representation reasoning and machine learning algorithms to characterize brain network dynamics and electronic health records (EHR) data.
Knowledge representation and reasoning involves development of knowledge models or ontologies. We have led the development of new methods to use ontology engineering principles across multiple stages of machine learning workflows, including feature engineering and model validation. This involves the development of deep neural network (DNN) models and the use of classical machine learning algorithms such as support vector machines (SVM) for integrative analysis of multi-modal brain connectivity data in neurological disorders such as epilepsy and Parkinson's Disease. To address the challenges of data quality and scientific reproducibility, we have led the development of a provenance metadata framework called ProvCaRe using ontology engineering and natural language processing techniques.
Webpage: https://bmhinformatics.case.edu/
Knowledge representation and reasoning involves development of knowledge models or ontologies. We have led the development of new methods to use ontology engineering principles across multiple stages of machine learning workflows, including feature engineering and model validation. This involves the development of deep neural network (DNN) models and the use of classical machine learning algorithms such as support vector machines (SVM) for integrative analysis of multi-modal brain connectivity data in neurological disorders such as epilepsy and Parkinson's Disease. To address the challenges of data quality and scientific reproducibility, we have led the development of a provenance metadata framework called ProvCaRe using ontology engineering and natural language processing techniques.
Webpage: https://bmhinformatics.case.edu/
Affiliations
Fellows of AMIA (FAMIA)
FAMIA stands for “Fellow of the American Medical Informatics Association” and it recognizes the contributions and professional accomplishments of AMIA members who apply informatics skills and knowledge to their practice – be that in a clinical setting, a public or population health capacity, or as a clinical researcher.
Year Inducted
2021
Clinical Research Informatics
Working Group
Member-at-Large 2023-2024
The Clinical Research Informatics Working Group's mission is to advance the discipline of Clinical Research Informatics (CRI) by fostering interaction, discussion and collaboration among individuals and groups involved or interested in the practice and study of CRI, and to serve as the home for CRI professionals within AMIA.
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Natural Language Processing
Working Group
Vice Chair 2023-2024
The mission of the Natural Language Processing is to develop, apply, and promote natural language processing in biomedical science, patient care, public health and biomedical education.
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