Dr. Cui Tao’s background is in clinical informatics and computer science, and her research interests include ontologies, standard terminologies, semantic web, information extraction and integration, machine learning as well as applying these technologies to clinical and translational studies.
Dr. Tao is teaching HI6306 (Information and Knowledge Representation in Health Informatics) at SBMI. When asked where informatics is headed, Tao said, “I believe the future of biomedical informatics will focus on big data analysis with data standardization and normalization.”
Dr. Tao is a recipient of the Presidential Early Career Awards for Scientists and Engineers (PECASE), the highest honor bestowed by the United States Government on science and engineering professionals in the early stages of their independent research careers.
Education
- PhD, Computer Science, Brigham Young University
- MS, Computer Science, Brigham Young University
- BS, Biology and Computer Science, Beijing Normal University
Areas of Expertise
- Applying semantic and ontology technologies to clinical and biomedical applications
- Ontology generation and conceptual modeling
- Common terminology and ontology services
- Information extraction and integration
- Big data analysis using machine learning and statistical methods
- Temporal relation modeling, extraction and reasoning
- Secondary use of EHR data for clinical and translational studies
- Ontology-based analysis for cancer drug repurposing
- Vaccine and drug safety analysis
- Ontology-based mHealth and patient education
- Ontology-based personalized decision support systems
- Program Director / Principal Investigator
Dynamic learning for post-vaccine event prediction using temporal information in VAERS
(1R01AI130460, $3.12 M), 2017-2022 (Impact score 16, 1% percentile)
- Program Director / Principal Investigator
Metadata applications on informed content to facilitate biorepository data regulation and sharing
Funded by NIH/NHGRI (1U01HG009454, $1.4M), 2016-2019 (Impact score 26)
- Program Director / Principal Investigator
Patient Medical History Representation, Extraction, and Inference from EHR Data
Funded by NIH/NLM. (1R01LM011829, $.73M), 2014 – 2019 (Impact score 22, 4% percentile)
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Main
Education
- PhD, Computer Science, Brigham Young University
- MS, Computer Science, Brigham Young University
- BS, Biology and Computer Science, Beijing Normal University
Areas of Expertise
- Applying semantic and ontology technologies to clinical and biomedical applications
- Ontology generation and conceptual modeling
- Common terminology and ontology services
- Information extraction and integration
- Big data analysis using machine learning and statistical methods
-
Research Projects
- Temporal relation modeling, extraction and reasoning
- Secondary use of EHR data for clinical and translational studies
- Ontology-based analysis for cancer drug repurposing
- Vaccine and drug safety analysis
- Ontology-based mHealth and patient education
- Ontology-based personalized decision support systems
-
Funding
- Program Director / Principal Investigator
Dynamic learning for post-vaccine event prediction using temporal information in VAERS
(1R01AI130460, $3.12 M), 2017-2022 (Impact score 16, 1% percentile)
- Program Director / Principal Investigator
Metadata applications on informed content to facilitate biorepository data regulation and sharing
Funded by NIH/NHGRI (1U01HG009454, $1.4M), 2016-2019 (Impact score 26)
- Program Director / Principal Investigator
Patient Medical History Representation, Extraction, and Inference from EHR Data
Funded by NIH/NLM. (1R01LM011829, $.73M), 2014 – 2019 (Impact score 22, 4% percentile)
