Bio: Dr. Ricky Taira obtained his Bachelor’s degree in electrical engineering in 1982, and went on to receive a PhD in biomedical physics in 1988 from UCLA. He is now a Professor in the Department of Radiological Sciences at UCLA’s David Geffen School of Medicine. His past research interests have included picture archive and communication systems (PACS), medical knowledge bases (the KMeD project) for intelligent patient case retrieval, and structuring clinical observations for disease modeling. Currently, his main research focus is on developing a cognitively inspired natural language processing system (NLP) for clinical reports. He is the co-PI and investigator of several NIH-funded grants. Dr. Taira is the UCLA site PI for a telemedicine screening grant for diabetic retinopathy in collaboration with Charles Drew University and the Los Angeles County of Health Services. Dr. Taira teaches courses in medical knowledge representation and medical imaging informatics that are part of the UCLA Medical and Imaging Informatics interdisciplinary training program.
Ogunyemi OI, Gandhi M, Lee M, Teklehaimanot S, Daskivich LP, Hindman D, Lopez K, Taira RK. Detecting diabetic retinopathy through machine learning on electronic health record data from an urban, safety net healthcare system. Submitted to the Journal of the American Medical Informatics Association Open, April 2021.
2020Taira RK, Garlid A, Speier W. Design considerations for a hierarchical semantic compositional framework for medical natural language understanding. Submitted to the Journal of Biomedical Informatics, 25 pages, November 2020.
Kim H, Kim J, Taira RK. Ambiguity in Communicating Intensity of Physical Activity: Survey Study. JMIR Public Health Surveill. 2020 May 28;6(2):e16303. doi: 10.2196/16303. PMID: 32348256; PMCID: PMC7290482.
2019Kim H, Mentzer J, Taira RK. Developing a Physical Activity Ontology to Support the Interoperability of Physical Activity Data. J Med Internet Res. 2019 Apr 23;21(4):e12776. doi: 10.2196/12776. PMID: 31012864; PMCID: PMC6658272.
2018Taira RK, Ogunyemi L, Kim H. Lexically grounded ontologic frames for medical NLP. Proc. of the AMIA Symposium, 2018.
Garcia-Gathright JI, Matiasz NJ, Adame C, Sarma KV, Sauer L, Smedley NF, Spiegel ML, Strunck J, Garon EB, Taira RK, Aberle DR, Bui AAT. Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies. Comput Biol Med. 2018 Jan 1;92:55-63. DOI: 10.1016/j.compbiomed.2017.10.034. Epub 2017 Nov 3. PMID: 29149658; PMCID: PMC5762403.
2017Rios Piedra EAR, Taira RK, El-Saden S, Ellingson BM, Bui AAT, Hsu W. Assessing Variability in Brain Tumor Segmentation to Improve Volumetric Accuracy and Characterization of Change. IEEE EMBS Int Conf Biomed Health Inform. 2016 Feb;2016:380-383. DOI: 10.1109/BHI.2016.7455914. Epub 2016 Apr 21. PMID: 28670648; PMCID: PMC5489257.
Rios Piedra EAR, Ellingson B, El-Saden S, Taira RK, Bui AAT, Hsu W. Brain Tumor Segmentation by Variability Characterization of Tumor Boundaries. International Workshop on Brain lesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2017;:206–216.
Rios Piedra EAR, Ellingson B, El-Saden S, Cloughesy T, Taira RK, Bui AAT, Hsu W. NIMG-08. Evaluating the applicability of tumor probability maps as a resource for improved brain tumor segmentation. Neuro Oncology 19(Supp 6):v144, 2017.
Rios Piedra EAR, Orosz I, Zide M, El-Saden S, Taira RK, Bui AAT, Hsu W. A Usability Study to Evaluate the Impact of a Novel Automated Brain Tumor Assessment Application. Poster presented at American Medical Informatics Association. 2017.
