Ricardo Henao

Henao

Assistant Professor in Biostatistics and Bioinformatics

Appointments and Affiliations

  • Assistant Professor in Biostatistics and Bioinformatics
  • Assistant Professor in the Department of Electrical and Computer Engineering
  • Member of Duke Center for Applied Genomics and Precision Medicine
  • Member of the Duke Clinical Research Institute

Contact Information

  • Office Location: 140 Science Drive, Durham, NC 27710
  • Office Phone: (919) 668-0647
  • Email Address: ricardo.henao@duke.edu

Education

  • Ph.D. Technical University of Denmark (Denmark), 2011

Courses Taught

  • COMPSCI 394: Research Independent Study
  • ECE 391: Projects in Electrical and Computer Engineering
  • ECE 899: Special Readings in Electrical Engineering
  • EGR 190: Special Topics in Engineering
  • EGR 393: Research Projects in Engineering
  • EGR 590: Special Topics in Engineering
  • POE 190: Practice Oriented Education

In the News

Representative Publications

  • Lorenzi, E; Henao, R; Heller, K, Hierarchical infinite factor models for improving the prediction of surgical complications for geriatric patients, The Annals of Applied Statistics, vol 13 no. 4 (2019), pp. 2637-2661 [10.1214/19-AOAS1292] [abs].
  • Lydon, EC; Henao, R; Burke, TW; Aydin, M; Nicholson, BP; Glickman, SW; Fowler, VG; Quackenbush, EB; Cairns, CB; Kingsmore, SF; Jaehne, AK; Rivers, EP; Langley, RJ; Petzold, E; Ko, ER; McClain, MT; Ginsburg, GS; Woods, CW; Tsalik, EL, Validation of a host response test to distinguish bacterial and viral respiratory infection., Ebiomedicine, vol 48 (2019), pp. 453-461 [10.1016/j.ebiom.2019.09.040] [abs].
  • Engelhard, MM; Oliver, JA; Henao, R; Hallyburton, M; Carin, LE; Conklin, C; McClernon, FJ, Identifying Smoking Environments From Images of Daily Life With Deep Learning., Jama Network Open, vol 2 no. 8 (2019) [10.1001/jamanetworkopen.2019.7939] [abs].
  • Benitez, M; Tian, J; Kelly, M; Selvakumaran, V; Phelan, M; Mazurowski, M; Lo, JY; Rubin, GD; Henao, R, Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports, Progress in Biomedical Optics and Imaging Proceedings of Spie, vol 10950 (2019) [10.1117/12.2512886] [abs].
  • Han, S; Tian, J; Kelly, M; Selvakumaran, V; Henao, R; Rubin, GD; Lo, JY, Classifying abnormalities in computed tomography radiology reports with rule-based and natural language processing models, Progress in Biomedical Optics and Imaging Proceedings of Spie, vol 10950 (2019) [10.1117/12.2513577] [abs].