Probabilistic Graphical Models: Applications in Biomedicine


May 24, 2012


Enrique Sucar


National Institute for Astrophysics, Optics, and Electronics (INAOE)


Probabilistic graphical models include a variety of techniques based on probability and decision theory-techniques that give us a theoretically well-founded basis for making decisions under conditions of uncertainty and to solve complex problems efficiently. Over the last year, these methods have been used in a great variety of applications, from medical expert systems to intelligent user interfaces.

In this talk, I give a general introduction to probabilistic graphical models and describe some of the most popular ones, such as Bayesian networks and Markov decision processes. Then I demonstrate their application in three complex problems in biomedicine: (1) helping a physician guide an endoscope in the colon, (2) modeling the evolutionary networks of HIV, and (3) adapting a stroke rehabilitation system for the patient.


Enrique Sucar

L. Enrique Sucar is director of research at the National Institute for Astrophysics, Optics and Electronics in Puebla, Mexico. His main research interests are in graphical models and probabilistic reasoning, and their applications in computer vision, robotics, and biomedicine. Enrique has been an invited professor at the University of British Columbia, Canada; Imperial College, London; and INRIA, France, and has authored more than 150 publications and directed 15 PhD theses. He is a member of the National Research System and the Mexican Science Academy and is a senior member of the IEEE. In addition, he has served as president of the Mexican AI Society, has been a member of the advisory board of IJCAI, and is an associate editor of the journal ComputaciĆ³n y Sistema. He received his PhD in computing from Imperial College, London.