CS793: Health Informatics II: Application Domains

People:

Dr .Jesse Hoey, (jhoey@cs.uwaterloo.ca)

TIMETABLE:

Meetings will take place once per week in MC2036
Monday 1600-1850hrs (4:00pm-6:50pm)

STRUCTURE

The course will consist of one 3-hour in-class session per week
The course will consists of a mixture of lectures, seminars by guests, and paper discussions.
There will be 2 assignments (paper critiques) and a project (including proposal).

READINGS:

Primary Texts:

Edward H. Shortliffe and James J. Cimino "Biomedical Informatics: Computer Applications in Health Care and Biomedecine". Third Edition, Springer, 2006
(parts are avaialable online)
Ira J. Kalet "Principles of Biomedical Informatics". Academic Press, 2009.

Secondary Readings:

(see table below)

Assessment

Paper critiques (2 or 3) will consist of reading a research paper (from those suggested, or by choice with approval), and presenting a critique of the paper orally (during a discussion) and written (1-2 pages). Details on how to structure your written critique can be found here. Paper critiques are worth 40\% of the final mark.
Group or Solo Project . More details here. The project is worth 60\% of the final mark.

COURSE OUTLINE

In progress: to be determined in part by students and this list will be updated as we go along.
The topics currently scheduled are shown below.
Each week, we will have a lecture or invited speaker on a topic (1.5 hours) and a paper presentation by a student followed by a discussion (1.5 hours).
Usually, the lecture will come first (at 4pm), but on the weeks with a * next to the date, the lecture will come after the paper discussion (at 5:30)
The topic and paper do not have to be in the same area.
Each week, one student must propose a paper for the group to read for the following week

week	Date	Speaker	Topic	Presenter	Research papers(s)	Related readings
1	Jan 10	Jesse	Assistive Technologies (slides)			Martha E. Pollack. Intelligent Technology for an Aging Population. AI Magazine. Vol 26. No. 2 (2005) Jesse Hoey, Thomas Ploetz, Dan Jackson, Patrick Olivier, Andrew Monk and Cuong Pham Rapid Specification and Automated Generation of Prompting Systems to Assist People with Dementia. Pervasive and Mobile Computing, 2011 Alex Mihailidis, Jennifer N. Boger, Marcelle Candido and Jesse Hoey The COACH prompting system to assist older adults with dementia through handwashing: An efficacy study. BMC Geriatrics, 8, 28, 2008 Jesse Hoey, Pascal Poupart, Axel von Bertoldi, Tammy Craig, Craig Boutilier and Alex Mihailidis Automated Handwashing Assistance For Persons With Dementia Using Video and a Partially Observable Markov Decision Process. Computer Vision and Image Understanding (CVIU), 114, 5, May, 2010
2	Jan 17	Jesse	Probability, Bayesian Networks and Diagnosis (slides)	Jesse (slides) (critique)	J. Wu, A. Osuntogun, T. Choudhury, M. Philipose, J. Rehg A Scalable Approach to Activity Recognition based on Object Use IEEE Int. Conf. on Computer Vision (ICCV 2007) (2007), pp. 1-8.	Kalet Chap. 3
3	Jan 24*	Ming Li	Bioinformatics	Raymond	Li Zhang, Aidong Zhang and Murali Ramanathan VizStruct: exploratory visualization for gene expression profiling Bioinformatics (2004) 20 (1): 85-92.	Shortliffe Chap 22 Bin Ma, John Tromp and Ming Li. PatternHunter: faster and more sensitive homology search Bioinformatics (2002) 18 (3): 440-445. Li M, Ma B, Kisman D, Tromp J. Patternhunter II: highly sensitive and fast homology search. J Bioinform Comput Biol. 2004 Sep;2(3):417-39. Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schaffer, Jinghui Zhang, Zheng Zhang, Webb Miller and David J. Lipman Gapped BLAST and PSI-BLAST: a new generation of protein database search programsNucl. Acids Res. (1997) 25 (17): 3389-3402. Ella Bingham and Heikki Mannila Random projection in dimensionality reduction: Applications to image and text data. Proc KDD 2001, San Francisco, CA.
4	Jan 31	Karim Keshavjee	Privacy, Data Quality, EMR	Ayman	Wendy W. Chapmana, Lee M. Christensenb, Michael M. Wagnera, Peter J. Haugb, Oleg Ivanova, John N. Dowlinga, Robert T. Olszewskia Classifying free-text triage chief complaints into syndromic categories with natural language processingAI in Medicine Vol. 33 Issue 1. Pages 33-40. January 2005. Local PDF copy available here	Handbook of Biosurveillance (9780123693785): Michael M. Wagner, Andrew W. Moore, Ron M. Aryel. Chapter 17 - Natural Language Processing for Biosurveillance Handbook of Biosurveillance (9780123693785): Michael M. Wagner, Andrew W. Moore, Ron M. Aryel. Chapter 23 - Chief Complaints and ICD Codes Chapman WW, Gundlapalli A, South B, Dowling JN. Natural Language Processing for Biosurveillance. In Infectious Disease Informatics and Biosurveillance: Research, Systems, and Case Studies, Springer (in press)
5	Feb 7	Jeff Orchard	Medical Image Processing	Miriam	Marwa N. Muhammada, Daniela S. Raicub, Jacob D. Furstb, Ekarin Varutbangkulb Texture versus Shape Analysis for Lung Nodule Similarity in Computed Tomography Studies Medical Imaging 2008: PACS and Imaging Informatics, edited by Katherine P. Andriole, Khan M. Siddiqui, Proc. of SPIE Vol. 6919, 69190I, (2008)	Shortliffe chapters 9, 18 Zitoval IVC 2003 is a survey of image registration (not just applied to medical imaging) Maes TMI 1997 is one of the first medical imaging papers about registration using mutual information VanLeemput TMI 1999 is a basic intro to medical imaging segmentation using clustering (estimation maximization) Orchard TIP 2010 links registration and segmentation into a single framework Warfield SK, Zou KH, and Wells WM. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 2004;23(7):903-21.
6	Feb 14*	Mathieu Sinn	Human activity recognition in health care	Andre	VanLeemput TMI 1999	Shortliffe chapter 17 L. Liao, D. Fox, and H. Kautz. Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields. International Journal of Robotics Research, 2007. T.L.M. van Kasteren, G. Englebienne and B.J.A. Krose. An Activity Monitoring System for Elderly Care Using Generative and Discriminative Models. Personal and Ubiquitous Computing, 2010. M. Sinn and P. Poupart. Activity Recognition for Users of Rolling Walker Mobility Aids. NIPS Workshop on Machine Learning for Assistive Technologies, 2010.
7	Feb 21	Reading week - no class
8	Feb 28*	Daniel Heo	Telehealth and Telus Healthspace	Angela	Shahriyar Amin and Priya Narasimhan TwitterJacket: An automated activity and health monitoring solution for the elderly Tech. Report, CyLab,CMU 2009	Kim et al A mobile telemedicine system for remote consultation in cases of acute stroke Journal of Telemedicine and Telecare; 15: 102-107, 2009
9	March 7	Paul Stolee	Using health information to enhance musculoskeletal rehabilitation for older persons. Slides	Hadi	Distributed patient scheduling in hospitals by Paulussen, T.O. and Jennings, N.R. and Decker, K.S. and Heinzl, A., IJCAI 2003.local copy Multi-agent Pareto appointment exchanging in hospital patient scheduling by Ivan Vermeulen, Sander Bohte, Koye Somefun, Han La Poutr. Service Oriented Computing and Applications, Vol 1, No 3, pp185-196, 2007 local copy	The K-nearest neighbor algorithm predicted rehabilitation potential better than current Clinical Assessment ProtocolMu Zhua, Wenhong Chena, John P. Hirdesb and Paul Stolee. Journal of Clinical Epidemiology 60 (2007) Sharing clinical information across care settings: the birth of an integrated assessment system.Leonard C. Gray, Katherine Berg, Brant E. Fries, Jean-Claude Henrard, John P. Hirdes, Knight Steel, and John N. Morris. BMVC Health Services Research 2009, 9:71
10	March 14	Ian McKillop	Public Health Informatics	Henry	An integrated machine learning approach to stroke prediction Aditya Khosla, Yu Cao, Cliff Chiung-Yu Lin, Hsu-Kuang Chiu, Junling Hu, Honglak Leey. KDD '10 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining; ACM New York, NY, USA 2010
11	March 21	none		Tariq Shehroz	Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski & Larry Brilliant Detecting influenza epidemics using search engine query data Nature Vol 457, 19 Feb. 2009 Chris Geib Problems with Intent Recognition for Elder Care AAAI Workshop on Automation as Caregiver, 2002.
12	March 28	none		Seid Anup	F Alagoz, A Calero Valdez et al From Cloud Computing to Mobile Internet, From User Focus to Culture and Hedonism The Crucible of Mobile Health Care and Wellness Applications ICPCA, 2010 Courtney D. Corley, Diane J. Cook, Armin R. Mikler and Karan P. Singh Text and Structural Data Mining of Influenza Mentions in Web and Social Media Special Issue of Int. Journal of Environmental Research and Public Health 2010, 7(2), 596-615
11	April 11	Course Project Reports