Notes 20110301 CIS 6050 Neural Networks
From SnOwy - Ed's Wiki Notebook
First class back from the break
- note -- a phase change in a trendline is called an intervention to statisticians
- example of neural network usages
Title: Detection of Disease Outbreaks in Pharmaceutical Sales
... Neural Networks and Threshold Algorithms
Glenn Guthrie, Deborah Stacey, David Calvert
- motivation: Walkerton outbreak -- E. coli 0157 (also Cryptosporidium, North Battleford, Saskatchewan)
- syndromic surveillance
- monitor data to figure out how healthy a population is
- count emergency room visits
- these two diseases have two very different progressions
- E. coli: very fast
- Cryptosporidium: slow onset -- sudden phase change
- performance measures -- false positives and false negatives or true positives and true negatives
- prefer false positives over false negatives
- that is, better not to miss outbreaks
- maximize of course true positives, true negatives as first priority
- we assume that the number of affected individuals (in an outbreak) is highly correlated with sales of medicines
- the data used was simulated data derived from the real data
- this is beneficial for any kind of medical study -- the data is now free to distribute without privacy restrictions
- used a sliding window in time to feed into the artificial neural network
- seven days -- today, today-1, today-2 .. today-6
- outbreak detection must be earlier not later
- threshold needed to detect outbreak
- vs industry standard: the moving average (an average calculated over the last n-days)
- this study was continued with telehealth data and with emergency room data
- thought that the emergency room data would be better (doctor looks over a patient)
- telehealth data was very accurate
- over the counter sales were really too variable to do anything useful
- emergency room data had too many confounding variables
- all of these worked with varying degrees of success
Adaptive Resonance Theory (ART)
- started as an attempt to do biological modeling
- how things in the brain work
- was interesting to start but not practical
- ended up being pretty practical
- similar to a kind of clustering called first-in clustering
- contrast: traditional clustering -- all data is evaluated simultaneously, cluster boundaries drawn together
- first-in clustering: the very first thing that is seen becomes the center of a cluster;
- a threshold determines when a new point is just too far and should spawn its own cluster
- -- ART ...
- active regulation of self-organizing learning
- -- actively tries to create new clusters instead of smoothing clusters together
- recognition by attention and expectation
- system recognizes patterns by looking for previously presented patterns
- patterns stored in weights
- design problem -- goal
- how do you design a network that can continue to learn without forgetting old patterns
- contrast: back propagation network pushes weights away if trained with highly contrasting exemplars
- how do we retain this plasticity
- how do intelligent systems remain adaptive in response to significant events
- and still remain stable in response to irrelevant events
- compare: back propagation does this with slow weight change
- a few irrelevant events will be smoothed out
- ART network must work in real time
- online training
- known as the stability-plasticity dilemma
- key computational ideas
- top-down learned expectations
- focus attention on bottom-up information such that previously learned patterns are stable
- new patterns can be added
- inputs (bottum-up) care compared with learned patterns (in weights)
- either clustered by system or create a new set of weights to store a new pattern