Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure.There are unanswered questions with regards to acute respiratory outcomes, particularly asthma, due to environmental exposures.  The study concluded that Kohonen self-organizing maps provide effective classification models to study asthma outcomes, particularly when using multidimensional data. 
Kohonen self-organizing maps
In contribution to asthma research, the current study explored a computational intelligence paradigm of artificial neural networks (ANNs) called self-organizing maps (SOM). 
ANNs
In contribution to asthma research, the current study explored a computational intelligence paradigm of artificial neural networks (ANNs) called self-organizing maps (SOM).  To train the SOM, air quality data (nitrogen dioxide, sulphur dioxide and particulate matter), interpolated to geocoded addresses of asthmatics, were used with clinical data to classify asthma outcomes.  Socio-demographic data such as age, gender and race were also used to perform the classification by the SOM.  The resultant SOM model had low quantization error. 
SOM