This picture is taken from a recent article by Kamalkumar Mistry which explains the benefits of possible combining event processing with neural networks, starting by explaining each of these technologies separately and then providing a patient monitoring example. In this example the combination of the two technologies is that a patient is monitored in real-time, the signals from the monitors are getting to an event processing system as an input, and after processing is done the output is fed into a neural networks, which recommends action based on an individual model of each individual patient.
This is a valid use case, I actually thought about additional use of neural nets and this is to tune up the monitoring, in event processing terminology - set up the patterns to be monitored. Taking back the monitoring patients example, which is actually one of the use cases we analyzed in the past, in fact it is example no.1 in the examples we put in the introductory chapter of the EPIA book, (chapter 1, page 7). In our example, the physician can tune up the system to have individual monitoring patterns for each individual patient, since in different patients, different combinations of signals over time may mean different things. The event processing pattern can be recommended by the neural network system based on the same patient model mentioned by Mistry, and in this case the relationship between the neural net and the event processing system are reversed.
Anyway - it is interesting to investigate this combination further.
