Week beginning 25th March

Spent time at work extracting breast cancer data from WinPath. I spoke to staff from the breast care team to see if I can find out about patient outcomes from those who had histology results. I have a couple of contacts to follow up.

I have decided to try to use the data from work if I can get the clinical outcomes because I always have the breast cancer data that I have down loaded as a backup if this isn't achieveable. I will have to be careful not to use patient identifiable data because of the Data Protection Act. Therefore I won't be able to use a date of birth field but age will be OK.

Downloaded several papers on breast cancer prognosis and artificial intelligence via IEEE Xplore. I am reading through these but generally they have used neural networks with some success and fuzzy logic in hybrid systems. I would like to use neural networks in a similar way although, providing I can use the data from work, I will use different prognostic indicators such as oestrogen and progesterone receptor status and her2 status which were not included in these papers. I will try training a multilayer perceptron and comparing this with a radial basis function network and/or Kohonen self organising network to look at supervised vs unsupervised learning. I will try using an one of these network that use unsupervised learning to cluster the data coupled to a MLP to classify the data clusters.

I will try using a rule based system to compare it's performance with that of the neural networks. I will try using fuzzy logic to produce rules based on analysing the data I have. For example: If number of lymph nodes is high and the tumour is large then the chances of reoccurence if high.

Now to sort out that TMA.