Monday 26/02/07

Had a look around the OU site. I downloaded a presentation on the T4xx projects and got a bit of a fright. There was mention of CD-ROMs accessing library resources etc. I've got to get my finger out! Need to read through the blurb the OU sent, and make sure I fully understand what's expected of me. Must check whether I've got a CD-ROM and read the study guide to work out what I have to do for TMA01.

Sunday 25/02/2007

More thoughts about the project. I think a rule based system may be the way to take the raw data and present this in a usable form to the NN. Still not sure about what to do the project on. Perhaps using AI to examine pathologist workload statistics. To classify different histology specimen types into the 10 different work weight categories depending on the number of blocks taken, the routine, special stains and immunocytochemistry requests on each case etc. I would like to do something in neuropathology. I like the idea of using a artficial neural network to analyse a problem in a real neural network. I need to have a chat with some of the pathologists this week but our Clinical Pathology Accreditation Inspection is on Tuesday and Wednesday so it won't be easy to find the time.

Opening Thoughts

The aim of this log is to show how I have developed this project from the beginning to the final document. I have an aversion to planning work properly so I will have to remain determined to write my thoughts in this log regularly. Up till now I have done some reading around AI techniques particularly Neural Networks. My experience of T396 was that I found Flex, the rule based system difficult to use but was much happier using the Neural Works. Therefore I really want to base this project around Neural Networks (NNs). So I want this project to look at the feasibility of using NNs in a particular situation rather than the more obvious project of comparing Rule Based Systems with NNs in a particular situation. That is my aim however I may find I need to consider Rule Based Systems as the project is implemented.

I have given a great deal of thought to what situation I will apply the NNs to. I have looked at one example project on the OU’s web site and read an interesting article about using a Kohonen Network to sort photos according to what is actually in the photo, rather like Adobe Photoshop. However I would really like to use data from work. The problem here though is getting hold of clinical outcomes of patients due to patient confidentiality. I would like to get results from various tests from Pathology, Radiography etc and see if an AI system can predict the clinical outcome for the patient. Cancer treatment seems to be a good area. If I can find out details of the disease that was diagnosed, the histology and immunocytochemistry results, patients age, sex, treatment eg chemo/radiotherapy and the five year survival rates for the disease it may be possible to predict the likely outcome for undiagnosed patient. That said this may be too obvious e.g. if the tumour has shows metastasis the patient will probably die, if there is no metastasis they will probablyl survive. I may have to look at a specific grade of disease. This is rather like the oiled seabird project in the ETMA for T326. An AI system like this if it worked, would be able to predict which patients are likely to survive and so who to utilise valuable resources trying to save. In practice treatment is never as cut and dried as this. The more I think about this the less convinced I am that it would be any good.