An Intelligent Assistant for Medical Doctors when Prioritising Pathology Results

In any hospital millions of computerised pathology results are produced every year. The resource required by doctors to check these results, so as to determine whether some action is required or simply to endorse the result, is substantial. The issue is compounded by additional issues such as spurious test results or uncertain risk level for potential diseases. Prioritising pathology results is thus a significant challenge. This PhD research project is directed at the utilisation of the tools and techniques of Machine Learning to help prioritise pathology results to save doctor time. More specifically the work is directed at using patterns extracted from pathology historical records to prioritise previously unseen pathology results. A particular challenge is that the data available is unlabelled; the machine learning must therefore be conducted in an unsupervised manner or using a proxy for a ground truth. One idea is to priortise through the identification of outliers, another is to identify patterns and trends in existing patient records that have led to changes in care and use these patterns and trends as a proxy mechanism.