We are working on the data mining of audiology patient records, looking for factors influencing which patients would most benefit from being fitted with a hearing aid. Various research groups have used statistics and neural networks for integration of data, such as the chi-squared test or self organizing maps (SOMs). This motivates our research into a new architecture which combines neural network and statistical techniques suitable for mining heterogeneous audiology records. Audiology records contain the following three different types of data: 1. Audiograms (graphs of hearing ability at different frequencies) 2. Structured tabular data (gender, date of birth, etc) 3.Unstructured text (specific observations made about each patient in a field for comments/remarks) So far, we have performed clustering of hearing aid patient audiograms with an SOM, and clustering of audiograms using the k-means algorithm to identify 3 main groups of hearing aid wearers.
|Publication status||Published - 23 Oct 2009|
|Event||North East Post-Graduate Conference (NEPG) 2009 - Newcastle University|
Duration: 23 Oct 2009 → …
|Conference||North East Post-Graduate Conference (NEPG) 2009|
|Period||23/10/09 → …|