Clustering audiology data

Naveed Anwar, Michael Oakes, Stefan Wermter, Stefan Heinrich

Research output: Contribution to conferencePaperpeer-review

Abstract

In this paper we describe new results of statistical and neural data mining of audiology patient records, with the ultimate aim of looking for factors influencing which patients would most benefit from being fitted with a hearing aid. We describe how a combination of neural and statistical techniques can usefully subdivide a set of patients into clusters, based on their hearing thresholds at six different frequencies, and then label the clusters with meaningful text labels. In our first experiment, we cluster the patients based on similarities between their audiograms using k-means clustering, resulting in two main clusters. We then use the chi-squared test to label each cluster with the keywords selected from the text comment, diagnosis and hearing aid type associated with each patient which are most typical (and atypical) of each cluster. In our second experiment, we again cluster the patients based on similarities between their audiograms, but this time using a self-organizing map (SOM). Here the locations in the resulting map, corresponding to individual patients, are labeled with the type of hearing aid selected for each patient. We demonstrate that this automatic textual labeling addresses well the heterogeneous character of medical audiology records, since they consist of numeric, structured and free text data.
Original languageEnglish
Publication statusPublished - 27 May 2010
Event19th Annual Machine Learning Conference of Belgium and The Netherlands - Leuven
Duration: 27 May 2010 → …

Conference

Conference19th Annual Machine Learning Conference of Belgium and The Netherlands
Period27/05/10 → …

Fingerprint Dive into the research topics of 'Clustering audiology data'. Together they form a unique fingerprint.

Cite this