Abstract
We present data demonstrating how brain health may be assessed by applying data-mining and text analytics to patient language. Three brain-based disorders are investigated - Alzheimer’s Disease, cognitive impairment and clinical depression. Prior studies identify particular language characteristics associated with these disorders. Our data show computer-based pattern recognition can distinguish language samples from individuals with and without these conditions. Binary classification accuracies range from 73% to 97% depending on details of the classification task. Text classification accuracy is known to improve substantially as training data approaches web-scale. Such a web scale dataset seems inevitable given the ubiquity of social computing and its language intensive nature. Given this context, we claim that the classification accuracy levels obtained in our experiments are significant findings for the fields of web intelligence and applied brain informatics.
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Jarrold, W.L., Peintner, B., Yeh, E., Krasnow, R., Javitz, H.S., Swan, G.E. (2010). Language Analytics for Assessing Brain Health: Cognitive Impairment, Depression and Pre-symptomatic Alzheimer’s Disease. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds) Brain Informatics. BI 2010. Lecture Notes in Computer Science(), vol 6334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15314-3_28
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DOI: https://doi.org/10.1007/978-3-642-15314-3_28
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