Corpus ID: 49872665

Isolating effects of age with fair representation learning when assessing dementia

  title={Isolating effects of age with fair representation learning when assessing dementia},
  author={Zining Zhu and Jekaterina Novikova and F. Rudzicz},
  • Zining Zhu, Jekaterina Novikova, F. Rudzicz
  • Published 2018
  • Mathematics, Computer Science
  • ArXiv
  • One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts. However, these linguistic features are impacted in a similar but different fashion by the normal aging process. Aging is therefore a confounding factor, whose effects have been hard for machine learning classifiers to isolate. In this paper, we show that deep neural network (DNN) classifiers can infer ages from linguistic… CONTINUE READING

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    Publications referenced by this paper.
    Linguistic Features Identify Alzheimer's Disease in Narrative Speech.
    • 217
    • PDF
    Adam: A Method for Stochastic Optimization
    • 49,946
    • PDF
    Age-associated cognitive decline.
    • 623
    • PDF
    Detecting cognitive impairments by agreeing on interpretations of linguistic features
    • 9
    • PDF
    Generative Adversarial Nets
    • 17,848
    • PDF
    "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician.
    • 71,448
    • PDF
    Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
    • 18,924
    • PDF
    Normal cognitive aging.
    • 496