@InProceedings{shibata-EtAl:2016:ClinicalNLP,
  author    = {Shibata, Daisaku  and  Wakamiya, Shoko  and  Kinoshita, Ayae  and  Aramaki, Eiji},
  title     = {Detecting Japanese Patients with Alzheimer’s Disease based on Word Category Frequencies},
  booktitle = {Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {78--85},
  abstract  = {In recent years, detecting Alzheimer disease (AD) in early stages based on
	natural language processing (NLP) has drawn much attention. To date, vocabulary
	size, grammatical complexity, and fluency have been studied using NLP metrics.
	However, the content analysis of AD narratives is still unreachable for NLP.
	This study investigates features of the words that AD patients use in their
	spoken language. After recruiting 18 examinees of 53--90 years old (mean:
	76.89), they were divided into two groups based on MMSE scores. The AD group
	comprised 9 examinees with scores of 21 or lower. The healthy control group
	comprised 9 examinees with a score of 22 or higher. Linguistic Inquiry and Word
	Count (LIWC) classified words were used to categorize the words that the
	examinees used. The word frequency was found from observation. Significant
	differences were confirmed for the usage of impersonal pronouns in the AD
	group. This result demonstrated the basic feasibility of the proposed NLP-based
	detection
	approach.},
  url       = {http://aclweb.org/anthology/W16-4211}
}