TY - GEN
T1 - Personalized early stage alzheimer's disease detection
T2 - 19th SIGBioMed Workshop on Biomedical Language Processing, BioNLP 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
AU - Wang, Ning
AU - Luo, Fan
AU - Peddagangireddy, Vishal
AU - Subbalakshmi, K. P.
AU - Chandramouli, R.
N1 - Publisher Copyright:
© Association for Computation Linguistics.
PY - 2020
Y1 - 2020
N2 - Alzheimer's disease (AD)-related global healthcare cost is estimated to be $1 trillion by 2050. Currently, there is no cure for this disease; however, clinical studies show that early diagnosis and intervention helps to extend the quality of life and inform technologies for personalized mental healthcare. Clinical research indicates that the onset and progression of Alzheimer's disease lead to dementia and other mental health issues. As a result, the language capabilities of patient start to decline. In this paper, we show that machine learningbased unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer's disease. We demonstrate this approach on 10 year's (1980 to 1989) of President Ronald Reagan's speech data set. Key linguistic biomarkers that indicate early-stage AD are identified. Experimental results show that Reagan had early onset of Alzheimer's sometime between 1983 and 1987. This finding is corroborated by prior work that analyzed his interviews using a statistical technique.
AB - Alzheimer's disease (AD)-related global healthcare cost is estimated to be $1 trillion by 2050. Currently, there is no cure for this disease; however, clinical studies show that early diagnosis and intervention helps to extend the quality of life and inform technologies for personalized mental healthcare. Clinical research indicates that the onset and progression of Alzheimer's disease lead to dementia and other mental health issues. As a result, the language capabilities of patient start to decline. In this paper, we show that machine learningbased unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer's disease. We demonstrate this approach on 10 year's (1980 to 1989) of President Ronald Reagan's speech data set. Key linguistic biomarkers that indicate early-stage AD are identified. Experimental results show that Reagan had early onset of Alzheimer's sometime between 1983 and 1987. This finding is corroborated by prior work that analyzed his interviews using a statistical technique.
UR - http://www.scopus.com/inward/record.url?scp=85118150659&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85118150659
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 133
EP - 139
BT - BioNLP 2020 - 19th SIGBioMed Workshop on Biomedical Language Processing, Proceedings of the Workshop
Y2 - 9 July 2020
ER -