This proposal concerns the development and evaluation of computational methods through which linguistic manifestations of cognitive changes in Alzheimer?s Disease (AD) dementia can be identified in transcribed speech. Such methods are of value even prior to the availability of disease modifying treatments, as through earlier detection they provide the means to reduce the emotional and financial burden on patients, caregivers, and the healthcare system. Lack of a clear diagnosis in the face of cognitive manifestations of dementia can produce uncertainty, and negatively impact planning of future care. Misattributed AD symptoms can lead to social isolation. In addition, it is estimated that early and accurate diagnosis can help save an estimated $7.9 trillion in medical and care costs. With ~30-40% of healthy adults subjectively reporting forgetfulness on a regular basis, there is an urgent need to develop sensitive and specific, easy-to-use, safe, and cost-effective tools for monitoring AD-specific cognitive markers in individuals concerned about their cognitive function. Language reflects cognitive status, but manual analysis of language data is prohibitively time-consuming. In the proposed research we will develop and evaluate computational methods to identify linguistic biomarkers of AD, leveraging perplexity estimates derived from neural language models trained on transcripts of the speech of healthy controls only. This approach deviates from the supervised learning paradigm that characterizes most computational linguistics approaches to identifying AD, obviating the danger of overfitting to the characteristics of participants with dementia represented in the small datasets available for training. Nonetheless, our preliminary research has demonstrated that classification performance on the basis of such perplexity estimates rivals that documented with supervised machine learning models trained on hundreds of manually engineered features. The proposed research will result in a validated set of methods for detection of AD using transcribed speech, methods with the potential for broad dissemination on account of recent advances in automated speech recognition.

Public Health Relevance

The need to monitor unintended effects of medications has been highlighted by several high-profile events in which fatal side effects of approved drugs were detected after their release to market. In the proposed research, we will develop and evaluate methods to identify biologically plausible adverse drug events using both observational data and knowledge extracted from the biomedical literature. If successful, these methods will provide the means for earlier detection of harmful drug effects, limiting consequent morbidity and mortality.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
3R01LM011563-06S1
Application #
9881862
Study Section
Special Emphasis Panel (ZLM1)
Program Officer
Sim, Hua-Chuan
Project Start
2013-09-01
Project End
2021-05-31
Budget Start
2019-07-03
Budget End
2020-05-31
Support Year
6
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Washington
Department
Other Health Professions
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Mower, Justin; Subramanian, Devika; Cohen, Trevor (2018) Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications. J Am Med Inform Assoc 25:1339-1350
Cohen, Trevor; Widdows, Dominic (2017) Embedding of semantic predications. J Biomed Inform 68:150-166
Cai, Ruichu; Liu, Mei; Hu, Yong et al. (2017) Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports. Artif Intell Med 76:7-15
Yu, Zhiguo; Wallace, Byron C; Johnson, Todd et al. (2017) Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness. Stud Health Technol Inform 245:657-661
Amith, Muhammad; Cunningham, Rachel; Savas, Lara S et al. (2017) Using Pathfinder networks to discover alignment between expert and consumer conceptual knowledge from online vaccine content. J Biomed Inform 74:33-45
Mower, Justin; Subramanian, Devika; Shang, Ning et al. (2016) Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships. AMIA Annu Symp Proc 2016:1940-1949
Malec, Scott A; Wei, Peng; Xu, Hua et al. (2016) Literature-Based Discovery of Confounding in Observational Clinical Data. AMIA Annu Symp Proc 2016:1920-1929
Widdows, Dominic; Cohen, Trevor (2015) Reasoning with Vectors: A Continuous Model for Fast Robust Inference. Log J IGPL 23:141-173
Shang, Ning; Xu, Hua; Rindflesch, Thomas C et al. (2014) Identifying plausible adverse drug reactions using knowledge extracted from the literature. J Biomed Inform 52:293-310
Cohen, T; Widdows, D; Stephan, C et al. (2014) Predicting high-throughput screening results with scalable literature-based discovery methods. CPT Pharmacometrics Syst Pharmacol 3:e140