Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent

The search for early biomarkers of mild cognitive impairment (MCI) has been central to the Alzheimer’s Disease (AD) and dementia research community in recent years. To identify MCI status at the earliest possible point, recent studies have shown that linguistic markers such as word choice, utterance and sentence structures can potentially serve as preclinical behavioral markers. Here we present an adaptive dialogue algorithm (an AI-enabled dialogue agent) to identify sequences of questions (a dialogue policy) that distinguish MCI from normal (NL) cognitive status. Our AI agent adapts its questioning strategy based on the user’s previous responses to reach an individualized conversational strategy per user. Because the AI agent is adaptive and scales favorably with additional data, our method provides a potential avenue for large-scale preclinical screening of neurocognitive decline as a new digital biomarker, as well as longitudinal tracking of aging patterns in the outpatient setting.

[1]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[2]  M. Weiner.  Commentary on “Diagnosis of Alzheimer's disease: Two decades of progress.” Central role of technology in the treatment and prevention of Alzheimer's disease , 2005, Alzheimer's & Dementia.

[3]  Steve J. Young,et al.  A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies , 2006, The Knowledge Engineering Review.

[4]  Christopher Clark,et al.  Disease-modifying therapies for Alzheimer disease , 2007, Neurology.

[5]  Susanne Bay,et al.  Handbook of Research on User Interface Design and Evaluation for Mobile Technology , 2008 .

[6]  Janet C. Read,et al.  Using Wizard of Oz to Evaluate Mobile Applications , 2008 .

[7]  Alexander H. Waibel,et al.  Computers in the Human Interaction Loop , 2009, Handbook of Ambient Intelligence and Smart Environments.

[8]  L. Schneider,et al.  Report of the task force on designing clinical trials in early (predementia) AD , 2010, Neurology.

[9]  C. Jack,et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade , 2010, The Lancet Neurology.

[10]  Brian Roark,et al.  Spoken Language Derived Measures for Detecting Mild Cognitive Impairment , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[11]  L. McEvoy,et al.  Predicting MCI outcome with clinically available MRI and CSF biomarkers , 2011, Neurology.

[12]  Lukás Burget,et al.  Strategies for training large scale neural network language models , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.

[13]  C. Kim,et al.  Social isolation and quality of life in Alzheimer's dementia patients with Parkinson's disease , 2012, Alzheimer's & Dementia.

[14]  Lehel Csató,et al.  Reinforcement learning with guided policy search using Gaussian processes , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[15]  Nick C Fox,et al.  Brain imaging in Alzheimer disease. , 2012, Cold Spring Harbor perspectives in medicine.

[16]  J. Kaye,et al.  Tools for advancing research into social networks and cognitive function in older adults , 2013, International Psychogeriatrics.

[17]  Matthew Henderson,et al.  Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised adaptation , 2014, 2014 IEEE Spoken Language Technology Workshop (SLT).

[18]  Richard Simon,et al.  Overfitting, generalization, and MSE in class probability estimation with high‐dimensional data , 2014, Biometrical journal. Biometrische Zeitschrift.

[19]  Shai Ben-David,et al.  Understanding Machine Learning: From Theory to Algorithms , 2014 .

[20]  M. Montero‐Odasso.  Gait performance as a biomarker of mild cognitive impairment: Clinical and imaging correlates , 2015, Alzheimer's & Dementia.

[21]  Paul M. Thompson,et al.  Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition , 2015, Front. Neurosci..

[22]  T. Litvinova,et al.  A STUDY OF LINGUISTIC FEATURES OF DECEPTIVE TEXTS WITH THE USE OF THE PROGRAM LINGUISTIC INQUIRY AND WORD COUNT , 2015 .

[23]  Sanja Fidler,et al.  Skip-Thought Vectors , 2015, NIPS.

[24]  C. Troakes,et al.  Diabetes, aging, and the brain: A comparative test of brain amylin in diabetics with late-onset Alzheimer's disease versus non-diabetics with late-onset Alzheimer's disease or early-onset Alzheimer's disease , 2015, Alzheimer's & Dementia.

[25]  V. Manera,et al.  Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease , 2015, Alzheimer's & dementia.

[26]  David A. Loewenstein,et al.  Web-enabled conversational interactions as a method to improve cognitive functions: Results of a 6-week randomized controlled trial , 2015, Alzheimer's & dementia.

[27]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[28]  Philip S. Thomas,et al.  High-Confidence Off-Policy Evaluation , 2015, AAAI.

[29]  Загоровская Ольга Владимировна,et al.  Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count , 2015 .

[30]  B. Lawlor,et al.  INTERACTIVE IMPACTS OF LONELINESS AND SOCIAL ISOLATION ON INCIDENT DEMENTIA IN THE ENGLISH LONGITUDINAL STUDY OF AGEING , 2016, Alzheimer's & Dementia.

[31]  Taghi M. Khoshgoftaar,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[32]  Paul M. Thompson,et al.  Discriminative fusion of multiple brain networks for early mild cognitive impairment detection , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[33]  Kathleen C. Fraser,et al.  Linguistic Features Identify Alzheimer's Disease in Narrative Speech. , 2015, Journal of Alzheimer's disease : JAD.

[34]  Avraham Shtub,et al.  Simulation Training: Evaluating the Instructor’s Contribution to a Wizard of Oz Simulator in Obstetrics and Gynecology Ultrasound Training , 2017, JMIR medical education.

[35]  Peter Stone,et al.  Reinforcement learning , 2019, Scholarpedia.

[36]  Meysam Asgari,et al.  Predicting mild cognitive impairment from spontaneous spoken utterances , 2017, Alzheimer's & dementia.

[37]  Jiliang Tang,et al.  A Survey on Dialogue Systems: Recent Advances and New Frontiers , 2017, SKDD.

[38]  James R. Glass,et al.  Spoken language biomarkers for detecting cognitive impairment , 2017, 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).

[39]  Richard S. Sutton,et al.  Reinforcement Learning , 1992, Handbook of Machine Learning.

[40]  Jan Snajder,et al.  TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors , 2018, SemEval@NAACL-HLT.

[41]  C. Jack,et al.  NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease , 2018, Alzheimer's & Dementia.