Researchers hope voice assistants can spot signs of dementia

#artificialintelligence

An effort to use voice-assistant devices like Amazon's Alexa to detect signs of memory problems in people has gotten a boost with a grant from the federal government. Researchers from Dartmouth-Hitchcock and the University of Massachusetts Boston will get a four-year $1.2 million grant from the National Institute on Aging. The team hopes to develop a system that would use machine and deep learning techniques to detect changes in speech patterns to determine if someone is a risk of developing dementia or Alzheimer's. "We are tackling a significant and complicated data-science question: whether the collection of long-term speech patterns of individuals at home will enable us to develop new speech-analysis methods for early detection of this challenging disease," Xiaohui Liang, an assistant professor of computer science from the University of Massachusetts Boston, said in a statement. "Our team envisions that the changes in the speech patterns of individuals using the voice assistant systems may be sensitive to their decline in memory and function over time."


Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders

arXiv.org Artificial Intelligence

For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributions and real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.


Discriminative Feature Selection for Uncertain Graph Classification

arXiv.org Machine Learning

Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objects are certain, and the binary edges within each graph represent the "presence" of linkages among the nodes. In many real-world applications, however, the linkage structure of the graphs is inherently uncertain. Therefore, existing measurements of interestingness based upon certain graphs are unable to capture the structural uncertainty in these applications effectively. In this paper, we study the problem of discriminative subgraph feature selection from uncertain graphs. This problem is challenging and different from conventional subgraph mining problems because both the structure of the graph objects and the discrimination score of each subgraph feature are uncertain. To address these challenges, we propose a novel discriminative subgraph feature selection method, DUG, which can find discriminative subgraph features in uncertain graphs based upon different statistical measures including expectation, median, mode and phi-probability. We first compute the probability distribution of the discrimination scores for each subgraph feature based on dynamic programming. Then a branch-and-bound algorithm is proposed to search for discriminative subgraphs efficiently. Extensive experiments on various neuroimaging applications (i.e., Alzheimer's Disease, ADHD and HIV) have been performed to analyze the gain in performance by taking into account structural uncertainties in identifying discriminative subgraph features for graph classification.


Two-block vs. Multi-block ADMM: An empirical evaluation of convergence

arXiv.org Machine Learning

Alternating Direction Method of Multipliers (ADMM) has become a widely used optimization method for convex problems, particularly in the context of data mining in which large optimization problems are often encountered. ADMM has several desirable properties, including the ability to decompose large problems into smaller tractable sub-problems and ease of parallelization, that are essential in these scenarios. The most common form of ADMM is the two-block, in which two sets of primal variables are updated alternatingly. Recent years have seen advances in multi-block ADMM, which update more than two blocks of primal variables sequentially. In this paper, we study the empirical question: {\em Is two-block ADMM always comparable with sequential multi-block ADMM solving an equivalent problem?} In the context of optimization problems arising in multi-task learning, through a comprehensive set of experiments we surprisingly show that multi-block ADMM consistently outperformed two-block ADMM on optimization performance, and as a consequence on prediction performance, across all datasets and for the entire range of dual step sizes. Our results have an important practical implication: rather than simply using the popular two-block ADMM, one may considerably benefit from experimenting with multi-block ADMM applied to an equivalent problem.


Cognitive Assessment Estimation from Behavioral Responses in Emotional Faces Evaluation Task -- AI Regression Approach for Dementia Onset Prediction in Aging Societies

arXiv.org Machine Learning

We present a practical health-theme machine learning (ML) application concerning `AI for social good' domain for `Producing Good Outcomes' track. In particular, the solution is concerning the problem of a potential elderly adult dementia onset prediction in aging societies. The paper discusses our attempt and encouraging preliminary study results of behavioral responses analysis in a working memory-based emotional evaluation experiment. We focus on the development of digital biomarkers for dementia progress detection and monitoring. We present a behavioral data collection concept for a subsequent AI-based application together with a range of regression encouraging results of Montreal Cognitive Assessment (MoCA) scores in the leave-one-subject-out cross-validation setup. The regressor input variables include experimental subject's emotional valence and arousal recognition responses, as well as reaction times, together with self-reported education levels and ages, obtained from a group of twenty older adults taking part in the reported data collection project. The presented results showcase the potential social benefits of artificial intelligence application for elderly and establish a step forward to develop ML approaches, for the subsequent application of simple behavioral objective testing for dementia onset diagnostics replacing subjective MoCA.