Education through machines is a reality. From industry to business and to education now, it is time to lose our reservations and embrace artificial intelligence. Teachers can see the limitations that technology can't or doesn't observe. However, there is no scalable way for students to closely engage with teachers as class sizes grow. This leads to gaps of knowledge between the lessons teachers submit to their administrators and the principles students are able to absorb.
Treatment plans for troubled teens are highly specialized. Designed to restore young people struggling with issues such as: anger, oppositional defiant disorder (ODD), attention deficit disorder (ADD), substance abuse, depression, grief and loss, adoption, obsessive compulsive disorder (OCD), eating disorders, self-harm, or rebellion, just to name a few. Located in Idaho, near the Sawtooth Mountains, we serve families from all over the U.S. Most of our parents and clients can come from California, Texas, Washington, Oregon, Illinois, Idaho, Colorado, Nevada, Georgia, Florida, New York, Connecticut, Massachusetts, Maine, and Tennessee just to list a few. Several families also come to us from overseas.
It shouldn't really be a surprise that artificial intelligence (AI) gets a special mention in the long-term plan for the NHS, published in March. AI is seen as important for the future of the NHS because it can make healthcare more effective and efficient, leaving staff free to focus on, as the plan puts it, the'complexity of human interactions that technology will never master'. With a growing population, limited resources yet more and more treatments available, the use of intelligent technology will be key to ensuring our healthcare services can keep pace. At Grow MedTech, we see AI as one of the most important digital technologies that will combine with traditional medtech to create the products and technologies of the future. And Yorkshire is a hotbed for the technology, with all of our partner universities offering expertise in the field.
ADHD is being recognized as a diagnosis which persists into adulthood impacting economic, occupational, and educational outcomes. There is an increased need to accurately diagnose and recommend interventions for this population. One consideration is the development and implementation of reliable and valid outcome measures which reflect core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity when compared to their peers (Michalek et al., 2014). A reduction in working memory capacity indicates attentional control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as eye tracking technology, to generate a relationship between ADHD and measures of working memory capacity would be useful to advancing our understanding and treatment of the diagnosis in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a working memory capacity task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study.
In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed bandit field is currently flourishing, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize state-of-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this exciting and fast-growing field.
A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the identification of the computational dynamics underlying task processing. Here we advance a state space model (SSM) based on generative piecewise-linear recurrent neural networks (PLRNN) to assess dynamics from neuroimaging data. In contrast to many other nonlinear time series models which have been proposed for reconstructing latent dynamics, our model is easily interpretable in neural terms, amenable to systematic dynamical systems analysis of the resulting set of equations, and can straightforwardly be transformed into an equivalent continuous-time dynamical system. The major contributions of this paper are the introduction of a new observation model suitable for functional magnetic resonance imaging (fMRI) coupled to the latent PLRNN, an efficient stepwise training procedure that forces the latent model to capture the 'true' underlying dynamics rather than just fitting (or predicting) the observations, and of an empirical measure based on the Kullback-Leibler divergence to evaluate from empirical time series how well this goal of approximating the underlying dynamics has been achieved. We validate and illustrate the power of our approach on simulated 'ground-truth' dynamical (benchmark) systems as well as on actual experimental fMRI time series. Given that fMRI is one of the most common techniques for measuring brain activity non-invasively in human subjects, this approach may provide a novel step toward analyzing aberrant (nonlinear) dynamics for clinical assessment or neuroscientific research.
Sina Habibi, CEO of Cognetivity Neurosciences, spoke with INN about the company's partnership with DPUK and additional plans for 2019. At the recent Cantech Investment Conference, Sina Habibi, CEO of Cognetivity Neurosciences (CSE:CGN,OTCQB:CGNSF) spoke with the Investing News Network (INN) about the company's partnership with the Dementia Platform UK (DPUK) and additional plans for 2019. Habibi said the company will be putting more efforts into its artificial intelligence (AI) platform and collecting more data as it seeks to train its solutions to detect mental health disorders, like attention deficit hyperactivity disorder (ADHD). As it currently stands, Cognetivity is using AI and machine learning to aid in the early detection of dementia and Alzheimer's disease. On that note, in addition to the DPUK partnership, Habibi spoke to INN about a health application the company has that could be launched by the end of 2019.
Concentration and attention deficit could soon be treated with an app and not with pharmaceuticals. Researchers have released a specifically designed game called Decoder which they claim is just as effective as Ritalin when it comes to squashing the symptoms of Attention Deficit Hyperactivity Disorder (ADHD). The game asks people to tap the screen when they spot number sequences or patterns. Participants showed improved attention span and concentration after eight hours of use over the course of a month. It claims those who used the app showed increased performance comparable to the effects seen using stimulants such as Ritalin - a common treatment for Attention Deficit Hyperactivity Disorder (ADHD).
Normative modeling has recently been introduced as a promising approach for modeling variation of neuroimaging measures across individuals in order to derive biomarkers of psychiatric disorders. Current implementations rely on Gaussian process regression, which provides coherent estimates of uncertainty needed for the method but also suffers from drawbacks including poor scaling to large datasets and a reliance on fixed parametric kernels. In this paper, we propose a deep normative modeling framework based on neural processes (NPs) to solve these problems. To achieve this, we define a stochastic process formulation for mixed-effect models and show how NPs can be adopted for spatially structured mixed-effect modeling of neuroimaging data. This enables us to learn optimal feature representations and covariance structure for the random-effect and noise via global latent variables. In this scheme, predictive uncertainty can be approximated by sampling from the distribution of these global latent variables. On a publicly available clinical fMRI dataset, we compare the novelty detection performance of multivariate normative models estimated by the proposed NP approach to a baseline multi-task Gaussian process regression approach and show substantial improvements for certain diagnostic problems.
There is a wide array of existing instruments used to assess childhood behavior and development for the evaluation of social, emotional and behavioral disorders. Many of these instruments either focus on one diagnostic category or encompass a broad set of childhood behaviors. We built an extensive ontology of the questions associated with key features that have diagnostic relevance for child behavioral conditions, such as Autism Spectrum Disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and anxiety, by incorporating a subset of existing child behavioral instruments and categorizing each question into clinical domains. Each existing question and set of question responses were then mapped to a new unique Rosetta question and set of answer codes encompassing the semantic meaning and identified concept(s) of as many existing questions as possible. This resulted in 1274 existing instrument questions mapping to 209 Rosetta questions creating a minimal set of questions that are comprehensive of each topic and subtopic. This resulting ontology can be used to create more concise instruments across various ages and conditions, as well as create more robust overlapping datasets for both clinical and research use.