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AI for Mental Health

#artificialintelligence

The human brain is often called the most complex object in the known universe. Kind of makes you feel good about being human, no? The problem with complicated stuff is that it's hard to figure out. That makes effective eavesdropping quite challenging. It also means that a lot can go wrong.


AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment

arXiv.org Artificial Intelligence

Cognitive impairment has become epidemic in older adult population. The recent advent of tiny wearable and ambient devices, a.k.a Internet of Things (IoT) provides ample platforms for continuous functional and cognitive health assessment of older adults. In this paper, we design, implement and evaluate AutoCogniSys, a context-aware automated cognitive health assessment system, combining the sensing powers of wearable physiological (Electrodermal Activity, Photoplethysmography) and physical (Accelerometer, Object) sensors in conjunction with ambient sensors. We design appropriate signal processing and machine learning techniques, and develop an automatic cognitive health assessment system in a natural older adults living environment. We validate our approaches using two datasets: (i) a naturalistic sensor data streams related to Activities of Daily Living and mental arousal of 22 older adults recruited in a retirement community center, individually living in their own apartments using a customized inexpensive IoT system (IRB #HP-00064387) and (ii) a publicly available dataset for emotion detection. The performance of AutoCogniSys attests max. 93\% of accuracy in assessing cognitive health of older adults.


Fine-grain atlases of functional modes for fMRI analysis

arXiv.org Machine Learning

Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2,500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional "soft" functional atlases, to represent and analyse brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results.


Artificial Intelligence (AI) and Mental Health Care

#artificialintelligence

This post is offered as a concise overview of important advances in artificial intelligence that will soon impact the way mental health care is practiced in day to day clinical settings. The result will be more individualized treatment incorporating both conventional and evidence-based complementary and alternative medicine (CAM) modalities, more effective and more cost-effective treatments of many common mental health problems, and improved outcomes. To have practical clinical utility in medicine and mental health care, an AI system must encompass machine-learning software capable of processing very large volumes of structured data, and natural language processing (NLP) software capable of mining unstructured data such as narrative text in electronic health records and medical imaging data. To assist health-care providers with clinical decision-making, the AI system must be'trained' to a requisite level of expertise within a particular domain of medical knowledge. Following completion of training, it is vital to keep the supply of pertinent medical data current.


EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications

arXiv.org Artificial Intelligence

Brain-Computer Interface (BCI) is a powerful communication tool between users and systems, which enhances the capability of the human brain in communicating and interacting with the environment directly. Advances in neuroscience and computer science in the past decades have led to exciting developments in BCI, thereby making BCI a top interdisciplinary research area in computational neuroscience and intelligence. Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications. Many people benefit from EEG-based BCIs, which facilitate continuous monitoring of fluctuations in cognitive states under monotonous tasks in the workplace or at home. In this study, we survey the recent literature of EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensated for the gaps in the systematic summary of the past five years (2015-2019). In specific, we first review the current status of BCI and its significant obstacles. Then, we present advanced signal sensing and enhancement technologies to collect and clean EEG signals, respectively. Furthermore, we demonstrate state-of-art computational intelligence techniques, including interpretable fuzzy models, transfer learning, deep learning, and combinations, to monitor, maintain, or track human cognitive states and operating performance in prevalent applications. Finally, we deliver a couple of innovative BCI-inspired healthcare applications and discuss some future research directions in EEG-based BCIs.


Artificial Intelligence: A Need of Modern 'Intelligent' Education - Thrive Global

#artificialintelligence

Artificial intelligence is influencing the future of virtually every industry and every human being. It has acted as the main driver of emerging technologies like big data, robotics, and IoT, and it will continue to act as a technological innovator for the near future. According to tech experts, artificial intelligence (AI) has the potential to transform the world. However, those same experts do not agree on what kind of effect that transformation will have on the average person. Some believe that humans will be much better off in the hands of advanced AI systems, while others think it will lead to our inevitable downfall.


The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study

arXiv.org Machine Learning

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical data sets---collected with different scanners, protocols and disease populations---and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens' scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to data sets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment.


Artificial Intelligence (AI) and Mental Health Care

#artificialintelligence

This post is offered as a concise overview of important advances in artificial intelligence that will soon impact the way mental health care is practiced in day to day clinical settings. The result will be more individualized treatment incorporating both conventional and evidence-based complementary and alternative medicine (CAM) modalities, more effective and more cost-effective treatments of many common mental health problems, and improved outcomes. To have practical clinical utility in medicine and mental health care, an AI system must encompass machine-learning software capable of processing very large volumes of structured data, and natural language processing (NLP) software capable of mining unstructured data such as narrative text in electronic health records and medical imaging data. To assist health-care providers with clinical decision-making, the AI system must be'trained' to a requisite level of expertise within a particular domain of medical knowledge. Following completion of training, it is vital to keep the supply of pertinent medical data current.


Towards automated symptoms assessment in mental health

arXiv.org Machine Learning

Activity and motion analysis has the potential to be used as a diagnostic tool for mental disorders. However, to-date, little work has been performed in turning stratification measures of activity into useful symptom markers. The research presented in this thesis has focused on the identification of objective activity and behaviour metrics that could be useful for the analysis of mental health symptoms in the above mentioned dimensions. Particular attention is given to the analysis of objective differences between disorders, as well as identification of clinical episodes of mania and depression in bipolar patients, and deterioration in borderline personality disorder patients. A principled framework is proposed for mHealth monitoring of psychiatric patients, based on measurable changes in behaviour, represented in physical activity time series, collected via mobile and wearable devices. The framework defines methods for direct computational analysis of symptoms in disorganisation and psychomotor dimensions, as well as measures for indirect assessment of mood, using patterns of physical activity, sleep and circadian rhythms. The approach of computational behaviour analysis, proposed in this thesis, has the potential for early identification of clinical deterioration in ambulatory patients, and allows for the specification of distinct and measurable behavioural phenotypes, thus enabling better understanding and treatment of mental disorders.


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.