1,483 research outputs found
Feature Space Augmentation: Improving Prediction Accuracy of Classical Problems in Cognitive Science and Computer Vison
The prediction accuracy in many classical problems across multiple domains has seen a rise since computational tools such as multi-layer neural nets and complex machine learning algorithms have become widely accessible to the research community. In this research, we take a step back and examine the feature space in two problems from very different domains. We show that novel augmentation to the feature space yields higher performance. Emotion Recognition in Adults from a Control Group: The objective is to quantify the emotional state of an individual at any time using data collected by wearable sensors. We define emotional state as a mixture of amusement, anger, disgust, fear, sadness, anxiety and neutral and their respective levels at any time. The generated model predicts an individual’s dominant state and generates an emotional spectrum, 1x7 vector indicating levels of each emotional state and anxiety. We present an iterative learning framework that alters the feature space uniquely to an individual’s emotion perception, and predicts the emotional state using the individual specific feature space. Hybrid Feature Space for Image Classification: The objective is to improve the accuracy of existing image recognition by leveraging text features from the images. As humans, we perceive objects using colors, dimensions, geometry and any textual information we can gather. Current image recognition algorithms rely exclusively on the first 3 and do not use the textual information. This study develops and tests an approach that trains a classifier on a hybrid text based feature space that has comparable accuracy to the state of the art CNN’s while being significantly inexpensive computationally. Moreover, when combined with CNN’S the approach yields a statistically significant boost in accuracy. Both models are validated using cross validation and holdout validation, and are evaluated against the state of the art
Knowledge Base for MENTAL AI, in Data Science Context
Globally, 1 in 7 people has some kind of mental or substance use disorder that affects their
thinking, feelings, and behaviour in everyday life. Mental well-being is vital for physical
health. No Health Without Mental Health! People with mental health disorders can carry on
with normal life if they get the proper treatment and support. Mental disorders are complex
to diagnose due to similar and common symptoms for numerous types of mental illnesses,
with a minute difference among them.
In the era of big, the challenge stays to make sense of the huge amount of health research and
care data. Computational methods hold significant potential to enable superior patient stratification approaches to the established clinical practice, which in turn are a pre-requirement
for the development of effective personalized medicine approaches. Personalized psychiatry
also plays a vital role in predicting mental disorders and improving diagnosis and optimized
treatment. The use of intelligent systems is expected to grow in the medical field, and it will
continue to pose abundant opportunities for solutions that can help save patients’ lives. As
it does for many industries, Artificial Intelligence (AI) systems can support mental health
specialists in their jobs.
Machine learning algorithms can be applied to find different patterns in the most diverse
sets of data. This work aims to examine and compare different machine learning classification methodologies to predict different mental disorders and, from that, extract knowledge
that can help mental health professionals in their tasks. Our algorithms were trained using
a total dataset of 3353 patients from different hospital units. These data are divided into
three subsets of data, mainly by the characteristics that the pathologies present. We evaluate the performance of the algorithms using different metrics. Among the metrics applied,
we chose the F1 score to compare and analyze the algorithms, as it is the most suitable for
the data we have since they found themselves imbalances. In the first evaluation, we trained
our models, using all the patient’s symptoms and diagnoses. In the second evaluation, we
trained our models, using only the symptoms that were somehow related to each other and
that influenced the other pathologies.Milhões de pessoas em todo o mundo são afetadas por transtornos mentais que influenciam o
seu pensamento, sentimento ou comportamento. A saúde mental é um pré-requisito essencial para a saúde fÃsica e geral. Pessoas com transtornos mentais geralmente precisam de
tratamento e apoio adequados para levar uma vida normal. A saúde mental é uma condição
de bem-estar em que um indivÃduo reconhece as suas habilidades, pode lidar com as tensões
quotidianas da vida, trabalhar de forma produtiva e pode contribuir para a sua comunidade.
A saúde mental afeta a vida das pessoas com transtorno mental, as suas profissões e a produtividade da comunidade.
Boa saúde mental e resiliência são essenciais para a nossa saúde biológica, conexões humanas, educação, trabalho e alcançar o nosso potencial. A pandemia do covid-19 impactou
significativamente a saúde mental das pessoas, em particular grupos como saúde e outros
trabalhadores da linha de frente, estudantes, pessoas que moram sozinhas e pessoas com
condições de saúde mental pré-existentes. Além disso, os serviços para transtornos mentais,
neurológicos e por uso de substâncias foram significativamente interrompidos. Os transtornos
mentais são classificados como de diagnóstico complexo devido à semelhança dos sintomas.
Consultas regulares de saúde de pessoas com transtornos mentais graves podem impedir
a morte prematura. A dificuldade dos especialistas em diagnosticar é geralmente causada
pela semelhança dos sintomas nos transtornos mentais, como por exemplo, transtorno de
bordeline e bipolar.
Os algoritmos de aprendizado de máquina podem ser aplicados para encontrar diferentes
padrões nos mais diversos conjuntos de dados. Este trabalho, visa examinar e comparar
diferentes metodologias de classificação de aprendizado de máquina para prever difentes
transtornos mentais e disso, extrair conhecimento que possam auxiliar os profissionais da
area de saude mental, nas suas tarefas. Os nossos algoritmos, foram treinados utilizando um
conjunto total de dados de 3353 pacientes, provenientes de diferentes unidades hospitalares.
Esses dados, estão repartidos em três subconjuntos de dados, principalmente, pelas caracterÃsticas que as patologias apresentam. Avaliamos o desempenho dos algoritmos usando
diferentes métricas. Dentre as métricas aplicadas, escolhemos o F1 score para comparar e
analisar os algoritmos, pois é o mais adequado para os dados que possuÃmos. Visto que eles
se encontravam desequilÃbrios. Na primeira avaliação, treinamos os nossos modelos, utilizando todos os sintomas e diagnósticos dos pacientes. Na segunda avaliação, treinamos os
nossos modelos, utilizando apenas os sintomas que apresentavam alguma relação entre si e
que influenciavam nas outras patologias
Contributions to the study of Austism Spectrum Brain conectivity
164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines
Connecting Phenotype To Genotype: PheWAS-inspired Analysis Of Autism Spectrum Disorder
Autism Spectrum Disorder (ASD) is extremely heterogeneous clinically and genetically. There is a pressing need for a better understanding of the heterogeneity of ASD based on scientifically rigorous approaches centered on systematic evaluation of the clinical and research utility of both phenotype and genotype markers. This paper presents a holistic PheWAS-inspired method to identify meaningful associations between ASD phenotypes and genotypes. We generate two types of phenotype-phenotype (p-p) graphs: a direct graph that utilizes only phenotype data, and an indirect graph that incorporates genotype as well as phenotype data. We introduce a novel methodology for fusing the direct and indirect p-p networks in which the genotype data is incorporated into the phenotype data in varying degrees. The hypothesis is that the heterogeneity of ASD can be distinguished by clustering the p-p graph. The obtained graphs are clustered using network-oriented clustering techniques, and results are evaluated. The most promising clusterings are subsequently analyzed for biological and domain-based relevance. Clusters obtained delineated different aspects of ASD, including differentiating ASD-specific symptoms, cognitive, adaptive, language and communication functions, and behavioral problems. Some of the important genes associated with the clusters have previous known associations to ASD. We found that clusters based on integrated genetic and phenotype data were more effective at identifying relevant genes than clusters constructed from phenotype information alone. These genes included five with suggestive evidence of ASD association and one known to be a strong candidate
Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks
Neural network approaches are machine learning methods that are widely used
in various domains, such as healthcare and cybersecurity. Neural networks are
especially renowned for their ability to deal with image datasets. During the
training process with images, various fundamental mathematical operations are
performed in the neural network. These operations include several algebraic and
mathematical functions, such as derivatives, convolutions, and matrix
inversions and transpositions. Such operations demand higher processing power
than what is typically required for regular computer usage. Since CPUs are
built with serial processing, they are not appropriate for handling large image
datasets. On the other hand, GPUs have parallel processing capabilities and can
provide higher speed. This paper utilizes advanced neural network techniques,
such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST
VGG16, and our proposed models, to compare CPU and GPU resources. We
implemented a system for classifying Autism disease using face images of
autistic and non-autistic children to compare performance during testing. We
used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and
Execution time. It was observed that GPU outperformed CPU in all tests
conducted. Moreover, the performance of the neural network models in terms of
accuracy increased on GPU compared to CPU
Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD
Using consumer feedback from location-based services in PoI recommender systems for people with autism
When suggesting Points of Interest (PoIs) to people with autism spectrum
disorders, we must take into account that they have idiosyncratic sensory
aversions to noise, brightness and other features that influence the way they
perceive places. Therefore, recommender systems must deal with these aspects.
However, the retrieval of sensory data about PoIs is a real challenge because
most geographical information servers fail to provide this data. Moreover,
ad-hoc crowdsourcing campaigns do not guarantee to cover large geographical
areas and lack sustainability. Thus, we investigate the extraction of sensory
data about places from the consumer feedback collected by location-based
services, on which people spontaneously post reviews from all over the world.
Specifically, we propose a model for the extraction of sensory data from the
reviews about PoIs, and its integration in recommender systems to predict item
ratings by considering both user preferences and compatibility information. We
tested our approach with autistic and neurotypical people by integrating it
into diverse recommendation algorithms. For the test, we used a dataset built
in a crowdsourcing campaign and another one extracted from TripAdvisor reviews.
The results show that the algorithms obtain the highest accuracy and ranking
capability when using TripAdvisor data. Moreover, by jointly using these two
datasets, the algorithms further improve their performance. These results
encourage the use of consumer feedback as a reliable source of information
about places in the development of inclusive recommender systems
Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment
abstract: Parents fulfill a pivotal role in early childhood development of social and communication
skills. In children with autism, the development of these skills can be delayed. Applied
behavioral analysis (ABA) techniques have been created to aid in skill acquisition.
Among these, pivotal response treatment (PRT) has been empirically shown to foster
improvements. Research into PRT implementation has also shown that parents can be
trained to be effective interventionists for their children. The current difficulty in PRT
training is how to disseminate training to parents who need it, and how to support and
motivate practitioners after training.
Evaluation of the parents’ fidelity to implementation is often undertaken using video
probes that depict the dyadic interaction occurring between the parent and the child during
PRT sessions. These videos are time consuming for clinicians to process, and often result
in only minimal feedback for the parents. Current trends in technology could be utilized to
alleviate the manual cost of extracting data from the videos, affording greater
opportunities for providing clinician created feedback as well as automated assessments.
The naturalistic context of the video probes along with the dependence on ubiquitous
recording devices creates a difficult scenario for classification tasks. The domain of the
PRT video probes can be expected to have high levels of both aleatory and epistemic
uncertainty. Addressing these challenges requires examination of the multimodal data
along with implementation and evaluation of classification algorithms. This is explored
through the use of a new dataset of PRT videos.
The relationship between the parent and the clinician is important. The clinician can
provide support and help build self-efficacy in addition to providing knowledge and
modeling of treatment procedures. Facilitating this relationship along with automated
feedback not only provides the opportunity to present expert feedback to the parent, but
also allows the clinician to aid in personalizing the classification models. By utilizing a
human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the
classification models by providing additional labeled samples. This will allow the system
to improve classification and provides a person-centered approach to extracting
multimodal data from PRT video probes.Dissertation/ThesisDoctoral Dissertation Computer Science 201
Modern Views of Machine Learning for Precision Psychiatry
In light of the NIMH's Research Domain Criteria (RDoC), the advent of
functional neuroimaging, novel technologies and methods provide new
opportunities to develop precise and personalized prognosis and diagnosis of
mental disorders. Machine learning (ML) and artificial intelligence (AI)
technologies are playing an increasingly critical role in the new era of
precision psychiatry. Combining ML/AI with neuromodulation technologies can
potentially provide explainable solutions in clinical practice and effective
therapeutic treatment. Advanced wearable and mobile technologies also call for
the new role of ML/AI for digital phenotyping in mobile mental health. In this
review, we provide a comprehensive review of the ML methodologies and
applications by combining neuroimaging, neuromodulation, and advanced mobile
technologies in psychiatry practice. Additionally, we review the role of ML in
molecular phenotyping and cross-species biomarker identification in precision
psychiatry. We further discuss explainable AI (XAI) and causality testing in a
closed-human-in-the-loop manner, and highlight the ML potential in multimedia
information extraction and multimodal data fusion. Finally, we discuss
conceptual and practical challenges in precision psychiatry and highlight ML
opportunities in future research
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