Hybrid meta-heuristic algorithm based parameter optimization for extreme learning machines classification

Most classification algorithms suffer from manual parameter tuning and it affects the training computational time and accuracy performance. Extreme Learning Machines (ELM) emerged as a fast training machine learning algorithm that eliminates parameter tuning by randomly assigning the input weights and biases, and analytically determining the output weights using Moore Penrose generalized inverse method. However, the randomness assignment, does not guarantee an optimal set of input weights and biases of the hidden neurons. This will lead to ELM instability and local minimum solution. ELM performance also is affected by the network structure especially the number of hidden nodes. Too many hidden neurons will increase the network structure complexity and computational time. While too few hidden neuron numbers will affect the ELM generalization ability and reduce the accuracy. In this study, a heuristic-based ELM (HELM) scheme was designed to secure an optimal ELM structure. The results of HELM were validated with five rule-based hidden neuron selection schemes. Then HELM performance was compared with Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Classification and Regression Tree (CART) to investigate its relative competitiveness. Secondly, to improve the stability of ELM, the Moth-Flame Optimization algorithm is hybridized with ELM as MFO-ELM. MFO generates moths and optimizes their positions in the search space with a logarithm spiral model to obtain the optimal values of input weights and biases. The optimal weights and biases from the search space were passed into the ELM input space. However, it did not completely solve the problem of been stuck in the local extremum since MFO could not ensure a good balance between the exploration and exploitation of the search space. Thirdly, a co-evolutionary hybrid algorithm of the Cross-Entropy Moth-Flame Optimization Extreme Learning Machines (CEMFO-ELM) scheme was proposed. The hybrid of CE and MFO metaheuristic algorithms ensured a balance of exploration and exploitation in the search space and reduced the possibility of been trapped in the local minima. The performances of these schemes were evaluated on some selected medical datasets from the University of California, Irvine (UCI) machine learning repository, and compared with standard ELM, PSO-ELM, and CSO-ELM. The hybrid MFO-ELM algorithm enhanced the selection of optimal weights and biases for ELM, therefore improved its classification accuracy in a range of 0.4914 - 6.0762%, and up to 8.9390% with the other comparative ELM optimized meta-heuristic algorithms. The convergence curves plot show that the proposed hybrid CEMFO meta-heuristic algorithm ensured a balance between the exploration and exploitation in the search space, thereby improved the stability up to 53.75%. The overall findings showed that the proposed CEMFO-ELM provided better generalization performance on the classification of medical datasets. Thus, CEMFO-ELM is a suitable tool to be used not only in solving medical classification problems but potentially be used in other real-world problems

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 física 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 indivíduo 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 características 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 possuímos. Visto que eles se encontravam desequilíbrios. 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

Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen

Predicting the future:Clinical outcome prediction with machine learning in neuropsychiatry

Treatment of psychiatric disorders relies on subjective measures of symptoms to establish diagnoses and lacks an objective way to determine which treatments might work best for an individual patient. To improve the current state-of-the-art and to be able to help a growing number of patients with mental health disorders more efficiently, the discovery of biomarkers predictive of treatment outcome and prognosis is needed. In addition, the application of machine learning methods provides an improvement over the standard group-level analysis approach since it allows for individualized predictions. Machine learning models can also be tested for their generalization capabilities to new patients which would quantify their potential for clinical applicability. In this thesis, these approaches were combined and investigated across a set of different neuropsychiatric disorders. The investigated applications included the prediction of disease course in patients with anxiety disorders, early detection of behavioural frontotemporal dementia in at-risk individuals using structural magnetic resonance imaging (MRI), prediction of deep-brain stimulation treatment-outcome in patients with therapy-resistant obsessive compulsive disorder using structural MRI and prediction of treatment-response for adult and youth patients with posttraumatic stress disorder using resting-state functional MRI scans. Across all studies this thesis showed that machine learning methods combined with neuroimaging data can be utilized to identify biomarkers predictive of future clinical outcomes in neuropsychiatric disorders. Promising as it seems, this can only be the first step for the inclusion of these new approaches into clinical practice as further studies utilizing larger sample sizes are necessary to validate the discovered biomarkers

Predicting Psychopathological Onset: Early Signs of Neuropsychiatric Diseases

The aim of this Special Issue is to collect valuable contributions from scientists worldwide working on the role that biological, behavioral, and cognitive markers can have in predicting the onset of neuropsychiatric disorders. We were able to collect 13 original articles and two reviews on this topic. The results published in this Special Issue could provide significant support in pre-clinical phases for the identification of vulnerability factors, to better understand the course of the illness, and to predict its outcome, as well as aiding clinicians in the therapeutic decision-making process

Early Prediction Of Late-Life Depression Remission: Multi-Factor Kernel-Based Machine Learning Utilizing Single Dose Pharmacological Functional Magnetic Resonance Imaging

Treatment of major depressive disorder (MDD) currently relies on a prolonged trial and error process to identify the best pharmacological regimen. This process is further prolonged in older adults with major depressive disorder (Late-Life Depression or LLD), where it is associated with a host of negative outcomes, including suicide, worsening medical comorbidity, and poor quality of life. Functional magnetic resonance imaging (fMRI) brain changes have been associated with depression severity and treatment outcomes. Previous studies have shown that recovery from depression can be predicted using both pre-treatment neuroimaging as well as follow-up scans from the early treatment period. Pharmacological functional magnetic resonance imaging (phMRI) is an approach that utilizes multiple fMRI scans to investigate changes in functional neuroimaging following acute doses of pharmacotherapy. It has been demonstrated that antidepressants have a fast uptake period, effecting resting state networks as well as functional brain activation after only a single dose. We aimed to evaluate the efficacy of phMRI to identify these very early (single dose) functional changes, and use these to predict remission. Data was collected from an open-label pharmacologic treatment study of LLD (N=51). Multi-modal MRI, including phMRI, were acquired at 5 time-points. Results showed accurate prediction of depression remission from pre-treatment, as well as phMRI after only a single dose of pharmacotherapy. The trajectory of the neuroimaging changes across the treatment trial suggest an initial engagement of large scale resting networks, followed by engagement of implicit emotion control networks, and later changes in explicit emotion regulation. Utilizing kernel-based (multi-factor principal components) machine learning, we found that leveraging both pharmacological neuroimaging and clinical data improved prediction efficacy of remission. In this body of work, we have integrated multiple imaging modalities to explain the long delay in clinical response to antidepressants, and to identify early markers of response

Data-driven approaches for predicting asthma attacks in adults in primary care

Background Asthma attacks cause approximately 270 hospitalisations and four deaths per day in the United Kingdom (UK). Previous attempts to construct data-driven risk prediction models of asthma attacks have lacked clinical utility: either producing inaccurate predictions or requiring patient data which are not cost-effective to collect on a large scale (such as electronic monitoring device data). Electronic Health Record (EHR) use throughout the UK enables researchers to harness comprehensive and panoramic patient data, but their cleaning and pre-processing requires sophisticated empirical experimentation and data analytics approaches. My objectives were to appraise the previously utilised methods in asthma attack risk prediction modelling for feature extraction, model development, and model selection, and to train and test a model in Scottish EHRs. Methods In this thesis, I used a Scottish longitudinal primary care EHR dataset with linked secondary care records, to investigate the optimisation of an asthma attack risk prediction model. To inform the model, I refined methods for estimation of asthma medication adherence from EHRs, compared model training data enrichment procedures, and evaluated measures for validating model performance. After conducting a critical appraisal of the methods employed in the literature, I trained and tested four statistical learning algorithms for prediction in the next four weeks, i.e. logistic regression, naïve Bayes classification, random forests, and extreme gradient boosting, and validated model performance in an unseen hold-out dataset. Training data enrichment methods were compared across all algorithms to establish whether the sensitivity of estimating relatively uncommon event incidence, such as asthma attacks in the general asthma population, could be improved. Secondary event horizons were also examined, such as prediction in the next six months. Empirical experimentation established the balanced accuracy to be the most appropriate prediction model performance measure, and the calibration between estimated and observed risk was additionally assessed using the Area Under the Receiver-Operator Curve (AUC). Results Data were available for over 670,000 individuals, followed for up to 17 years (177,306 person-years in total). Binary prediction of asthma attacks in the following four-week period resulted in 1,203,476 data samples, of which 1% contained one or more attacks (12,193 total attacks). In the preliminary model selection phase, the random forest algorithm provided the best balance between accuracy in those with asthma attacks (sensitivity) and in those predicted to have attacks (positive predictive value) in the following four weeks. In an unseen data partition, the final random forest model, with optimised hyper-parameters, achieved an AUC of 0.91, and a balanced accuracy of 73.6% after the application of an optimised decision threshold. Accurate predictions were made for a median of 99.6% of those who did not go on to have attacks (specificity). As expected with rare event predictions, the sensitivity was lower at 47.7%, but this was well balanced with the positive predictive value of 48.9%. Furthermore, several of the secondary models, including predicting asthma attacks in the following 12 weeks, achieved state-of-the-art performance and still had high potential clinical utility. Conclusions I successfully developed an EHR-based model for predicting asthma attacks in the next four weeks. Accurately predicting asthma attacks occurrence may facilitate closer monitoring to ensure that preventative therapy is adequately managing symptoms, reinforce the need to keep abreast of triggers, and allow rescue treatments to be administered quickly when necessary

Treatment Selection: Understanding What Works For Whom In Mental Health

Individuals seeking treatment for mental health problems often have to choose between several different treatment options. For disorders like depression and PTSD, many of the available treatments have been found to be, on average, equally effective. Research on precision medicine aims to identify the most effective treatment for each patient. This work is based on the idea that individuals respond differently to treatment, and that these differences can be studied and characterized. The push for personalized and precision approaches in mental health involves identifying moderators - variables that predict differential response into treatment recommendations. Unfortunately, there has been little real-world application of these findings, in part due to the lack of systems suited to translating the information in actionable recommendations. This dissertation will review the history of treatment selection in mental health, and will present specific examples of treatment selection models in depression and PTSD. Differences between treatment selection in the context of two equivalently effective interventions and stratified medicine applications in which goal is to optimize the allocation of stronger and weaker interventions will be discussed. Methodological challenges in building (e.g., variable selection) and evaluating (e.g., cross-validation) treatment selection systems will be explored. Approaches to precision medicine being used by different groups will be compared. Finally, recommendations for future directions will be made

Individualised, interpretable and reproducible computer-aided diagnosis of dementia: towards application in clinical practice

Neuroimaging offers an unmatched description of the brain’s structure and physiology, but the information it provides is not easy to extract and interpret. A popular way to extract meaningful information from brain images is to use computational methods based on machine learning and deep learning to predict the current or future diagnosis of a patient. A large number of these approaches have been dedicated to the computer-aided diagnosis of dementia, and more specifically of Alzheimer's disease. However, only a few are translated to the clinic. This can be explained by different factors such as the lack of rigorous validation of these approaches leading to over-optimistic performance and their lack of reproducibility, but also the limited interpretability of these methods and their limited generalisability when moving from highly controlled research data to routine clinical data. This manuscript describes how we tried to address these limitations.We have proposed reproducible frameworks for the evaluation of Alzheimer's disease classification methods and developed two open-source software platforms for clinical neuroimaging studies (Clinica) and neuroimaging processing with deep learning (ClinicaDL). We have implemented and assessed the robustness of a visualisation method aiming to interpret convolutional neural networks and used it to study the stability of the network training. We concluded that, currently, combining a convolutional neural networks classifier with an interpretability method may not constitute a robust tool for individual computer-aided diagnosis. As an alternative, we have proposed an approach that detects anomalies in the brain by generating what would be the healthy version of a patient's image and comparing this healthy version with the real image. Finally, we have studied the performance of machine and deep learning algorithms for the computer-aided diagnosis of dementia from images acquired in clinical routine.La neuro-imagerie offre une description inégalée de la structure et de la physiologie du cerveau, mais les informations qu'elle fournit ne sont pas faciles à extraire et à interpréter. Une façon populaire d'extraire des informations pertinentes d'images cérébrales consiste à utiliser des méthodes basées sur l'apprentissage statistique et l'apprentissage profond pour prédire le diagnostic actuel ou futur d'un patient. Un grand nombre de ces approches ont été dédiées au diagnostic assisté par ordinateur de la démence, et plus spécifiquement de la maladie d'Alzheimer. Cependant, seules quelques-unes sont transposées en clinique. Cela peut s'expliquer par différents facteurs tels que l'absence de validation rigoureuse de ces approches conduisant à des performances trop optimistes et à leur manque de reproductibilité, mais aussi l'interprétabilité limitée de ces méthodes et leur généralisation limitée lors du passage de données de recherche hautement contrôlées à des données cliniques de routine. Ce manuscrit décrit comment nous avons tenté de remédier à ces limites.Nous avons proposé des cadres reproductibles pour l'évaluation des méthodes de classification de la maladie d'Alzheimer et développé deux plateformes logicielles open-source pour les études de neuroimagerie clinique (Clinica) et le traitement de la neuroimagerie par apprentissage profond (ClinicaDL). Nous avons implémenté et évalué la robustesse d'une méthode de visualisation visant à interpréter les réseaux neuronaux convolutifs et l'avons utilisée pour étudier la stabilité de l'entraînement du réseau. Nous avons conclu qu'actuellement, la combinaison de réseaux neuronaux convolutifs avec une méthode d'interprétabilité peut ne pas constituer un outil robuste pour le diagnostic individuel assisté par ordinateur. De façon alternative, nous avons proposé une approche qui détecte les anomalies dans le cerveau en générant ce qui serait la version saine de l'image d'un patient et en comparant cette version saine avec l'image réelle. Enfin, nous avons étudié les performances des algorithmes d'apprentissage statistique et profond pour le diagnostic assisté par ordinateur de la démence à partir d'images acquises en routine clinique

From Photography to fMRI

Hysteria, a mysterious disease known since antiquity, is said to have ceased to exist. Challenging this commonly held view, this is the first cross-disciplinary study to examine the current functional neuroimaging research into hysteria and compare it to the nineteenth-century image-based research into the same disorder. Paula Muhr's central argument is that, both in the nineteenth-century and the current neurobiological research on hysteria, images have enabled researchers to generate new medical insights. Through detailed case studies, Muhr traces how different images, from photography to functional brain scans, have reshaped the historically situated medical understanding of this disorder that defies the mind-body dualism