37 research outputs found

    Machine Learning for Alzheimer’s Disease and Related Dementias

    Get PDF
    Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing

    Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics

    Full text link
    A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. Our neuroimaging-genetic pipeline is comprised of image processing, neuroimaging feature extraction and genetic association steps. We propose a neural network classifier for extracting neuroimaging features that are related with disease and a multivariate Bayesian group sparse regression model for genetic association. We compare the predictive power of these features to expert selected features and take a closer look at the SNPs identified with the new neuroimaging features.Comment: Under revie

    Systems Analytics and Integration of Big Omics Data

    Get PDF
    A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

    Machine learning approaches to model cardiac shape in large-scale imaging studies

    Get PDF
    Recent improvements in non-invasive imaging, together with the introduction of fully-automated segmentation algorithms and big data analytics, has paved the way for large-scale population-based imaging studies. These studies promise to increase our understanding of a large number of medical conditions, including cardiovascular diseases. However, analysis of cardiac shape in such studies is often limited to simple morphometric indices, ignoring large part of the information available in medical images. Discovery of new biomarkers by machine learning has recently gained traction, but often lacks interpretability. The research presented in this thesis aimed at developing novel explainable machine learning and computational methods capable of better summarizing shape variability, to better inform association and predictive clinical models in large-scale imaging studies. A powerful and flexible framework to model the relationship between three-dimensional (3D) cardiac atlases, encoding multiple phenotypic traits, and genetic variables is first presented. The proposed approach enables the detection of regional phenotype-genotype associations that would be otherwise neglected by conventional association analysis. Three learning-based systems based on deep generative models are then proposed. In the first model, I propose a classifier of cardiac shapes which exploits task-specific generative shape features, and it is designed to enable the visualisation of the anatomical effect these features encode in 3D, making the classification task transparent. The second approach models a database of anatomical shapes via a hierarchy of conditional latent variables and it is capable of detecting, quantifying and visualising onto a template shape the most discriminative anatomical features that characterize distinct clinical conditions. Finally, a preliminary analysis of a deep learning system capable of reconstructing 3D high-resolution cardiac segmentations from a sparse set of 2D views segmentations is reported. This thesis demonstrates that machine learning approaches can facilitate high-throughput analysis of normal and pathological anatomy and of its determinants without losing clinical interpretability.Open Acces

    Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications

    Get PDF
    abstract: Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnesses the target information available in the limited labeled data, as well as the information in the abundant unlabeled data to build strong predictive models. However, not all the included information is useful. For example, some features may correspond to noise and including them will hurt the predictive model performance. Additionally, some instances may not be as relevant to model building and their inclusion will increase training time and potentially hurt the model performance. The objective of this research is to develop novel SSL models to balance data inclusivity and usability. My dissertation research focuses on applications of SSL in healthcare, driven by problems in brain cancer radiomics, migraine imaging, and Parkinson’s Disease telemonitoring. The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain. The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models. The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201
    corecore