Machine learning approaches in Covid-19 severity risk prediction in Morocco

AbstractThe purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

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Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps

Scientific Reports ◽

10.1038/s41598-021-90070-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Scott Broderick ◽

Ruhil Dongol ◽

Tianmu Zhang ◽

Krishna Rajan

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Crystal Chemistry ◽

Persistent Homology ◽

Hierarchical Classification ◽

Topological Data Analysis ◽

Learning Tool ◽

Coordination Polyhedra ◽

Machine Learning Tool ◽

Topological Data

AbstractThis paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds.

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Using machine learning approaches for multi-omics data analysis: A review

Biotechnology Advances ◽

10.1016/j.biotechadv.2021.107739 ◽

2021 ◽

Vol 49 ◽

pp. 107739

Author(s):

Parminder S. Reel ◽

Smarti Reel ◽

Ewan Pearson ◽

Emanuele Trucco ◽

Emily Jefferson

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Omics Data ◽

Learning Approaches ◽

Omics Data Analysis

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On the Application of Topological Data Analysis and Machine Learning to Flood Incidents, and Decision Making

SSRN Electronic Journal ◽

10.2139/ssrn.3981505 ◽

2021 ◽

Author(s):

Felix Obi Ohanuba ◽

Mohd Tahir Ismail ◽

Majid Khan Ali

Keyword(s):

Machine Learning ◽

Decision Making ◽

Data Analysis ◽

Topological Data Analysis ◽

Topological Data

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Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

Nature Communications ◽

10.1038/s41467-019-12397-x ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 3

Author(s):

Primož Godec ◽

Matjaž Pančur ◽

Nejc Ilenič ◽

Andrej Čopar ◽

Martin Stražar ◽

...

Keyword(s):

Machine Learning ◽

Image Analysis ◽

Deep Learning ◽

Data Analysis ◽

Protein Localization ◽

Visual Programming ◽

Developmental Competence ◽

Image Clustering ◽

Small Scale ◽

Learning Approaches

Abstract Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

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Personalized prognostic prediction of treatment outcome for depressed patients in a naturalistic psychiatric hospital setting: A comparison of machine learning approaches.

Journal of Consulting and Clinical Psychology ◽

10.1037/ccp0000451 ◽

2020 ◽

Vol 88 (1) ◽

pp. 25-38 ◽

Cited By ~ 5

Author(s):

Christian A. Webb ◽

Zachary D. Cohen ◽

Courtney Beard ◽

Marie Forgeard ◽

Andrew D. Peckham ◽

...

Keyword(s):

Machine Learning ◽

Treatment Outcome ◽

Psychiatric Hospital ◽

Hospital Setting ◽

Learning Approaches ◽

Depressed Patients ◽

Prognostic Prediction

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Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

10.1101/053033 ◽

2016 ◽

Cited By ~ 12

Author(s):

Michael P. Pound ◽

Alexandra J. Burgess ◽

Michael H. Wilson ◽

Jonathan A. Atkinson ◽

Marcus Griffiths ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Analysis ◽

Paradigm Shift ◽

State Of The Art ◽

Plant Phenotyping ◽

Learning Approaches ◽

Challenging Problem ◽

Feature Identification ◽

Art Performance

AbstractDeep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.

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Persistence Bag-of-Words for Topological Data Analysis

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/624 ◽

2019 ◽

Cited By ~ 1

Author(s):

Bartosz Zieliński ◽

Michał Lipiński ◽

Mateusz Juda ◽

Matthias Zeppelzauer ◽

Paweł Dłotko

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Mathematical Theory ◽

State Of The Art ◽

Persistent Homology ◽

Complex Structure ◽

Topological Data Analysis ◽

Bag Of Words ◽

Seamless Integration ◽

Alternative Approaches

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.

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Towards Personalized Diagnosis of Glioblastoma in Fluid-Attenuated Inversion Recovery (FLAIR) by Topological Interpretable Machine Learning

Mathematics ◽

10.3390/math8050770 ◽

2020 ◽

Vol 8 (5) ◽

pp. 770

Author(s):

Matteo Rucco ◽

Giovanna Viticchi ◽

Lorenzo Falsetti

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Tumor Growth ◽

Topological Data Analysis ◽

Textural Features ◽

The Third ◽

Interpretable Machine Learning ◽

Fluid Attenuated Inversion Recovery ◽

Topological Data

Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumor, which tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumors. Usually, GBMs are detected by magnetic resonance images (MRI). Among MRI, a fluid-attenuated inversion recovery (FLAIR) sequence produces high quality digital tumor representation. Fast computer-aided detection and segmentation techniques are needed for overcoming subjective medical doctors (MDs) judgment. This study has three main novelties for demonstrating the role of topological features as new set of radiomics features which can be used as pillars of a personalized diagnostic systems of GBM analysis from FLAIR. For the first time topological data analysis is used for analyzing GBM from three complementary perspectives—tumor growth at cell level, temporal evolution of GBM in follow-up period and eventually GBM detection. The second novelty is represented by the definition of a new Shannon-like topological entropy, the so-called Generator Entropy. The third novelty is the combination of topological and textural features for training automatic interpretable machine learning. These novelties are demonstrated by three numerical experiments. Topological Data Analysis of a simplified 2D tumor growth mathematical model had allowed to understand the bio-chemical conditions that facilitate tumor growth—the higher the concentration of chemical nutrients the more virulent the process. Topological data analysis was used for evaluating GBM temporal progression on FLAIR recorded within 90 days following treatment completion and at progression. The experiment had confirmed that persistent entropy is a viable statistics for monitoring GBM evolution during the follow-up period. In the third experiment we developed a novel methodology based on topological and textural features and automatic interpretable machine learning for automatic GBM classification on FLAIR. The algorithm reached a classification accuracy up to 97%.

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