scholarly journals Correction: Designing machine learning workflows with an application to topological data analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. e0229821
Author(s):  
Eric Cawi ◽  
Patricio S La Rosa ◽  
Arye Nehorai
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Topological Data Analysis ◽  
Topological Data

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

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.

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

Mathematics ◽  
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%.

scholarly journals Chatter Classification in Turning using Machine Learning and Topological Data Analysis

2018 ◽  
Vol 51 (14) ◽  
pp. 195-200 ◽  
Author(s):  
Firas A. Khasawneh ◽  
Elizabeth Munch ◽  
Jose A. Perea
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Topological Data Analysis ◽  
Topological Data

scholarly journals Designing machine learning workflows with an application to topological data analysis

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0225577 ◽  
Author(s):  
Eric Cawi ◽  
Patricio S. La Rosa ◽  
Arye Nehorai
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Topological Data Analysis ◽  
Topological Data

scholarly journals Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lorenzo Falsetti ◽  
Matteo Rucco ◽  
Marco Proietti ◽  
Giovanna Viticchi ◽  
Vincenzo Zaccone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Atrial Fibrillation ◽  
Data Analysis ◽  
Critically Ill ◽  
Major Bleeding ◽  
Critically Ill Patients ◽  
Therapeutic Failure ◽  
Topological Data Analysis ◽  
Topological Data

AbstractCritically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed to identify, adopting topological data analysis, the risk factors for therapeutic failure (in-hospital death or intensive care unit transfer), the in-hospital occurrence of stroke/TIA and major bleeding in a cohort of critically ill patients with pre-existing atrial fibrillation admitted to a stepdown unit; to engineer newer prediction models based on machine learning in the same cohort. We selected all medical patients admitted for critical illness and a history of pre-existing atrial fibrillation in the timeframe 01/01/2002–03/08/2007. All data regarding patients’ medical history, comorbidities, drugs adopted, vital parameters and outcomes (therapeutic failure, stroke/TIA and major bleeding) were acquired from electronic medical records. Risk factors for each outcome were analyzed adopting topological data analysis. Machine learning was used to generate three different predictive models. We were able to identify specific risk factors and to engineer dedicated clinical prediction models for therapeutic failure (AUC: 0.974, 95%CI: 0.934–0.975), stroke/TIA (AUC: 0.931, 95%CI: 0.896–0.940; Brier score: 0.13) and major bleeding (AUC: 0.930:0.911–0.939; Brier score: 0.09) in critically-ill patients, which were able to predict accurately their respective clinical outcomes. Topological data analysis and machine learning techniques represent a concrete viewpoint for the physician to predict the risk at the patients’ level, aiding the selection of the best therapeutic strategy in critically ill patients affected by pre-existing atrial fibrillation.

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