scholarly journals PreMevE: A Machine-Learning Based Predictive Model for MeV Electrons inside Earth’s Outer Radiation Belt

2021 ◽  
Author(s):  
Yue Chen ◽  
Rafael Pires de Lima ◽  
Saurabh Sinha ◽  
Youzuo Lin
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Radiation Belt ◽  
Machine Learning Algorithms ◽  
Dose Effect ◽  
Radiation Environment ◽  
Outer Radiation Belt ◽  
Wind Speeds ◽  
Whole Space ◽  
Dielectric Charge

<p>The presence of megaelectron-volt (MeV) electrons in the Earth’s outer radiation belt poses a hazardous radiation environment for spaceborne electronics through the total ionization dose effect and deep dielectric charge/discharge phenomenon. Thus, developing a reliable forecasting model for MeV electron events has long been a critical but challenging task for space community. Here we update our recent progresses on the PREdictive model for MEV Electrons (PreMevE). This model exploits the power of machine learning algorithms, takes advantage of the coherence caused by local wave‐electron resonance, and uses electron observations from NOAA POES satellites in low‐Earth orbits as inputs—along with the upstream solar wind speeds and densities and GEO measurements—to provide high‐fidelity 1- and 2-day predictions of 1 MeV, 2 MeV and > 2 MeV electron flux distributions across the whole outer radiation belt. Using near-equatorial long-term electron data from the NASA Van Allen Probes mission, we trained, validated and demonstrated that the PreMevE model has L-shell averaged performance efficiencies of ~0.6 for out-of-sample 1-day forecasts and ~0.5 for 2-day forecasts. This study adds new science significance to an existing LEO and GEO space infrastructure, provides reliable and powerful tools to the whole space community, and also suggests for the development of more future tailored space weather models driven by similar methodologies.</p>

scholarly journals Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

2020 ◽  
Author(s):  
Artem Smirnov ◽  
Max Berrendorf ◽  
Yuri Shprits ◽  
Elena A. Kronberg ◽  
Hayley J Allison ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiation Belt ◽  
Electron Flux ◽  
Learning Model ◽  
Energy Electron ◽  
Medium Energy ◽  
Outer Radiation Belt ◽  
Machine Learning Model

Integrated Bioinformatics and Machine Learning Algorithms Analyses Highlight Related Pathways and Genes Associated with Alzheimer's Disease

2021 ◽  
Vol 17 ◽  
Author(s):  
Hui Zhang ◽  
Qidong Liu ◽  
Xiaoru Sun ◽  
Yaru Xu ◽  
Yiling Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Analysis ◽  
Predictive Model ◽  
Differentially Expressed Genes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Differentially Expressed ◽  
Diagnosis And Treatment ◽  
Ppi Network ◽  
Key Genes

Background: The pathophysiology of Alzheimer's disease (AD) is still not fully studied. Objective: This study aimed to explore the differently expressed key genes in AD and build a predictive model of diagnosis and treatment. Methods: Gene expression data of the entorhinal cortex of AD, asymptomatic AD, and control samples from the GEO database were analyzed to explore the relevant pathways and key genes in the progression of AD. Differentially expressed genes between AD and the other two groups in the module were selected to identify biological mechanisms in AD through KEGG and PPI network analysis in Metascape. Furthermore, genes with a high connectivity degree by PPI network analysis were selected to build a predictive model using different machine learning algorithms. Besides, model performance was tested with five-fold cross-validation to select the best fitting model. Results: A total of 20 co-expression gene clusters were identified after the network was constructed. Module 1 (in black) and module 2 (in royal blue) were most positively and negatively correlated with AD, respectively. Total 565 genes in module 1 and 215 genes in module 2, respectively, overlapped in two differentially expressed genes lists. They were enriched in the G protein-coupled receptor signaling pathway, immune-related processes, and so on. 11 genes were screened by using lasso logistic regression, and they were considered to play an important role in predicting AD samples. The model built by the support vector machine algorithm with 11 genes showed the best performance. Conclusion: This result shed light on the diagnosis and treatment of AD.

Short- and Medium-range Prediction of Relativistic Electron Flux in the Earth’s Outer Radiation Belt by Machine Learning Methods

2021 ◽  
Vol 3 ◽  
pp. 47-57
Author(s):  
I. N. Myagkova ◽  
◽  
V. R. Shirokii ◽  
Yu. S. Shugai ◽  
O. G. Barinov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiation Belt ◽  
Gradient Boosting ◽  
Relativistic Electrons ◽  
Learning Methods ◽  
Outer Radiation Belt ◽  
Machine Learning Methods ◽  
The Earth ◽  
Skill Scores ◽  
Medium Range

The ways are studied to improve the quality of prediction of the time series of hourly mean fluxes and daily total fluxes (fluences) of relativistic electrons in the outer radiation belt of the Earth 1 to 24 hours ahead and 1 to 4 days ahead, respectively. The prediction uses an approximation approach based on various machine learning methods, namely, artificial neural networks (ANNs), decision tree (random forest), and gradient boosting. A comparison of the skill scores of short-range forecasts with the lead time of 1 to 24 hours showed that the best results were demonstrated by ANNs. For medium-range forecasting, the accuracy of prediction of the fluences of relativistic electrons in the Earth’s outer radiation belt three to four days ahead increases significantly when the predicted values of the solar wind velocity near the Earth obtained from the UV images of the Sun of the AIA (Atmospheric Imaging Assembly) instrument of the SDO (Solar Dynamics Observatory) are included to the list of the input parameters.

scholarly journals Establishment of a Predictive Model for GvHD-free, Relapse-free Survival after Allogeneic HSCT using Ensemble Learning

2021 ◽  
Author(s):  
Makoto Iwasaki ◽  
Junya Kanda ◽  
Yasuyuki Arai ◽  
Tadakazu Kondo ◽  
Takayuki Ishikawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cell ◽  
Stem Cell Transplantation ◽  
Predictive Model ◽  
Cell Transplantation ◽  
Risk Group ◽  
Machine Learning Algorithms ◽  
Ensemble Model ◽  
Relapse Free Survival ◽  
Free Survival

Graft-versus-host-disease-free, relapse-free survival (GRFS) is a useful composite endpoint that measures survival without relapse or significant morbidity after allogeneic hematopoietic stem cell transplantation (allo-HSCT). We aimed to develop a novel analytical method that appropriately handles right-censored data and competing risks to understand the risk for GRFS and each component of GRFS. This study was a retrospective data-mining study on a cohort of 2207 adult patients who underwent their first allo-HSCT at the Kyoto Stem Cell Transplantation Group (KSCTG), a multi-institutional joint research group of 17 transplantation centers in Japan. The primary endpoint was GRFS. A stacked ensemble of Cox proportional hazard regression and seven machine learning algorithms was applied to develop a prediction model. The median age of patients was 48 years. For GRFS, the stacked ensemble model achieved better predictive accuracy evaluated by C-index than other top-of-the-art competing risk models (ensemble model: 0.670, Cox-PH: 0.668, Random Survival Forest: 0.660, Dynamic DeepHit: 0.646). The probability of GRFS after 2 years was 30.54% for the high-risk and 40.69% for the low-risk group, respectively (hazard ratio [HR] compared to the low-risk group: 2.127; 95% CI: 1.19-3.80). We developed a novel predictive model for survival analysis that showed superior risk stratification to existing methods using a stacked ensemble of multiple machine learning algorithms.

scholarly journals Emergency department admissions during COVID-19: explainable machine learning to characterise data drift and detect emergent health risks

2021 ◽  
Author(s):  
Christopher Duckworth ◽  
Francis P Chmiel ◽  
Dan K. Burns ◽  
Zlatko D Zlatev ◽  
Neil M White ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Predictive Model ◽  
Clinical Outcomes ◽  
Health Risks ◽  
Concept Drift ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Healthcare Settings ◽  
Acute Healthcare

Supervised machine learning algorithms deployed in acute healthcare settings use data describing historical episodes to predict clinical outcomes. Clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (a phenomenon known as data drift), and so can the relationship between episode characteristics and associated clinical outcomes (so-called, concept drift). We demonstrate how explainable machine learning can be used to monitor data drift in a predictive model deployed within a hospital emergency department. We use the COVID-19 pandemic as an exemplar cause of data drift, which has brought a severe change in operational circumstances. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission to hospital during an emergency department attendance. We evaluate our model's performance on attendances occurring pre-pandemic (AUROC 0.856 95\%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC 0.826 95\%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature's SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.

scholarly journals Machine Learning Algorithms Using Logistic Regression for Predicting Neurosurgical Outcomes

2020 ◽  
Author(s):  
Nida Fatima
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Predictive Model ◽  
Web Application ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Brier Score ◽  
Patient Counselling ◽  
Selection Of Variables

Abstract Background: Preoperative prognostication of clinical and surgical outcome in patients with neurosurgical diseases can improve the risk stratification, thus can guide in implementing targeted treatment to minimize these events. Therefore, the author aims to highlight the development and validation of predictive models determining neurosurgical outcomes through machine learning algorithms using logistic regression.Methods: Logistic regression (enter, backward and forward) and least absolute shrinkage and selection operator (LASSO) method for selection of variables from selected database can eventually lead to multiple candidate models. The final model with a set of predictive variables must be selected based upon the clinical knowledge and numerical results.Results: The predictive model which performed best on the discrimination, calibration, Brier score and decision curve analysis must be selected to develop machine learning algorithms. Logistic regression should be compared with the LASSO model. Usually for the big databases, the predictive model selected through logistic regression gives higher Area Under the Curve (AUC) than those with LASSO model. The predictive probability derived from the best model could be uploaded to an open access web application which is easily deployed by the patients and surgeons to make a risk assessment world-wide.Conclusions: Machine learning algorithms provide promising results for the prediction of outcomes following cranial and spinal surgery. These algorithms can provide useful factors for patient-counselling, assessing peri-operative risk factors, and predicting post-operative outcomes after neurosurgery.

scholarly journals PreMevE: New Predictive Model for Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt

Space Weather ◽  
2019 ◽  
Vol 17 (3) ◽  
pp. 438-454 ◽  
Author(s):  
Yue Chen ◽  
Geoffrey D. Reeves ◽  
Xiangrong Fu ◽  
Michael Henderson
Keyword(s):  
Predictive Model ◽  
Radiation Belt ◽  
Outer Radiation Belt
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