scholarly journals 438. Phenotypic Differences Between Distinct Immune Biomarker Clusters During the ‘Hyperinflammatory’ Middle-Phase of COVID-19

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S320-S321
Author(s):  
Paul W Blair ◽  
Joost Brandsma ◽  
Nusrat J Epsi ◽  
Stephanie A Richard ◽  
Deborah Striegel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Pulmonary Disease ◽  
The United States ◽  
Phase Iii ◽  
Severe Illness ◽  
Stepwise Logistic Regression ◽  
Chronic Pulmonary Disease ◽  
Middle Phase ◽  
Broad Dynamic Range

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections peak during an inflammatory ‘middle’ phase and lead to severe illness predominately among those with certain comorbid noncommunicable diseases (NCDs). We used network machine learning to identify inflammation biomarker patterns associated with COVID-19 among those with NCDs. Methods SARS-CoV-2 RT-PCR positive subjects who had specimens available within 15-28 days post-symptom onset were selected from the DoD/USU EPICC COVID-19 cohort study. Plasma levels of 15 inflammation protein biomarkers were measured using a broad dynamic range immunoassay on samples collected from individuals with COVID-19 at 8 military hospitals across the United States. A network machine learning algorithm, topological data analysis (TDA), was performed using results from the ‘hyperinflammatory’ middle phase. Backward selection stepwise logistic regression was used to identify analytes associated with each cluster. NCDs with a significant association (0.05 significance level) across clusters using Fisher’s exact test were further evaluated comparing the NCD frequency in each cluster against all other clusters using a Kruskal-Wallis test. A sensitivity analysis excluding mild disease was also performed. Results The analysis population (n=129, 33.3% female, median 41.3 years of age) included 77 ambulatory, 31 inpatient, 16 ICU-level, and 5 fatal cases. TDA identified 5 unique clusters (Figure 1). Stepwise regression with a Bonferroni-corrected cutoff adjusted for severity identified representative analytes for each cluster (Table 1). The frequency of diabetes (p=0.01), obesity (p< 0.001), and chronic pulmonary disease (p< 0.001) differed among clusters. When restricting to hospitalized patients, obesity (8 of 11), chronic pulmonary disease (6 of 11), and diabetes (6 of 11) were more prevalent in cluster C than all other clusters. Cluster differences in comorbid diseases and severity by cluster. 1A: bar plot of diabetes prevalence; 1B: bar plot of chronic lung disease ; 1C: bar plot of obesity prevalence; 1D: prevalence of steroid treatment ; 1E: Topologic data analysis network with clusters labeled; 1F: Bar plot of ordinal levels of severity. Conclusion Machine learning clustering methods are promising analytical tools for identifying inflammation marker patterns associated with baseline risk factors and severe illness due to COVID-19. These approaches may offer new insights for COVID19 prognosis, therapy, and prevention. Disclosures Simon Pollett, MBBS, Astra Zeneca (Other Financial or Material Support, HJF, in support of USU IDCRP, funded under a CRADA to augment the conduct of an unrelated Phase III COVID-19 vaccine trial sponsored by AstraZeneca as part of USG response (unrelated work))

scholarly journals Topological data analysis identifies distinct biomarker phenotypes during the inflammatory phase of COVID-19

2021 ◽  
Author(s):  
Paul W Blair ◽  
Joost Brandsma ◽  
Josh G. Chenoweth ◽  
Stephanie A. Richard ◽  
Nusrat J. Epsi ◽  
...  
Keyword(s):  
Data Analysis ◽  
Dynamic Range ◽  
The United States ◽  
Topological Data Analysis ◽  
Severe Illness ◽  
Clustering Methods ◽  
Inflammatory Phase ◽  
Inflammatory Protein ◽  
Broad Dynamic Range ◽  
Topological Data

OBJECTIVES: The relationships between baseline clinical phenotypes and the cytokine milieu of the peak inflammatory phase of coronavirus 2019 (COVID-19) are not yet well understood. We used Topological Data Analysis (TDA), a dimensionality reduction technique to identify patterns of inflammation associated with COVID-19 severity and clinical characteristics. DESIGN: Exploratory analysis from a multi-center prospective cohort study. SETTING: Eight military hospitals across the United States between April 2020 and January 2021. PATIENTS: Adult (≥18 years of age) SARS-CoV-2 positive inpatient and outpatient participants were enrolled with plasma samples selected from the putative inflammatory phase of COVID-19, defined as 15-28 days post symptom onset. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Concentrations of 12 inflammatory protein biomarkers were measured using a broad dynamic range immunoassay. TDA identified 3 distinct inflammatory protein expression clusters. Peak severity (outpatient, hospitalized, ICU admission or death), Charlson Comorbidity Index (CCI), and body mass index (BMI) were evaluated with logistic regression for associations with each cluster. The study population (n=129, 33.3% female, median 41.3 years of age) included 77 outpatient, 31 inpatient, 16 ICU-level, and 5 fatal cases. Three distinct clusters were found that differed by peak disease severity (p <0.001), age (p <0.001), BMI (p<0.001), and CCI (p=0.001). CONCLUSIONS: Exploratory clustering methods can stratify heterogeneous patient populations and identify distinct inflammation patterns associated with comorbid disease, obesity, and severe illness due to COVID-19.

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

scholarly journals A weighted patient network-based framework for predicting chronic diseases using graph neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haohui Lu ◽  
Shahadat Uddin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Cardiovascular Disease ◽  
Chronic Disease ◽  
Pulmonary Disease ◽  
Chronic Diseases ◽  
Prediction Models ◽  
Disease Prediction ◽  
Chronic Pulmonary Disease ◽  
Graph Neural Networks

AbstractChronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic disease based on Graph Neural Networks (GNNs) to address these issues. We begin by projecting a patient-disease bipartite graph to create a weighted patient network (WPN) that extracts the latent relationship among patients. We then use GNN-based techniques to build prediction models. These models use features extracted from WPN to create robust patient representations for chronic disease prediction. We compare the output of GNN-based models to machine learning methods by using cardiovascular disease and chronic pulmonary disease. The results show that our framework enhances the accuracy of chronic disease prediction. The model with attention mechanisms achieves an accuracy of 93.49% for cardiovascular disease prediction and 89.15% for chronic pulmonary disease prediction. Furthermore, the visualisation of the last hidden layers of GNN-based models shows the pattern for the two cohorts, demonstrating the discriminative strength of the framework. The proposed framework can help stakeholders improve health management systems for patients at risk of developing chronic diseases and conditions.

A Database for Counterfeit Electronics and Automatic Defect Detection Based on Image Processing and Machine Learning

2016 ◽  
Author(s):  
Navid Asadizanjani ◽  
Sachin Gattigowda ◽  
Mark Tehranipoor ◽  
Domenic Forte ◽  
Nathan Dunn
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Integrated Circuits ◽  
Web Application ◽  
The United States ◽  
Global Market ◽  
Online Database ◽  
Ongoing Effort ◽  
Time Period ◽  
Physical Defects

Abstract Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1020-1030
Author(s):  
Pradeep S. ◽  
Jagadish S. Kallimani
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Categorical Data ◽  
Numerical Data ◽  
Processing Technique ◽  
Machine Learning Algorithms ◽  
Sequential Data ◽  
Industry Standard ◽  
Robust Model ◽  
Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

Global Power Shift

2017 ◽  
Author(s):  
Roberts Cynthia ◽  
Leslie Armijo ◽  
Saori Katada
Keyword(s):  
United States ◽  
South Africa ◽  
Data Analysis ◽  
The United States ◽  
Rising Powers ◽  
Extensive Data ◽  
Power Shift ◽  
Multiple Dimensions ◽  
Global Power ◽  
Rise Of China

This chapter evaluates multiple dimensions of the global power shift from the incumbent G5/G7 powers to the rising powers, especially the members of the BRICS (Brazil, Russia, India, China, and South Africa). Taking note of alternative conceptualizations of interstate “power,” the text maps the redistribution of economic capabilities from the G7 to the BRICS, most particularly the relative rise of China and decline of Japan, and especially Europe. Given these clear trends in measurable material capabilities, the BRICS have obtained considerable autonomy from outside pressures. Although the BRICS’ economic, financial, and monetary capabilities remain uneven, their relative positions have improved steadily. Via extensive data analysis, the chapter finds that whether one examines China alone or the BRICS as a group, BRICS members have achieved the necessary capabilities to challenge the global economic and financial leadership of the currently dominant powers, perhaps even the United States one day.

Race and Gender

2020 ◽  
pp. 251-269 ◽  
Author(s):  
Timnit Gebru
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
The United States ◽  
Error Rates ◽  
Political Factors ◽  
Recidivism Rates ◽  
Race And Gender ◽  
Decision Tools ◽  
And Gender ◽  
Technical Solutions

This chapter discusses the role of race and gender in artificial intelligence (AI). The rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial automated facial analysis systems have much higher error rates for dark-skinned women, while having minimal errors on light-skinned men. Moreover, a 2016 ProPublica investigation uncovered that machine learning–based tools that assess crime recidivism rates in the United States are biased against African Americans. Other studies show that natural language–processing tools trained on news articles exhibit societal biases. While many technical solutions have been proposed to alleviate bias in machine learning systems, a holistic and multifaceted approach must be taken. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.

scholarly journals Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

2021 ◽  
Vol 14 (5) ◽  
pp. 472
Author(s):  
Tyler C. Beck ◽  
Kyle R. Beck ◽  
Jordan Morningstar ◽  
Menny M. Benjamin ◽  
Russell A. Norris
Keyword(s):  
United States ◽  
Machine Learning ◽  
Drug Interactions ◽  
The United States ◽  
Structural Features ◽  
Physiochemical Properties ◽  
Drug Dosing ◽  
Therapeutic Outcomes ◽  
Cyp Inhibition ◽  
Cyp Inhibitors

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

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