Malnutrition, Health and the Role of Machine Learning in Clinical Setting

Sharma, Vaibhav; Sharma, Vishakha; Khan, Ayesha; Wassmer, David J.; Schoenholtz, Matthew D.; Hontecillas, Raquel; Bassaganya‐Riera, Josep; Zand, Ramin; Abedi, Vida

doi:10.3389/fnut.2020.00044

Cited by 22 publications

(16 citation statements)

References 77 publications

(63 reference statements)

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“…Populations with a higher disability 18 and lower median income 19 might be less mobile, have more comorbidities, 20,21 and also less likely benefit from timely high-quality care 22 and high-quality nutrition; all of these factors could be equally important to combating this pandemic. 23,24 Furthermore, our analysis of the preliminary data on mobility, given the recent social distancing guidelines, corroborate the impact of this intervention on lowering the infection rate and death.…”

Section: Discussionsupporting

confidence: 72%

Racial, Economic and Health Inequality and COVID-19 Infection in the United States

Abedi

Olulana

Avula

et al. 2020

Preprint

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148

109

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Background: There is preliminary evidence of racial and social-economic disparities in the population infected by and dying from COVID-19. The goal of this study is to report the associations of COVID-19 with respect to race, health and economic inequality in the United States.Methods: We performed a cross-sectional study of the associations between infection and mortality rate of COVID-19 and demographic, socioeconomic and mobility variables from 369 counties (total population: 102,178,117 [median: 73,447, IQR: 30,098]) from the seven most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts). Findings:The risk factors for infection and mortality are different. Our analysis shows that counties with more diverse demographics, higher population, education, income levels, and lower disability rates were at a higher risk of COVID-19 infection. However, counties with higher disability and poverty rates had a higher death rate. African Americans were more vulnerable to COVID-19 than other ethnic groups (1,981 African American infected cases versus 658 Whites per million). Data on mobility changes corroborate the impact of social distancing. Interpretation:The observed inequality might be due to the workforce of essential services, poverty, and access to care. Counties in more urban areas are probably better equipped at providing care. The lower rate of infection, but a higher death rate in counties with higher poverty and disability could be due to lower levels of mobility, but a higher rate of comorbidities and health care access.Funding: This study had no specific funding.

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Section: Discussionsupporting

confidence: 72%

Racial, Economic and Health Inequality and COVID-19 Infection in the United States

Abedi

Olulana

Avula

et al. 2020

Preprint

Self Cite

148

109

View full text Add to dashboard Cite

show abstract

“…Full risk factor description is available in Table 1 **Denotes statistically significant benefit from timely high-quality care [24] and high-quality nutrition; all of these factors could be equally important to combating this pandemic [25,26]. Furthermore, our analysis of the preliminary data on mobility, given the recent social distancing guidelines, corroborate the impact of this intervention on lowering the infection rate and death.…”

Section: E-04supporting

confidence: 56%

“…By adding the interaction terms in the linear regression model (death rate~poverty + disability + poverty:disability) of death rate, we do not observe a significant interaction ( p = 0.469), suggesting these two variables could be independent in their contribution to the risk of mortality. Populations with a higher disability [ 20 ] and lower median income [ 21 ] might be less mobile, have more comorbidities [ 22 , 23 ], and also less likely benefit from timely high-quality care [ 24 ] and high-quality nutrition; all of these factors could be equally important to combating this pandemic [ 25 , 26 ]. Furthermore, our analysis of the preliminary data on mobility, given the recent social distancing guidelines, corroborate the impact of this intervention on lowering the infection rate and death.…”

Section: Discussionmentioning

confidence: 99%

Racial, Economic, and Health Inequality and COVID-19 Infection in the United States

Abedi

Olulana

Avula

et al. 2020

J. Racial and Ethnic Health Disparities

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Objectives There is preliminary evidence of racial and social economic disparities in the population infected by and dying from COVID-19. The goal of this study is to report the associations of COVID-19 with respect to race, health, and economic inequality in the United States. Methods We performed an ecological study of the associations between infection and mortality rate of COVID-19 and demographic, socioeconomic, and mobility variables from 369 counties (total population, 102,178,117 [median, 73,447; IQR, 30,761-256,098]) from the seven most affected states (Michigan,

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“…We have also performed a detailed analysis of our Geisinger cohort and identified that sex was not an independent risk factor for all-cause mortality and ischemic stroke recurrence ( 33 ). Finally, the identification of malnutrition provides potential new venues to improve secondary prevention and outcome ( 34 ).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients

et al. 2021

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Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions.Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting–XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables.Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64–0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69).Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

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Malnutrition, Health and the Role of Machine Learning in Clinical Setting

Cited by 22 publications

References 77 publications

Racial, Economic and Health Inequality and COVID-19 Infection in the United States

Racial, Economic and Health Inequality and COVID-19 Infection in the United States

Racial, Economic, and Health Inequality and COVID-19 Infection in the United States

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients

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