Acute Hypertensive Episodes Prediction

Itzhak, Nevo; Nagori, Aditya; Lior, Edo; Schvetz, Maya; Lodha, Rakesh; Sethi, Tavpritesh; Moskovitch, Robert

doi:10.1007/978-3-030-59137-3_35

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Since December 2019, the novel coronavirus disease 2019 (COVID-19) has spread rapidly around the globe, infecting millions, leading to severe illness, hospitalization, admission to intensive care units (ICUs) [ 11 ], and death. In the USA, more than 20 million people have been infected, and some 400,000 died from COVID-19.…”

Section: Introductionmentioning

confidence: 99%

The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality

et al. 2022

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The effect of socio-economic factors, ethnicity, and other factors, on the morbidity and mortality of COVID-19 at the sub-population -level, rather than at the individual level, and their temporal dynamics, is only partially understood. Fifty-three county-level features were collected between 4/2020 and 11/2020 from 3,071 US counties from publicly available data of various American government and news websites: ethnicity, socio-economic factors, educational attainment, mask usage, population density, age distribution, COVID-19 morbidity and mortality, presidential election results, and ICU beds. We trained machine learning models that predict COVID-19 mortality and morbidity using county-level features and then performed a SHAP value game theoretic importance analysis of the predictive features for each model. The classifiers produced an AUROC of 0.863 for morbidity prediction and an AUROC of 0.812 for mortality prediction. A SHAP value-based analysis indicated that poverty rate, obesity rate, mean commute time, and mask usage statistics significantly affected morbidity rates, while ethnicity, median income, poverty rate, and education levels heavily influenced mortality rates. Surprisingly, the correlation between several of these factors and COVID-19 morbidity and mortality gradually shifted and even reversed during the study period; our analysis suggests that this phenomenon was probably due to COVID-19 being initially associated with more urbanized areas and, then, from 9/2020, with less urbanized ones. Thus, socio-economic features such as ethnicity, education, and economic disparity are the major factors for predicting county-level COVID-19 mortality rates. Between counties, low variance factors (e.g., age) are not meaningful predictors. The inversion of some correlations over time can be explained by COVID-19 spreading from urban to rural areas. Supplementary Information The online version contains supplementary material available at 10.1007/s11524-021-00601-7.

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Section: Introductionmentioning

confidence: 99%

The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality

et al. 2022

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“…Higher AUROC and AUPRC values imply that the model is more accurate. Interested readers might wish to read more about these metrics and the relationship between them elsewhere [1].…”

Section: Appendix - Supplemental Materialsmentioning

confidence: 99%

“…Since December 2019, the novel Coronavirus disease 2019 (COVID-19) has spread rapidly around the globe, infecting millions, leading to severe illness, hospitalization, admission to intensive care units (ICUs) [1], and death. In the U.S., more than 20 million people have been infected and some 400,000 died from COVID-19.…”

Section: Introductionmentioning

confidence: 99%

The Impact of U.S. County-Level Factors on COVID-19 Morbidity and Mortality

Itzhak

Shahar

Moskovitch

et al. 2021

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BackgroundThe effect of socioeconomic factors, ethnicity, and other variables, on the frequency of COVID-19 cases [morbidity] and induced deaths [mortality] at sub-population, rather than at individual levels, is only partially understood.ObjectiveTo determine which county-level features best predict COVID-19 morbidity and mortality for a given county in the U.S.DesignA Machine-Learning model that predicts COVID-19 mortality and morbidity using county-level features, followed by a SHAP-values-based importance analysis of the predictive features.SettingPublicly available data from various American government and news websites.Participants3,071 U.S. counties, from which 53 county-level features, as well as morbidity and mortality numbers, were collected.MeasurementsFor each county: Ethnicity, socioeconomic factors, educational attainment, mask usage, population density, age distribution, COVID-19 morbidity and mortality, air quality indicators, presidential election results, ICU beds.ResultsA Random Forest classifier produced an AUROC of 0.863 for morbidity prediction and an AUROC of 0.812 for mortality prediction. A SHAP-values-based analysis indicated that poverty rate, obesity rate, mean commute time to work, and proportion of people that wear masks significantly affected morbidity rates, while ethnicity, median income, poverty rate, and education levels, heavily influenced mortality rates. The correlation between several of these factors and COVID-19 morbidity and mortality, from 4/2020 to 11/2020 shifted, probably due to COVID-19 being initially associated with more urbanized areas, then with less urbanized ones.LimitationsData are still coming in.ConclusionsEthnicity, education, and economic disparity measures are major factors in predicting the COVID-19 mortality rate in a county. Between-counties low-variance factors (e.g., age), are not meaningful predictors.Differing correlations can be explained by the COVID-19 spread from metropolitan to less metropolitan areas.Primary Funding SourceNone.

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“…For example, Yang et al [8], Komorowski et al [9] and Reyna et al [10] developed artificial intelligence models to predict sepsis in intensive care; while Mao et al [11] and Lauritsen et al [12] extended the prediction application to ED and general ward. Itzhak et al [13] and Cherifa et al [14] developed models to predict acute hypertensive or hypotensive episodes among ICU admissions. However, few, if any studies have been designed to understand specific clinical features that patients exhibit at the time that physicians initiate rapid treatment of sepsis.…”

Section: Introductionmentioning

confidence: 99%

Clinical factors associated with rapid treatment of sepsis

et al. 2021

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Purpose To understand what clinical presenting features of sepsis patients are historically associated with rapid treatment involving antibiotics and fluids, as appropriate. Design This was a retrospective, observational cohort study using a machine-learning model with an embedded feature selection mechanism (gradient boosting machine). Methods For adult patients (age ≥ 18 years) who were admitted through Emergency Department (ED) meeting clinical criteria of severe sepsis from 11/2007 to 05/2018 at an urban tertiary academic medical center, we developed gradient boosting models (GBMs) using a total of 760 original and derived variables, including demographic variables, laboratory values, vital signs, infection diagnosis present on admission, and historical comorbidities. We identified the most impactful factors having strong association with rapid treatment, and further applied the Shapley Additive exPlanation (SHAP) values to examine the marginal effects for each factor. Results For the subgroups with or without fluid bolus treatment component, the models achieved high accuracy of area-under-receiver-operating-curve of 0.91 [95% CI, 0.86–0.95] and 0.84 [95% CI, 0.81–0.86], and sensitivity of 0.81[95% CI, 0.72–0.87] and 0.91 [95% CI, 0.81–0.97], respectively. We identified the 20 most impactful factors associated with rapid treatment for each subgroup. In the non-hypotensive subgroup, initial physiological values were the most impactful to the model, while in the fluid bolus subgroup, value minima and maxima tended to be the most impactful. Conclusion These machine learning methods identified factors associated with rapid treatment of severe sepsis patients from a large volume of high-dimensional clinical data. The results provide insight into differences in the rapid provision of treatment among patients with sepsis.

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Acute Hypertensive Episodes Prediction

Cited by 6 publications

References 11 publications

The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality

The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality

The Impact of U.S. County-Level Factors on COVID-19 Morbidity and Mortality

Clinical factors associated with rapid treatment of sepsis

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