ArticleInternational journal of medical informatics2020
Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms.
Article in International journal of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 44 citations in OpenAlex.
- Machine learning for predicting the risk stratification of 1-5 cm gastric gastrointestinal stromal tumors based on CT.BMC medical imaging · 2023Trial
- Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19.Journal of public health in Africa · 2023Article
- Mapping the risk of respiratory infections using suburban district areas in a large city in Colombia.BMC public health · 2023Article
- Social Factors as Major Determinants of Rural Development Variation for Predicting Epidemic Vulnerability: A Lesson for the Future.International journal of environmental research and public health · 2022Article
- Leveraging data analytics to understand the relationship between restaurants' safety violations and COVID-19 transmission.International journal of hospitality management · 2022Article
- A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020.Transactions in GIS : TG · 2021Review
- Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States.International journal of environmental research and public health · 2021Article
- Spatial statistical analysis of pre-existing mortalities of 20 diseases with COVID-19 mortalities in the continental United States.Sustainable cities and society · 2021Article
- Burden of Respiratory Infection and Tuberculosis Among US States from 1990 to 2019.Clinical epidemiology · 2021Article
- Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis.American journal of Alzheimer's disease and other dementiasArticle
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAlthough lower respiratory infections (LRI) are among the leading causes of mortality in the US, their association with underlying factors and geographic variation have not been adequately examined.
methodsIn this study, explanatory variables (n = 46) including climatic, topographic, socio-economic, and demographic factors were compiled at the county level across the continentalUS.Machine learning algorithms - logistic regression (LR), random forest (RF), gradient boosting decision trees (GBDT), k-nearest neighbors (KNN), and support vector machine (SVM) - were employed to predict the presence/absence of hotspots (P < 0.05) for elevated age-adjusted LRI mortality rates in a geographic information system framework.
resultsOverall, there was a historical shift in hotspots away from the western US into the southeastern parts of the country and they were highly localized in a few counties. The two decision tree methods (RF and GBDT) outperformed the other algorithms (accuracies: 0.92; F1-scores: 0.85 and 0.84; area under the precision-recall curve: 0.84 and 0.83, respectively). Moreover, the results of the RF and GBDT indicated that higher spring minimum temperature, increased winter precipitation, and higher annual median household income were among the most substantial factors in predicting the hotspots.
conclusionsThis study helps raise awareness of public health decision-makers to develop and target LRI prevention programs.
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