Imputation of GPS Coordinate Time Series Using missForest

A novel MissForest-based missing values imputation approach with recursive feature elimination in medical applications

BACKGROUND

Missing values in datasets present significant challenges for data analysis, particularly in the medical field where data accuracy is crucial for patient diagnosis and treatment. Although MissForest (MF) has demonstrated efficacy in imputation research and recursive feature elimination (RFE) has proven effective in feature selection, the potential for enhancing MF through RFE integration remains unexplored.

METHODS

This study introduces a novel imputation method, "recursive feature elimination-MissForest" (RFE-MF), designed to enhance imputation quality by reducing the impact of irrelevant features. A comparative analysis is conducted between RFE-MF and four classical imputation methods: mean/mode, k-nearest neighbors (kNN), multiple imputation by chained equations (MICE), and MF. The comparison is carried out across ten medical datasets containing both numerical and mixed data types. Different missing data rates, ranging from 10 to 50%, are evaluated under the missing completely at random (MCAR) mechanism. The performance of each method is assessed using two evaluation metrics: normalized root mean squared error (NRMSE) and predictive fidelity criterion (PFC). Additionally, paired samples t-tests are employed to analyze the statistical significance of differences among the outcomes.

RESULTS

The findings indicate that RFE-MF demonstrates superior performance across the majority of datasets when compared to four classical imputation methods (mean/mode, kNN, MICE, and MF). Notably, RFE-MF consistently outperforms the original MF, irrespective of variable type (numerical or categorical). Mean/mode imputation exhibits consistent performance across various scenarios. Conversely, the efficacy of kNN imputation fluctuates in relation to varying missing data rates.

CONCLUSION

This study demonstrates that RFE-MF holds promise as an effective imputation method for medical datasets, providing a novel approach to addressing missing data challenges in medical applications.

An improved Gaussian process for filling the missing data in GNSS position time series considering the influence of adjacent stations

Due to various unavoidable reasons or gross error elimination, missing data inevitably exist in global navigation satellite system (GNSS) position time series, which may result in many analysis methods not being applicable. Typically, interpolating the missing data is a crucial preprocessing step before analyzing the time series. The conventional methods for filling missing data do not consider the influence of adjacent stations. In this work, an improved Gaussian process (GP) approach is developed to fill the missing data of GNSS time series, in which the time series of adjacent stations are applied to construct impact factors, together with a comparison of the conventional GP and the commonly used cubic spline methods. For the simulation experiments, the root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) are adopted to evaluate the performance of the improved GP. The results show that the filled missing data of the improved GP are closer to the true values than those of the conventional GP and cubic spline methods, regardless of the missing percentages ranging from 5 to 30%, with an interval of 5%. Specifically, the mean relative RMSE and MAE improvements for the improved GP with respect to the conventional GP are 21.2%, 21.3% and 8.3% and 12.7%, 16.2% and 11.01% for the North (N), East (E) and Up (U) components, respectively. In the real experiment, eight GNSS stations are analyzed using improved GP, together with conventional GP and a cubic spline. The results indicate that the first three principal components (PCs) of the improved GP can perverse 98.3%, 99.8% and 77.0% of the total variance for the N, E and U components, respectively. This value is obviously higher than those of the conventional GP and cubic spline. Therefore, we can conclude that the improved GP can better fill in the missing data in GNSS position time series than the conventional GP and cubic spline because of the impacts of adjacent stations.

Factors Influencing Hospitalization of Nursing Home Residents in Korea Using Regularized Negative Binomial Regression

The appropriateness of hospitalization for nursing home (NH) residents is still up for debate, with determining factors including timeliness, available treatment, healthcare staff, medication options in hospitals, and safety issues. Although the factors leading to hospitalization have been studied expansively, research on staffing is limited. Thus, this study aimed to investigate organizational predictors, nurse staffing, and government incentives and find important factors to hospitalization due to infection or disease among NH residents in Korea. A cross-sectional design was used, and data were collected via survey from a total of 51 NHs from August 27, 2021 to March 25, 2022. A total of 32 explanatory variables were included. The response variable was the count of hospitalized residents due to infection or disease. We analyzed data using least absolute shrinkage and negative binomial regression. We found that registered nurses' increased hours per resident day were related to decreased hospitalizations due to infection or disease. Appropriate retention and recruitment of nurse staffing with professional leadership should be performed to increase the quality of care for NH residents.

Prioritized regional management for antibiotics and heavy metals in animal manure across China

High levels of antibiotics and heavy metals in animal manure pose a potential threat to both the ecological environment and public health. A regional knowledge of their distribution and risk assessment across China remains unclear. A dataset containing 4082 records covering a total of forty-two antibiotics and eight heavy metals was established for animal manure across China. The results showed that the residual concentration of antibiotics was in the order of tetracyclines > aminoglycosides > fluoroquinolones > macrolides > sulfonamides > β-lactams, and that of heavy metals is Zn > Cu > Cr > Pb > Ni > As > Cd > Hg. The mean concentration of antibiotics and heavy metals was higher in pig manure compared to chicken and cow manure (Kruskal-Wallis test). The lowest level of antibiotics was observed in Northwest China based on geographic distribution characteristics. It was related to the high ratio of cow and sheep farming that less antibiotics were administered to. The pollution status of heavy metals was more severe in East China. Furthermore, high correlations were observed between antibiotics (tetracyclines) and heavy metals (Cu, Zn, and As). Especially, tetracycline in North China and Cd in Northeast China exhibited a high risk in manure; thus, they were priority regions for antibiotics/heavy metals pollution control. This study identified risk assessment of typical antibiotics and heavy metals in animal manure and emphasized the necessity of regional management across China.