Background Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions. Methods We conducted a retrospective cohort study on 37,091 consecutive hospitalized adult patients with 55,933 discharges between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Patients who were aged < 20 years, were admitted for cancer-related treatment, participated in clinical trial, were discharged against medical advice, died during admission, or lived abroad were excluded. Predictors for analysis included 7 categories of variables extracted from hospital’s medical record dataset. In total, four machine learning algorithms, namely logistic regression, random forest, extreme gradient boosting, and categorical boosting, were used to build classifiers for prediction. The performance of prediction models for 14-day unplanned readmission risk was evaluated using precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision–recall curve (AUPRC). Results In total, 24,722 patients were included for the analysis. The mean age of the cohort was 57.34 ± 18.13 years. The 14-day unplanned readmission rate was 1.22%. Among the 4 machine learning algorithms selected, Catboost had the best average performance in fivefold cross-validation (precision: 0.9377, recall: 0.5333, F1-score: 0.6780, AUROC: 0.9903, and AUPRC: 0.7515). After incorporating 21 most influential features in the Catboost model, its performance improved (precision: 0.9470, recall: 0.5600, F1-score: 0.7010, AUROC: 0.9909, and AUPRC: 0.7711). Conclusions Our models reliably predicted 14-day unplanned readmissions and were explainable. They can be used to identify patients with a high risk of unplanned readmission based on influential features, particularly features related to diagnoses. The operation of the models with physiological indicators also corresponded to clinical experience and literature. Identifying patients at high risk with these models can enable early discharge planning and transitional care to prevent readmissions. Further studies should include additional features that may enable further sensitivity in identifying patients at a risk of early unplanned readmissions.
In order which aim to save land resource and use low grade nature resource to realize high cost performance product. In this paper, the molding method of quartz sand fired brick is discussed. As the plastic (hand pressing) is only used to manually beat the mud mass, the sludge is squeezed by hand. Into the mold and molding and cold isostatic pressure molding pressure of 100 MPa, The difference is far away, which makes the gaps between the particles close to each other greatly different, the pressure of hand-press molding is small, the voids in the blank after molding are large and the hole diameter is large, and the green compact density of the brick body is small. The compact densities of the 65MPa and 150MPa pressed brick bodies are all lower than 100MPa, but 150MPa is still denser than 65MPa. The compressive strength is first increased and then decreased with increasing the molding pressure, when the pressure is 100 MPa, the maximum compressive strength is achieved at 100 MPa achieves the highest point.
Ultrafine Co2O3powder was prepared via hydrothermal synthesis. The effect of technology on the performance of the superfine Co2O3powders was investigated, and the hydrothermal parameters in preparing Co2O3were gradually improved. In addition, the morphology and grain size of the Co2O3powder were analyzed by FESEM. Results show that reducing the salt–alkali molar ratio resulted in more uniform Co2O3powder and smaller particles, with average particle size of approximately 40 nm. Reaction time displayed little effect on the Co2O3powder, but the particle size decreased with the reaction time. The concentration of salt solution remarkably affected the morphology of the Co2O3powder. Lower concentration resulted in smaller particle aggregation and particle size.
The sintering methods of quartz sand porous ceramics were researched with the low grade quartz sand along the Yangtze River via the vacuum sintering method in this paper, which lay technology foundation for researching new heat insulating materials. The quartz porous ceramics is obtained with the high performance cost, the quartz porous ceramics is sintered at 1050°C via the vacuum conditions, the density of ceramics is 1.267g/cm3, the porosity is 51.6%, the compressive strength is 3.184MPa, the porous ceramics show the homogeneous distribution micro-pore and good shape. The density and the compressive strength of prepared ceramics via the vacuum sintering both are higher than that of prepared ceramics via the atmosphere sintering, however, the porosity is shown the opposite results.
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