For hospitals where decisions regarding acceptable rates of elective admissions are made in advance based on expected available bed capacity and emergency requests, accurate predictions of inpatient bed capacity are especially useful for capacity reservation purposes. As given, the remaining unoccupied beds at the end of each day, bed capacity of the next day can be obtained by examining the forecasts of the number of discharged patients during the next day. The features of fluctuations in daily discharges like trend, seasonal cycles, special-day effects, and autocorrelation complicate decision optimizing, while time-series models can capture these features well. This research compares three models: a model combining seasonal regression and ARIMA, a multiplicative seasonal ARIMA (MSARIMA) model, and a combinatorial model based on MSARIMA and weighted Markov Chain models in generating forecasts of daily discharges. The models are applied to three years of discharge data of an entire hospital. Several performance measures like the direction of the symmetry value, normalized mean squared error, and mean absolute percentage error are utilized to capture the under- and overprediction in model selection. The findings indicate that daily discharges can be forecast by using the proposed models. A number of important practical implications are discussed, such as the use of accurate forecasts in discharge planning, admission scheduling, and capacity reservation.
ObjectiveThe personality trait of neuroticism is a risk factor for major depressive disorder (MDD), but this relationship has not been demonstrated in clinical samples from Asia.MethodsWe examined a large-scale clinical study of Chinese Han women with recurrent major depression and community-acquired controls.ResultsElevated levels of neuroticism increased the risk for lifetime MDD (with an odds ratio of 1.37 per SD), contributed to the comorbidity of MDD with anxiety disorders, and predicted the onset and severity of MDD. Our findings largely replicate those obtained in clinical populations in Europe and US but differ in two ways: we did not find a relationship between melancholia and neuroticism; we found lower mean scores for neuroticism (3.6 in our community control sample).LimitationsOur findings do not apply to MDD in community-acquired samples and may be limited to Han Chinese women. It is not possible to determine whether the association between neuroticism and MDD reflects a causal relationship.ConclusionsNeuroticism acts as a risk factor for MDD in Chinese women, as it does in the West and may particularly predispose to comorbidity with anxiety disorders. Cultural factors may have an important effect on its measurement.
Summary
Background
Large tertiary hospitals usually face long waiting lines; patients who want to receive hospitalization need to be screened in advance. The patient admission screening process involves a health‐care professional ranking patients by analyzing registration information.
Objective
The purpose of this study was to develop a machine‐learning approach to screening, using historical data and the experience of health‐care professionals to develop a set of screening rules to help health‐care professionals prioritize patient needs automatically.
Methods
We used five machine‐learning methods to sequence and predict elective patients: logistic regression (LR), random forest (RF), gradient‐boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and an ensemble model of the four models.
Results
The results indicate that all of the five models showed a good prioritization performance with high predictive values. In particular, XGBoost had the best predictive performance compared with others in terms of the area under the receiver operating characteristic curve (AUC), with the AUC values of LR, RF, GBDT, XGBoost, and the ensemble model being 0.881, 0.816, 0.820, 0.901, and 0.897, respectively.
Conclusion
The results reported here indicate that machine‐learning techniques can be valuable for automating the screening process. Our model can assist health‐care professionals in automatically evaluating less complex cases by identifying important factors affecting patient admission.
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