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1.
目的 构建基于百度指数的CEEMD - GRNN模型预测HIV感染病例数、为信息缺乏的HIV感染疫情预测提供可靠的方法,旨在为艾滋病流行趋势的传统预测方法提供有益补充。方法 第一,利用GRNN建立HIV感染病例数原始序列与百度指数的非线性关系;第二,先利用CEEMD提取HIV感染病例数的周期,再利用GRNN建立提取后序列与百度指数的非线性关系;第三,基于上述两种思想进一步建立组合预测模型,称为CEEMD - GRNN组合模型;最后,将CEEMD - GRNN组合模型应用于HIV感染病例数的预测。结果 模型拟合结果表明,最优单项模型的MAPE为10.17%,CEEMD - GRNN组合模型的MAPE为7.18%,组合模型的预测精度高于最优单项模型。结论 本文提出的CEEMD - GRNN组合模型预测精度优于最优单项模型,所提模型能够为信息不充足的非线性HIV感染病例数据提供稳定可靠的预测方法。  相似文献   
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BackgroundPatients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first and second stage palliation surgeries.ObjectivesThe objective of this study is to develop and validate a real-time computer algorithm that can automatically recognize physiological precursors of cardiorespiratory deterioration in children with single-ventricle physiology during their interstage hospitalization.MethodsA retrospective study was conducted from prospectively collected physiological data of subjects with single-ventricle physiology. Deterioration events were defined as a cardiac arrest requiring cardiopulmonary resuscitation or an unplanned intubation. Physiological metrics were derived from the electrocardiogram (heart rate, heart rate variability, ST-segment elevation, and ST-segment variability) and the photoplethysmogram (peripheral oxygen saturation and pleth variability index). A logistic regression model was trained to separate the physiological dynamics of the pre-deterioration phase from all other data generated by study subjects. Data were split 50/50 into model training and validation sets to enable independent model validation.ResultsOur cohort consisted of 238 subjects admitted to the cardiac intensive care unit and stepdown units of Texas Children’s Hospital over a period of 6 years. Approximately 300,000 h of high-resolution physiological waveform and vital sign data were collected using the Sickbay software platform (Medical Informatics Corp., Houston, Texas). A total of 112 cardiorespiratory deterioration events were observed. Seventy-two of the subjects experienced at least 1 deterioration event. The risk index metric generated by our optimized algorithm was found to be both sensitive and specific for detecting impending events 1 to 2 h in advance of overt extremis (receiver-operating characteristic curve area: 0.958; 95% confidence interval: 0.950 to 0.965).ConclusionsOur algorithm can provide 1 to 2 h of advanced warning for 62% of all cardiorespiratory deterioration events in children with single-ventricle physiology during their interstage period, with only 1 alarm being generated at the bedside per patient per day.  相似文献   
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Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction ($≈ \mathcal{O}(10^{−6}))$ of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.  相似文献   
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Context

Survival predictions for advanced cancer patients impact many aspects of care, but the accuracy of clinician prediction of survival (CPS) is low. Prognostic tools such as the Palliative Prognostic Index (PPI) have been proposed to improve accuracy of predictions. However, it is not known if PPI is better than CPS at discriminating survival.

Objective

We compared the prognostic accuracy of CPS to PPI in patients with advanced cancer.

Methods

This was a prospective study in which palliative care physicians at our tertiary care cancer center documented both the PPI and CPS in hospitalized patients with advanced cancer. We compared the discrimination of CPS and PPI using concordance statistics, area under the receiver-operating characteristics curve (AUC), net reclassification index, and integrated discrimination improvement for 30-day survival and 100-day survival.

Results

Two hundred fifteen patients were enrolled with a median survival of 109 days and a median follow-up of 239 days. The AUC for 30-day survival was 0.76 (95% CI 0.66–0.85) for PPI and 0.58 (95% CI 0.47–0.68) for CPS (P < 0.0001). Using the net reclassification index, 67% of patients were correctly reclassified using PPI instead of CPS for 30-day survival (P = 0.0005). CPS and PPI had similar accuracy for 100-day survival (AUC 0.62 vs. 0.64; P = 0.58).

Conclusion

We found that PPI was more accurate than CPS when used to discriminate survival at 30 days, but not at 100 days. This study highlights the reason and timing for using PPI to facilitate survival predictions.  相似文献   
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Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many weeks in advance, but there is no documented evaluation of how such forecasts might support the day‐to‐day operations of public health staff. Methods: During the 2015 influenza season in Melbourne, Australia, weekly forecasts were presented at Health Department surveillance unit meetings, where they were evaluated and updated in light of expert opinion to improve their accuracy and usefulness. Results: Predictive capacity of the model was substantially limited by delays in reporting and processing arising from an unprecedented number of notifications, disproportionate to seasonal intensity. Adjustment of the predictive algorithm to account for these delays and increased reporting propensity improved both current situational awareness and forecasting accuracy. Conclusions: Collaborative engagement with public health practitioners in model development improved understanding of the context and limitations of emerging surveillance data. Incorporation of these insights in a quantitative model resulted in more robust estimates of disease activity for public health use. Implications for public health: In addition to predicting future disease trends, forecasting methods can quantify the impact of delays in data availability and variable reporting practice on the accuracy of current epidemic assessment. Such evidence supports investment in systems capacity.  相似文献   
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叶孟良  李智涛  欧荣 《重庆医学》2012,41(13):1260-1261
目的建立预测与监测的求和自回归移动平均模型(ARIMA)的时间序列模型,研究日住院量的变化规律。方法通过对2009年2~4月重庆市逐日住院患者量分析用Box-Ljung统计量评价ARIMA模型的拟合度,用平均预测相对误差作为预测效果的评价指标。结果重庆市住院患者量以周为时间周期,每周中以周一、二住院量达到高峰,周六、日为低谷。ARIMA(0,1,1)(1,1,1)7是重庆市2009年2~4月住院量预测最优拟合预测模型,一周和两周外推预测的平均相对误差分别为6.27%和9.14%。结论对住院患者量的历史数据进行时间序列分析是用于住院患者量监测的一个重要的内容。本研究所建立的ARIMA模型适用于重庆市住院患者量预测,预测精度较高。  相似文献   
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Artificial Neural Network (ANN) models were developed and applied in order to predict the total weekly number of Childhood Asthma Admission (CAA) at the greater Athens area (GAA) in Greece. Hourly meteorological data from the National Observatory of Athens and ambient air pollution data from seven different areas within the GAA for the period 2001-2004 were used. Asthma admissions for the same period were obtained from hospital registries of the three main Children's Hospitals of Athens. Three different ANN models were developed and trained in order to forecast the CAA for the subgroups of 0-4, 5-14-year olds, and for the whole study population. The results of this work have shown that ANNs could give an adequate forecast of the total weekly number of CAA in relation to the bioclimatic and air pollution conditions. The forecasted numbers are in very good agreement with the observed real total weekly numbers of CAA.  相似文献   
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Epicardial adipose tissue thickness is associated with the severity and extent of atherosclerotic coronary artery disease. We prospectively investigated whether epicardial adipose tissue thickness is related to coronary artery disease extent and complexity as denoted by Gensini and Syntax scores, and whether the thickness predicts critical disease.After performing coronary angiography in 183 patients who had angina or acute myocardial infarction, we divided them into 3 groups: normal coronary arteries, noncritical disease (≥1 coronary lesion with <70% stenosis), and critical disease (≥1 coronary lesion with <70% stenosis). We used transthoracic echocardiography to measure epicardial adipose tissue thickness, then calculated Gensini and Syntax scores by reviewing the angiograms.Mean thicknesses were 4.3 ± 0.9, 5.2 ± 1.5, and 7.5 ± 1.9 mm in patients with normal coronary arteries, noncritical disease, and critical disease, respectively (P <0.001). At progressive thicknesses (<5, 5–7, and >7 mm), mean Gensini scores were 4.1 ± 5.5, 19.8 ± 15.6, and 64.9 ± 32.4, and mean Syntax scores were 4.7 ± 5.9, 16.6 ± 8.5, and 31.7 ± 8.7, respectively (both P <0.001). Thickness had strong and positive correlations with both scores (Gensini, r =0.82, P <0.001; and Syntax, r =0.825, P <0.001). The cutoff thickness value to predict critical disease was 5.75 mm (area under the curve, 0.875; 95% confidence interval, 0.825–0.926; P <0.001).Epicardial adipose tissue thickness is independently related to coronary artery disease extent and complexity as denoted by Gensini and Syntax scores, and it predicts critical coronary artery disease.  相似文献   
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