Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the...Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the rainfall scoring rules of China Meteorological Administration. The verification results show that the average score of annual precipitation prediction in recent six years is higher than that made by a professional forecaster, so this model has a good prospect of application. Moreover, the level of making prediction is steady, and it can be widely used in long-term prediction of rainfall.展开更多
Background: Risk stratification of long-term outcomes for patients undergoing Coronary artery bypass grafting has enormous potential clinical importance. Aim: To develop risk stratification models for predicting long-...Background: Risk stratification of long-term outcomes for patients undergoing Coronary artery bypass grafting has enormous potential clinical importance. Aim: To develop risk stratification models for predicting long-term outcomes following coronary artery bypass graft (CABG) surgery. Methods: We retrospectively revised the electronic medical records of 2330 patients who underwent adult Cardiac surgery between August 2016 and December 2022 at Madinah Cardiac Center, Saudi Arabia. Three hundred patients fulfilled the eligibility criteria of CABG operations with a complete follow-up period of at least 24 months, and data reporting. The collected data included patient demographics, comorbidities, laboratory data, pharmacotherapy, echocardiographic parameters, procedural details, postoperative data, in-hospital outcomes, and follow-up data. Our follow-up was depending on the clinical status (NYHA class), chest pain recurrence, medication dependence and echo follow-up. A univariate analysis was performed between each patient risk factor and the long-term outcome to determine the preoperative, operative, and postoperative factors significantly associated with each long-term outcome. Then a multivariable logistic regression analysis was performed with a stepwise, forward selection procedure. Significant (p < 0.05) risk factors were identified and were used as candidate variables in the development of a multivariable risk prediction model. Results: The incidence of all-cause mortality during hospital admission or follow-up period was 2.3%. Other long-term outcomes included all-cause recurrent hospitalization (9.8%), recurrent chest pain (2.4%), and the need for revascularization by using a stent in 5 (3.0%) patients. Thirteen (4.4%) patients suffered heart failure and they were on the maximum anti-failure medications. The model for predicting all-cause mortality included the preoperative EF ≤ 35% (AOR: 30.757, p = 0.061), the bypass time (AOR: 1.029, p = 0.003), and the duration of ventilation following the operation (AOR: 1.237, p = 0.021). The model for risk stratification of recurrent hospitalization comprised the preoperative EF ≤ 35% (AOR: 6.198, p p = 0.023), low postoperative cardiac output (AOR: 3.622, p = 0.007), and the development of postoperative atrial fibrillation (AOR: 2.787, p = 0.038). Low postoperative cardiac output was the only predictor that significantly contributed to recurrent chest pain (AOR: 11.66, p = 0.004). Finally, the model consisted of low postoperative cardiac output (AOR: 5.976, p < 0.001) and postoperative ventricular fibrillation (AOR: 4.216, p = 0.019) was significantly associated with an increased likelihood of the future need for revascularization using a stent. Conclusions: A risk prediction model was developed in a Saudi cohort for predicting all-cause mortality risk during both hospital admission and the follow-up period of at least 24 months after isolated CABG surgery. A set of models were also developed for predicting long-term risks of all-cause recurrent hospitalization, recurrent chest pain, heart failure, and the need for revascularization by using stents.展开更多
Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to...Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.展开更多
目的:鉴于脓毒症的高发病率和高病死率,早期识别高风险患者并及时干预至关重要,而现有死亡风险预测模型在操作、适用性和预测长期预后等方面均存在不足。本研究旨在探讨脓毒症患者死亡的危险因素,构建近期和远期死亡风险预测模型。方法...目的:鉴于脓毒症的高发病率和高病死率,早期识别高风险患者并及时干预至关重要,而现有死亡风险预测模型在操作、适用性和预测长期预后等方面均存在不足。本研究旨在探讨脓毒症患者死亡的危险因素,构建近期和远期死亡风险预测模型。方法:从美国重症监护医学信息数据库IV(Medical Information Mart for Intensive Care-IV,MIMIC-IV)中选取符合脓毒症3.0诊断标准的人群,按7?3的比例随机分为建模组和验证组,分析患者的基线资料。采用单因素Cox回归分析和全子集回归确定脓毒症患者死亡的危险因素并筛选出构建预测模型的变量。分别用时间依赖性曲线下面积(area under the curve,AUC)、校准曲线和决策曲线评估模型的区分度、校准度和临床实用性。结果:共纳入14240例脓毒症患者,28 d和1年病死率分别为21.45%(3054例)和36.50%(5198例)。高龄、女性、高感染相关器官衰竭评分(sepsis-related organ failure assessment,SOFA)、高简明急性生理学评分(simplified acute physiology score II,SAPS II)、心率快、呼吸频率快、脓毒症休克、充血性心力衰竭、慢性阻塞性肺疾病、肝脏疾病、肾脏疾病、糖尿病、恶性肿瘤、高白细胞计数(white blood cell count,WBC)、长凝血酶原时间(prothrombin time,PT)、高血肌酐(serum creatinine,SCr)水平均为脓毒症死亡的危险因素(均P<0.05)。由PT、呼吸频率、体温、合并恶性肿瘤、合并肝脏疾病、脓毒症休克、SAPS II及年龄8个变量构建的模型,其28 d和1年生存的AUC分别为0.717(95%CI 0.710~0.724)和0.716(95%CI 0.707~0.725)。校准曲线和决策曲线表明该模型具有良好的校准度及较好的临床应用价值。结论:基于MIMIC-IV建立的脓毒症患者近期和远期死亡风险预测模型有较好的识别能力,对患者预后风险评估及干预治疗具有一定的临床参考意义。展开更多
In this paper, we calculated the seismic pattern of instrumental recorded small and moderate earthquakes near the epicenter of the 1303 Hongtong M=8 earthquake, Shanxi Province. According to the spatial distribution o...In this paper, we calculated the seismic pattern of instrumental recorded small and moderate earthquakes near the epicenter of the 1303 Hongtong M=8 earthquake, Shanxi Province. According to the spatial distribution of small and moderate earthquakes, 6 seismic dense zones are delineated. Temporal distribution of ML2 earthquakes since 1970 in each seismic dense zone has been analyzed. Based on temporal distribution characteristics and historical earthquake activity, three types of seismicities are proposed. The relationship between seismic types and crustal medium is analyzed. The mechanism of three types is discussed. Finity of strong earthquake recurrence is pro-posed. Seismic hazard in mid-long term and diversity of earthquake disaster in Shanxi seismic belt are discussed.展开更多
基金Supported by the Major State Basic Research Development Program("973"Program)(2012CB956204)Special Project for Climate Change of China Meteorological Administration(CCSF2011-4)
文摘Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the rainfall scoring rules of China Meteorological Administration. The verification results show that the average score of annual precipitation prediction in recent six years is higher than that made by a professional forecaster, so this model has a good prospect of application. Moreover, the level of making prediction is steady, and it can be widely used in long-term prediction of rainfall.
文摘Background: Risk stratification of long-term outcomes for patients undergoing Coronary artery bypass grafting has enormous potential clinical importance. Aim: To develop risk stratification models for predicting long-term outcomes following coronary artery bypass graft (CABG) surgery. Methods: We retrospectively revised the electronic medical records of 2330 patients who underwent adult Cardiac surgery between August 2016 and December 2022 at Madinah Cardiac Center, Saudi Arabia. Three hundred patients fulfilled the eligibility criteria of CABG operations with a complete follow-up period of at least 24 months, and data reporting. The collected data included patient demographics, comorbidities, laboratory data, pharmacotherapy, echocardiographic parameters, procedural details, postoperative data, in-hospital outcomes, and follow-up data. Our follow-up was depending on the clinical status (NYHA class), chest pain recurrence, medication dependence and echo follow-up. A univariate analysis was performed between each patient risk factor and the long-term outcome to determine the preoperative, operative, and postoperative factors significantly associated with each long-term outcome. Then a multivariable logistic regression analysis was performed with a stepwise, forward selection procedure. Significant (p < 0.05) risk factors were identified and were used as candidate variables in the development of a multivariable risk prediction model. Results: The incidence of all-cause mortality during hospital admission or follow-up period was 2.3%. Other long-term outcomes included all-cause recurrent hospitalization (9.8%), recurrent chest pain (2.4%), and the need for revascularization by using a stent in 5 (3.0%) patients. Thirteen (4.4%) patients suffered heart failure and they were on the maximum anti-failure medications. The model for predicting all-cause mortality included the preoperative EF ≤ 35% (AOR: 30.757, p = 0.061), the bypass time (AOR: 1.029, p = 0.003), and the duration of ventilation following the operation (AOR: 1.237, p = 0.021). The model for risk stratification of recurrent hospitalization comprised the preoperative EF ≤ 35% (AOR: 6.198, p p = 0.023), low postoperative cardiac output (AOR: 3.622, p = 0.007), and the development of postoperative atrial fibrillation (AOR: 2.787, p = 0.038). Low postoperative cardiac output was the only predictor that significantly contributed to recurrent chest pain (AOR: 11.66, p = 0.004). Finally, the model consisted of low postoperative cardiac output (AOR: 5.976, p < 0.001) and postoperative ventricular fibrillation (AOR: 4.216, p = 0.019) was significantly associated with an increased likelihood of the future need for revascularization using a stent. Conclusions: A risk prediction model was developed in a Saudi cohort for predicting all-cause mortality risk during both hospital admission and the follow-up period of at least 24 months after isolated CABG surgery. A set of models were also developed for predicting long-term risks of all-cause recurrent hospitalization, recurrent chest pain, heart failure, and the need for revascularization by using stents.
基金Funding is provided by Taif University Researchers Supporting Project Number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.
文摘目的:鉴于脓毒症的高发病率和高病死率,早期识别高风险患者并及时干预至关重要,而现有死亡风险预测模型在操作、适用性和预测长期预后等方面均存在不足。本研究旨在探讨脓毒症患者死亡的危险因素,构建近期和远期死亡风险预测模型。方法:从美国重症监护医学信息数据库IV(Medical Information Mart for Intensive Care-IV,MIMIC-IV)中选取符合脓毒症3.0诊断标准的人群,按7?3的比例随机分为建模组和验证组,分析患者的基线资料。采用单因素Cox回归分析和全子集回归确定脓毒症患者死亡的危险因素并筛选出构建预测模型的变量。分别用时间依赖性曲线下面积(area under the curve,AUC)、校准曲线和决策曲线评估模型的区分度、校准度和临床实用性。结果:共纳入14240例脓毒症患者,28 d和1年病死率分别为21.45%(3054例)和36.50%(5198例)。高龄、女性、高感染相关器官衰竭评分(sepsis-related organ failure assessment,SOFA)、高简明急性生理学评分(simplified acute physiology score II,SAPS II)、心率快、呼吸频率快、脓毒症休克、充血性心力衰竭、慢性阻塞性肺疾病、肝脏疾病、肾脏疾病、糖尿病、恶性肿瘤、高白细胞计数(white blood cell count,WBC)、长凝血酶原时间(prothrombin time,PT)、高血肌酐(serum creatinine,SCr)水平均为脓毒症死亡的危险因素(均P<0.05)。由PT、呼吸频率、体温、合并恶性肿瘤、合并肝脏疾病、脓毒症休克、SAPS II及年龄8个变量构建的模型,其28 d和1年生存的AUC分别为0.717(95%CI 0.710~0.724)和0.716(95%CI 0.707~0.725)。校准曲线和决策曲线表明该模型具有良好的校准度及较好的临床应用价值。结论:基于MIMIC-IV建立的脓毒症患者近期和远期死亡风险预测模型有较好的识别能力,对患者预后风险评估及干预治疗具有一定的临床参考意义。
基金Key Science Research Project (100501-05-09) from China Earthquake Administration during the tenth Five-year Plan.
文摘In this paper, we calculated the seismic pattern of instrumental recorded small and moderate earthquakes near the epicenter of the 1303 Hongtong M=8 earthquake, Shanxi Province. According to the spatial distribution of small and moderate earthquakes, 6 seismic dense zones are delineated. Temporal distribution of ML2 earthquakes since 1970 in each seismic dense zone has been analyzed. Based on temporal distribution characteristics and historical earthquake activity, three types of seismicities are proposed. The relationship between seismic types and crustal medium is analyzed. The mechanism of three types is discussed. Finity of strong earthquake recurrence is pro-posed. Seismic hazard in mid-long term and diversity of earthquake disaster in Shanxi seismic belt are discussed.