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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年 第16卷 第3期   页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost     deep neural network     healthcare     risk prediction    

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Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov

《工程管理前沿（英文）》 doi: 10.1007/s42524-024-0082-1

摘要： The accurate estimation of geological risks is essential for preventing geohazards, and ensuring efficient and safe construction processes. This study proposes a method, the online hidden Markov model (OHMM), which combines online learning with the hidden Markov model to estimate geological risks. The OHMM is tailored for the continuous nature of observational data, allowing it to adaptively update with each new piece of data. To address the challenge of limited data in the early stages of construction, we use pre-construction borehole samples as additional data. This approach extends the short sequence of observed data to match the length of a complete sequence through an observation extension mechanism. The effectiveness of the OHMM, equipped with this observation extension mechanism, is demonstrated in a case study that models geological risks for a tunnel excavation project in Singapore. The OHMM outperforms traditional methods, including the hidden Markov model, long short-term memory network, neural network, and support vector machine, in predicting geological risks ahead of the tunnel boring machine. Notably, the OHMM can accurately forecast geological risks in areas yet to be constructed, using limited observational and site investigation data. This research advances geological risk prediction models by offering an online updating capability for tunnel excavation and construction projects. It enables early-stage risk prediction and provides long-term forecasts with minimal historical data requirements, maximizing the use of site investigation data.

关键词： geological risk prediction     machine learning     online learning     hidden Markov model     borehole logging    

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SinoSCORE: a logistically derived additive prediction model for post-coronary artery bypass grafting

null

《医学前沿（英文）》 2013年 第7卷 第4期   页码 477-485 doi: 10.1007/s11684-013-0284-0

摘要：

This study aims to construct a logistically derived additive score for predicting in-hospital mortality risk in Chinese patients undergoing coronary artery bypass surgery (CABG). Data from 9839 consecutive CABG patients in 43 Chinese centers were collected between 2007 and 2008 from the Chinese Coronary Artery Bypass Grafting Registry. This database was randomly divided into developmental and validation subsets (9:1). The data in the developmental dataset were used to develop the model using logistic regression. Calibration and discrimination characteristics were assessed using the validation dataset. Thresholds were defined for each model to distinguish different risk groups. After excluding 275 patients with incomplete information, the overall mortality rate of the remaining 9564 patients was 2.5%. The SinoSCORE model was constructed based on 11 variables: age, preoperative NYHA stage III or IV, chronic renal failure, extracardiac arteriopathy, chronic obstructive pulmonary disease, preoperative atrial fibrillation or flutter (within 2βweeks), left ventricular ejection fraction, other elective surgery, combined valve procedures, preoperative critical state, and BMI. In the developmental dataset, calibration using a Hosmer-Lemeshow (HL) test was at =β0.44 and discrimination based on the area under the receiver operating characteristic curve (ROC) was 0.80. In the validation dataset, the HL test was at =β0.34 and the area under the ROC (AUC) was 0.78. A logistically derived additive model for predicting in-hospital mortality among Chinese patients undergoing CABG was developed based on the most up-to-date multi-center data from China.

关键词： coronary artery bypass grafting     risk stratification     in-hospital mortality    

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高速公路车辆行驶中基于可达性的置信度感知碰撞概率检测

Xinwei Wang, Zirui Li, Javier Alonso-Mora, Meng Wang

《工程（英文）》 2024年 第33卷 第2期   页码 90-107 doi: 10.1016/j.eng.2023.10.010

摘要：

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles. Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions. However, they suffer from over-conservatism, potentially resulting in false–positive risk events in complicated real-world applications. In this paper, we combine two reachability analysis techniques, a backward reachable set (BRS) and a stochastic forward reachable set (FRS), and propose an integrated probabilistic collision–detection framework for highway driving. Within this framework, we can first use a BRS to formally check whether a two-vehicle interaction is safe; otherwise, a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step. Thus, the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety–critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidenceaware dynamic belief to improve the prediction accuracy. Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data. The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising for real-world applications.

关键词： Probabilistic collision detection     Confidence awareness     Probabilistic acceleration prediction     Reachability analysis     Risk assessment    

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灰色预测模型GM（１，１）的适用性分析及在火灾风险预测中的应用

陈子锦,王福亮,陆守香

《中国工程科学》 2007年 第9卷 第5期   页码 91-94

摘要：

通过对灰色预测模型———GM（１，１）的理论分析，证明了该模型的预测值及其变化趋势均具有单调 性，进而提出了GM（１，１）模型的适用性判据，并给出了该判据在火灾风险灰色预测中的应用实例。

关键词： 火灾预测     GM(1     1)模型     火灾伤人率    

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Understanding and addressing the environmental risk of microplastics

《环境科学与工程前沿（英文）》 2023年 第17卷 第1期 doi: 10.1007/s11783-023-1612-5

摘要：

Over the past decades, the plastic production has been dramatically increased. Indeed, a category of small plastic particles mainly with the shapes of fragments, fibers, or spheres, called microplastics (particles smaller than 5 mm) and nanoplastics (particles smaller than 1 μm) have attracted particular attention. Because of its wide distribution in the environment and potential adverse effects to animal and human, microplastic pollution has been reported as a serious environment problem receiving increased attention in recent years. As one of the commonly detected emerging contaminants in the environment, recent evidence indicates that the concentration of microplastics show an increasing trend, for the reason that up to 12.7 million metric tons of plastic litter is released into aquatic environment from land-based sources each year. Furthermore, microplastic exposure levels of model organisms in laboratory studies are usually several orders of magnitude higher than those found in environment, and the microplastics exposure conditions are also different with those observed in the environment. Additionally, the detection of microplastics in feces indicates that they can be excreted out of the bodies of animal and human. Hence, great uncertainties might exist in microplastics exposure and health risk assessment based on current studies, which might be exaggerated. Policies reduce microplastic emission sources and hence minimize their environmental risks are determined. To promote the above policies, we must first overcome the technical obstacles of detecting microplastics in various samples.

关键词： Emerging contaminants     Microplastics     Environment risk     Health effect    

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Spatial prediction of soil contamination based on machine learning: a review

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1693-1

摘要：

● A review of machine learning (ML) for spatial prediction of soil contamination.

关键词： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Ecological Risk Management of Drinking Water Project: The Case Study of Kunming City

Ji-liang Zheng,Jun Hu,Xuan Zhou,Ching Yuen Luk

《工程管理前沿（英文）》 2015年 第2卷 第3期   页码 311-319 doi: 10.15302/J-FEM-2015045

摘要： Following rapid infrastructure development and industrialization, the problems of water pollution and water shortage have become more severe. Whether there is safe drinking water in cities has attracted wide attention. The ecological risk management of drinking water project is an important means of ensuring the safety of a drinking water source. Based on ecological risk assessment and management theories, this paper establishes an ecological risk management model and assessment system with the aim of providing theoretical guidance and scientific basis for formulating a policy on the safety and protection of drinking water sources in a city. Kunming is one of the cities plagued by severe water shortage in China. Its ecological risk management of drinking water has attracted the attention of both the local government and the public. Using Kunming as the case study, this paper conducts a comparative analysis and assessment on three major reservoirs that face ecological risks. It highlights the existing problems and gives helpful suggestions.

关键词： drinking water project     ecological risk     ecological risk assessment     risk management    

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Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

《环境科学与工程前沿（英文）》 2023年 第17卷 第12期 doi: 10.1007/s11783-023-1752-7

摘要：

● Online learning models accurately predict influent flow rate at wastewater plants.

关键词： Wastewater prediction     Data stream     Online learning     Batch learning     Influent flow rates    

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风险矩阵方法与应用述评

朱启超,匡兴华,沈永平

《中国工程科学》 2003年 第5卷 第1期   页码 89-94

摘要：

技术项目的风险管理一直深受美国国防部的重视。介绍了在美国国防采办风险管理中广泛应用的风险矩阵方法，并对其优缺点和适用性进行分析，结合我国国防预研技术项目管理的特点，提出我国开展技术性项目风险管理的思路。

关键词： 风险矩阵     风险管理     项目管理    

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Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

《能源前沿（英文）》 2016年 第10卷 第4期   页码 479-488 doi: 10.1007/s11708-016-0425-7

摘要： In this paper a novel method for reliability prediction and validation of nuclear power units in service is proposed. The equivalent availability factor is used to measure the reliability, and the equivalent availability factor deducting planed outage hours from period hours and maintenance factor are used for the measurement of inherent reliability. By statistical analysis of historical reliability data, the statistical maintenance factor and the undetermined parameter in its numerical model can be determined. The numerical model based on the maintenance factor predicts the equivalent availability factor deducting planed outage hours from period hours, and the planed outage factor can be obtained by using the planned maintenance days. Using these factors, the equivalent availability factor of nuclear power units in the following 3 years can be obtained. Besides, the equivalent availability factor can be predicted by using the historical statistics of planed outage factor and the predicted equivalent availability factor deducting planed outage hours from period hours. The accuracy of the reliability prediction can be evaluated according to the comparison between the predicted and statistical equivalent availability factors. Furthermore, the reliability prediction method is validated using the nuclear power units in North American Electric Reliability Council (NERC) and China. It is found that the relative errors of the predicted equivalent availability factors for nuclear power units of NERC and China are in the range of –2.16% to 5.23% and –2.15% to 3.71%, respectively. The method proposed can effectively predict the reliability index in the following 3 years, thus providing effective reliability management and maintenance optimization methods for nuclear power units.

关键词： nuclear power units in service     reliability     reliability prediction     equivalent availability factors    

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Position-varying surface roughness prediction method considering compensated acceleration in milling

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 855-867 doi: 10.1007/s11465-021-0649-z

摘要： Machined surface roughness will affect parts’ service performance. Thus, predicting it in the machining is important to avoid rejects. Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing. Aiming at surface roughness prediction in the machining process, this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis. To reduce the stochastic error of measuring the machined surface profile height, the surface area is repeatedly measured three times, and Pauta criterion is adopted to eliminate abnormal points. The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model. Seven acceleration features are extracted, and valley, which has the highest R-square proving the effectiveness of the filtering features, is selected as the input of the prediction model by mutual information coefficients. Finally, by comparing the measured and predicted surface roughness curves, they have the same trends, with the average error of 16.28% and the minimum error of 0.16%. Moreover, the prediction curve matches and agrees well with the actual surface state, which verifies the accuracy and reliability of the model.

关键词： surface roughness prediction     compensated acceleration     milling     thin-walled workpiece    

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Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

《机械工程前沿（英文）》 2010年 第5卷 第2期   页码 171-175 doi: 10.1007/s11465-009-0091-0

摘要： Trend prediction technology is the key technology to achieve condition-based maintenance of mechanical equipment. Large-sized water injection units are key equipment in oilfields. The traditional preventive maintenance is not economical and cannot completely avoid vicious accidents. To ensure the normal operation of units and save maintenance costs, trend prediction technology is studied to achieve condition-based maintenance for water injection units. The main methods of the technology are given, the trend prediction method based on neural network is put forward, and the expert system based on the knowledge is developed. The industrial site verification shows that the proposed trend prediction technology can reflect the operating condition trend change of the water injection units and provide technical means to achieve condition-based predictive maintenance.

关键词： water injection units     condition-based maintenance     trend prediction    

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

《结构与土木工程前沿（英文）》 2013年 第7卷 第1期   页码 72-82 doi: 10.1007/s11709-013-0185-y

摘要： A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

关键词： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

《化学科学与工程前沿（英文）》 2022年 第16卷 第4期   页码 523-535 doi: 10.1007/s11705-021-2083-5

摘要： Solubility has been widely regarded as a fundamental property of small molecule drugs and drug candidates, as it has a profound impact on the crystallization process. Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallization process efficiency, has attracted increasing attention. However, there are still many urgent challenges thus far. Herein we used seven descriptors based on understanding dissolution behavior to establish two solubility prediction models by machine learning algorithms. The solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the prediction models, which were constructed by random decision forests and artificial neural network with optimized data structure and model accuracy. Furthermore, a comparison with traditional prediction methods including the modified solubility equation and the quantitative structure-property relationships model was carried out. The highest accuracy shown by the testing set proves that the ML models have the best solubility prediction ability. Multiple linear regression and stepwise regression were used to further investigate the critical factor in determining solubility value. The results revealed that the API properties and the solute-solvent interaction both provide a nonnegligible contribution to the solubility value.

关键词： solubility prediction     machine learning     artificial neural network     random decision forests    

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标题 作者 时间 类型 操作

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

期刊论文

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov

期刊论文

SinoSCORE: a logistically derived additive prediction model for post-coronary artery bypass grafting

null

期刊论文

高速公路车辆行驶中基于可达性的置信度感知碰撞概率检测

Xinwei Wang, Zirui Li, Javier Alonso-Mora, Meng Wang

期刊论文

灰色预测模型GM（１，１）的适用性分析及在火灾风险预测中的应用

陈子锦,王福亮,陆守香

期刊论文

Understanding and addressing the environmental risk of microplastics

期刊论文

Spatial prediction of soil contamination based on machine learning: a review

期刊论文

Ecological Risk Management of Drinking Water Project: The Case Study of Kunming City

Ji-liang Zheng,Jun Hu,Xuan Zhou,Ching Yuen Luk

期刊论文

Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

期刊论文

风险矩阵方法与应用述评

朱启超,匡兴华,沈永平

期刊论文

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

期刊论文

Position-varying surface roughness prediction method considering compensated acceleration in milling

期刊论文

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

期刊论文

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

期刊论文

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

期刊论文