Healthcare information systems: data mining methods in the creation of a clinical recommender system

Recommender systems have been extensively studied to present items, such as movies, music and books that are likely of interest to the user. Researchers have indicated that integrated medical information systems are becoming an essential part of the modern healthcare systems. Such systems have evolved to an integrated enterprise-wide system. In particular, such systems are considered as a type of enterprise information systems or ERP system addressing healthcare industry sector needs. As part of efforts, nursing care plan recommender systems can provide clinical decision support, nursing education, clinical quality control, and serve as a complement to existing practice guidelines. We propose to use correlations among nursing diagnoses, outcomes and interventions to create a recommender system for constructing nursing care plans. In the current study, we used nursing diagnosis data to develop the methodology. Our system utilises a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items. Unlike common commercial systems, our system makes sequential recommendations based on user interaction, modifying a ranked list of suggested items at each step in care plan construction. We rank items based on traditional association-rule measures such as support and confidence, as well as a novel measure that anticipates which selections might improve the quality of future rankings. Since the multi-step nature of our recommendations presents problems for traditional evaluation measures, we also present a new evaluation method based on average ranking position and use it to test the effectiveness of different recommendation strategies.     

A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning

The performance of portfolio model can be improved by introducing stock prediction based on machine learning methods. However, the prediction error is inevitable, which may bring losses to investors. To limit the losses, a common strategy is diversification, which involves buying low-correlation stocks and spreading the funds across different assets. In this paper, a diversified portfolio selection method based on stock prediction is proposed, which includes two stages. To be specific, the purpose of the first stage is to select diversified stocks with high predicted returns, where the returns are predicted by machine learning methods, i.e. random forest (RF), support vector regression (SVR), long short-term memory networks (LSTM), extreme learning machine (ELM) and back propagation neural network (BPNN), and the diversification level is measured by Pearson correlation coefficient. In the second stage, the predictive results are incorporated into a modified mean–variance (MMV) model to determine the proportion of each asset. Using China Securities 100 Index component stocks as study sample, the empirical results demonstrate that the RF+MMV model achieves better results than similar counterparts and market index in terms of return and return–risk metrics.     

基于自适应学习的地块定价策略研究

基于最小二乘法的自适应地块价值评估策略,本文将机器学习模型与专家经验提炼的逻辑框架相结合,通过机器学习去全量化、自动化学习框架参数可以有效提高定价精度,改进后的定价系统可以达到商用级别精度(90％以上),较专家定价,具有成本低、效率高、精度强的优点,较纯机器学习模型定价,高质量样本量的需求大幅减少,达到低成本、高效率、...     

Identification of approximately duplicate material records in ERP systems

The quality of master data is crucial for the accurate functioning of the various modules of an enterprise resource planning (ERP) system. This study addresses specific data problems arising from the generation of approximately duplicate material records in ERP databases. Such problems are mainly due to the firm’s lack of unique and global identifiers for the material records, and to the arbitrary assignment of alternative names for the same material by various users. Traditional duplicate detection methods are ineffective in identifying such approximately duplicate material records because these methods typically rely on string comparisons of each field. To address this problem, a machine learning-based framework is developed to recognise semantic similarity between strings and to further identify and reunify approximately duplicate material records – a process referred to as de-duplication in this article. First, the keywords of the material records are extracted to form vectors of discriminating words. Second, a machine learning method using a probabilistic neural network is applied to determine the semantic similarity between these material records. The approach was evaluated using data from a real case study. The test results indicate that the proposed method outperforms traditional algorithms in identifying approximately duplicate material records.     

Automatic Interpretable Retail forecasting with promotional scenarios

Budgeting and planning processes require medium-term sales forecasts with marketing scenarios. The complexity in modern retailing necessitates consistent, automatic forecasting and insight generation. Remedies to the high dimensionality problem have drawbacks; black box machine learning methods require voluminous data and lack insights, while regularization may bias causal estimates in interpretable models.The proposed FAIR (Fully Automatic Interpretable Retail forecasting) method supports the retail planning process with multi-step-ahead category-store level forecasts, scenario evaluations, and insights. It considers category-store specific seasonality, focal- and cross-category marketing, and adaptive base sales while dealing with regularization-induced confounding.We show, with three chains from the IRI dataset involving 30 categories, that regularization-induced confounding decreases forecast accuracy. By including focal- and cross-category marketing, as well as random disturbances, forecast accuracy is increased. FAIR is more accurate than the black box machine learning method Boosted Trees and other benchmarks while also providing insights that are in line with the marketing literature.     

The new nurse manager: partner in managing costs and quality

Today's nurse manager is a leader in the 1990s health care delivery system. Collaborating with managers throughout the organization, the nurse manager calls upon skills and advanced education to carry out functions in the areas of clinical systems management, human resource management, environmental management, and financial management. This article describes this health care management role and presents case studies where collaboration with other disciplines was successful.     

基于离散选择模型的推荐系统改进算法

准确地预估用户的点击率,并根据该概率对商品排序以供用户选择在推荐系统领域有着重要的意义。推荐系统中常用的因子分解机等机器学习模型一般只考虑用户选择单个商品的概率,忽略了候选商品之间的相互影响,离散选择模型则考虑将商品候选集作为整体进行考虑。提出了使用深度学习模型来改进离散选择模型,模型使用相对特征层、注意力机制等网络结构帮助深度学习模型进行不同商品间的特征比较,研究结果表明引入离散选择模型的深度学习模型表现优于梯度提升决策树、因子分解机等模型。     

Automatic feature identification and graphical support in rule-based forecasting: a comparison

We examined automatic feature identification and graphical support in rule-based expert systems for forecasting. The rule-based expert forecasting system (RBEFS) includes predefined rules to automatically identify features of a time series and selects the extrapolation method to be used. The system can also integrate managerial judgment using a graphical interface that allows a user to view alternate extrapolation methods two at a time. The use of the RBEFS led to a significant improvement in accuracy compared to equal-weight combinations of forecasts. Further improvement were achieved with the user interface. For 6-year ahead ex ante forecasts, the rule-based expert forecasting system has a median absolute percentage error (MdAPE) 15% less than that of equally weighted combined forecasts and a 33% improvement over the random walk. The user adjusted forecasts had a MdAPE 20% less than that of the expert system. The results of the system are also compared to those of an earlier rule-based expert system which required human judgments about some features of the time series data. The results of the comparison of the two rule-based expert systems showed no significant differences between them.     

Development of an integrated medical supply information system

The integrated medical supply inventory control system introduced in this study is a hybrid system that is shaped by the nature of medical supply, usage and storage capacity limitations of health care facilities. The system links demand, service provided at the clinic, health care service provider's information, inventory storage data and decision support tools into an integrated information system. ABC analysis method, economic order quantity model, two-bin method and safety stock concept are applied as decision support models to tackle inventory management issues at health care facilities. In the decision support module, each medical item and storage location has been scrutinised to determine the best-fit inventory control policy. The pilot case study demonstrates that the integrated medical supply information system holds several advantages for inventory managers, since it entails benefits of deploying enterprise information systems to manage medical supply and better patient services.     

基于多Agent的现代制造企业培训系统研究

针对传统制造企业培训系统中出现的问题,提出了一个基于多Agent的企业培训系统。本文从制造企业培训的要求出发,利用Asp.net技术与多Agent技术相结合原理,实现了员工管理,在线培训、智能推荐、网上考试等功能,满足了企业和员工的需求。系统通过多Agent技术的运用,较好的解决了学习内容推荐的智能化,个性化以及员工职业生涯规划等问题,使网络学习者能在一种交互式的学习环境中得到更人性化的学习推荐服务,从而改善培训效果。     

Comparing Inference Methods for Non‐probability Samples

Social and economic scientists are tempted to use emerging data sources like big data to compile information about finite populations as an alternative for traditional survey samples. These data sources generally cover an unknown part of the population of interest. Simply assuming that analyses made on these data are applicable to larger populations is wrong. The mere volume of data provides no guarantee for valid inference. Tackling this problem with methods originally developed for probability sampling is possible but shown here to be limited. A wider range of model‐based predictive inference methods proposed in the literature are reviewed and evaluated in a simulation study using real‐world data on annual mileages by vehicles. We propose to extend this predictive inference framework with machine learning methods for inference from samples that are generated through mechanisms other than random sampling from a target population. Describing economies and societies using sensor data, internet search data, social media and voluntary opt‐in panels is cost‐effective and timely compared with traditional surveys but requires an extended inference framework as proposed in this article.     

Predicting monthly biofuel production using a hybrid ensemble forecasting methodology

This paper proposes a hybrid ensemble forecasting methodology that integrating empirical mode decomposition (EMD), long short-term memory (LSTM) and extreme learning machine (ELM) for the monthly biofuel (a typical agriculture-related energy) production based on the principle of decomposition—reconstruction—ensemble. The proposed methodology involves four main steps: data decomposition via EMD, component reconstruction via a fine-to-coarse (FTC) method, individual prediction via LSTM and ELM algorithms, and ensemble prediction via a simple addition (ADD) method. For illustration and verification, the biofuel monthly production data of the USA is used as the our sample data, and the empirical results indicate that the proposed hybrid ensemble forecasting model statistically outperforms all considered benchmark models considered in terms of the forecasting accuracy. This indicates that the proposed hybrid ensemble forecasting methodology integrating the EMD-LSTM-ELM models based on the decomposition—reconstruction—ensemble principle has been proved to be a competitive model for the prediction of biofuel production.     

The dynamic financial distress prediction method of EBW-VSTW-SVM

Financial distress prediction (FDP) takes important role in corporate financial risk management. Most of former researches in this field tried to construct effective static FDP (SFDP) models that are difficult to be embedded into enterprise information systems, because they are based on horizontal data-sets collected outside the modelling enterprise by defining the financial distress as the absolute conditions such as bankruptcy or insolvency. This paper attempts to propose an approach for dynamic evaluation and prediction of financial distress based on the entropy-based weighting (EBW), the support vector machine (SVM) and an enterprise’s vertical sliding time window (VSTW). The dynamic FDP (DFDP) method is named EBW-VSTW-SVM, which keeps updating the FDP model dynamically with time goes on and only needs the historic financial data of the modelling enterprise itself and thus is easier to be embedded into enterprise information systems. The DFDP method of EBW-VSTW-SVM consists of four steps, namely evaluation of vertical relative financial distress (VRFD) based on EBW, construction of training data-set for DFDP modelling according to VSTW, training of DFDP model based on SVM and DFDP for the future time point. We carry out case studies for two listed pharmaceutical companies and experimental analysis for some other companies to simulate the sliding of enterprise vertical time window. The results indicated that the proposed approach was feasible and efficient to help managers improve corporate financial management.     

Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?

Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices.     

我国B2C电子商务个性化商品推荐服务实证研究

优质的个性化推荐系统带来了巨大的经济价值和社会价值的同时,也能提高B2C电子商务在当今激烈的市场竞争环境下的存活能力。在简要介绍电子商务个性化推荐系统模型的基础上,详细分析比较了亚马逊、当当网、天猫三家B2C电子商务个性化商品推荐服务,最后对B2C电子商务推荐系统的进一步发展方向进行了展望。     

一种新的地方政府债务风险预警系统设计与应用

本文围绕构建地方政府债务风险预警系统,首先综合运用TOPSIS法和德尔菲法确定了样本的债务风险综合评价值;然后利用支持向量机,提出了基于结构风险最小化的地方政府债务风险预警模型,并将该模型的求解转化为非线性规划仅有线性约束问题,解决了传统方法中忽略模型置信范围、需要样本数量大及过度学习等缺陷。在实证研究中,基于训练样本的模型平均绝对百分精度达99. 69%,基于检验样本的模型平均绝对百分精度达96. 99%,数值结果表明本文所设计的地方政府债务风险预警系统是有效的,可行的。     

Refined analysis and a hierarchical multi-task learning approach for loan fraud detection

Fraud problems in loan application assessment cause significant losses for finance companies worldwide, and much research has focused on machine learning methods to improve the efficacy of fraud detection in some financial domains. However, diverse information falsification in individual fraud remains one of the most challenging problems in loan applications. To this end, we conducted an empirical study to explore the relationships between various fraud types and analyzed the factors influencing information fabrication. Weak relationships exist among different falsification types, and some essential factors play the same roles in different fraud types. In contrast, others have various or opposing effects on these types of frauds. Based on this finding, we propose a novel hierarchical multi-task learning approach to refine fraud-detection systems. Specifically, we first developed a hierarchical fraud category method to break down this problem into several subtasks according to the information types falsified by customers, reducing fraud identification's difficulty. Second, a heterogeneous network with a meta-path-based random walk and heterogeneous skip-gram model can solve the representation learning problem owing to the sophisticated relationships among the applicants' information. Furthermore, the final subtasks can be predicted using a multi-task learning approach with two prediction layers. The first layer provides the probabilities of general fraud categories as auxiliary information for the second layer, which is for specific subtask prediction. Finally, we conducted extensive experiments based on a real-world dataset to demonstrate the effectiveness of the proposed approach.     

Prediction of chronic kidney disease stages by renal ultrasound imaging

ABSTRACT

To detect chronic kidney disease (CKD) at earlier stages, diagnosis through non-invasive ultrasonographic imaging techniques provides an auxiliary clinical approach for at-risk CKD patients. We have established a detection method based on imaging processing techniques and machine learning approaches for the diagnosis of different CKD stages. Decisive area-proportional and textural features and support-vector-machine techniques were applied for efficient and effective analyses. Several clustered collections of CKD patients were evaluated and compared according to the estimated glomerular filtration rates. Based on the findings of evolving changes from ultrasound images, the proposed approach could be used as complementary evidences to help differentiate between different clinical diagnoses.     

基于互生理念的系统工程创新教学模式研究

系统工程是一门综合性、交叉性、实践性非常突出的学科。针对系统工程课程的目的与教学内容,以培养学生的创新思维为目标,文章提出了基于互生理念的系统工程课程创新教学模式。在这个理念指导下,将给教师和学生提供一个引发思考、发现自我的教与学的平台。互生理念是在学生、教师、环境三个要素相互依存、互生互动的情景下产生的,创新教学的实现和创新人才的培养需要借助这样一个互生链条得以实现。通过教学目标设计的创新与教学环节的创新,从而培养出一大批具有批判性思维和系统思维的创新型人才。     

Cyberattack-resilient load forecasting with adaptive robust regression

Cyberattacks in power systems that alter the input data of a load forecasting model have serious, potentially devastating consequences. Existing cyberattack-resilient work focuses mainly on enhancing attack detection. Although some outliers can be easily identified, more carefully designed attacks can escape detection and impact load forecasting. Here, a cyberattack-resilient load forecasting approach based on an adaptive robust regression method is proposed, where the observations are trimmed based on their residuals and the proportion of the trim is adaptively determined by an estimation of the contaminated data proportion. An extensive comparison study shows that the proposed method outperforms the standard robust regression in various settings.