DECISION SUPPORT SYSTEMS决策支持系统
DECISION SUPPORT SYSTEMS(英文缩写 DECIS SUPPORT SYST),ISSN 0167-9236,eISSN 1873-5797,中文译名:决策支持系统 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 6.969 | Q1 |
| 2022 | 7.500 | Q1 |
| 2023 | 6.700 | Q1 |
| 2024 | 6.800 | Q1 |
| 2025 | 7.500 | Q1 |
DECISION SUPPORT SYSTEMS 最新收录文献
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1. Re-evaluating causal inference: Bias reduction in confounder-effect modifier scenarios.
PMID:日期:2025-03-18Propensity Score Matching (PSM) is a widely used method for estimating causal treatment effects, but its performance can be limited in complex scenarios. This paper examines cases where a confounder also serves as an effect modifier and compares the bias-reduction performance of PSM with Inverse Probability Weighting (IPW). Using the University of California, Berkeley graduate admission data as an illustrative example, we show that PSM can produce biased estimates of the Average Treatment Effect (ATE) in such contexts. Through a simulation study, we demonstrate that PSM generally fails to adequately reduce bias for the ATE when a confounder is also an effect modifier, while IPW yields less biased estimates with lower Mean Squared Error (MSE). To validate these findings in a more real-world setting, we analyse data generated from a well-known matched-pairs experimental study of Mexico's Seguro Popular de Salud (Universal Health Insurance) Program. From this experiment we derive observational data that incorporates confounders and effect modifiers and compare the performance of PSM and IPW estimators. Our results confirm that IPW consistently provides more accurate and reliable estimates of the ATE, with smaller bias, compared to PSM.
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2. Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic.
PMID:日期:2023-04-24Managing an extreme event like a healthcare disaster requires accurate information about the event's circumstances to comprehend the full consequences of acting. However, information quality is rarely optimal since it takes time to determine the information of relevance. The COVID-19 pandemic showed that even official data sources are far from optimal since they suffer from reporting delays that slow decision-making. To support decision-makers with timely information, we utilize data from online social networks to propose an adaptable information extraction solution to create indices helping to forecast COVID-19 case numbers and hospitalization rates. We show that combining heterogeneous data sources like Twitter and Reddit can leverage these sources' inherent complementarity and yield better predictions than those using a single data source alone. We further show that the predictions run ahead of the official COVID-19 incidences by up to 14 days. Additionally, we highlight the importance of model adjustments whenever new information becomes available or the underlying data changes by observing distinct changes in the presence of specific symptoms on Reddit.
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3. Data analytics and decision-making systems: Implications of the global outbreaks.
PMID:日期:2022-08-11该文献暂无摘要。
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4. Victim crisis communication strategy on digital media: A study of the COVID-19 pandemic.
4. 数字媒体上的受害者危机沟通策略：一项关于COVID-19大流行的研究PMID:日期:2022-06-19The COVID-19 pandemic and the lockdown bore a devastating impact on organizations across the globe. In this crisis, organizations belonged to the victim cluster, with a low crisis responsibility. Nevertheless, organizations needed to strategize their crisis responses and communicate with stakeholders to reduce the threat to reputational capital and manage stakeholder reactions in the pandemic. In this paper, we studied organizational Twitter communication during the COVID-19 crisis through the lens of the situational crisis communication theory (SCCT). We analyzed 325,627 tweets collected from the Twitter pages of 464 organizations belonging to the Fortune 500 list. The Twitter data reflected organizational COVID-19 crisis response strategies and demonstrated organizational use of Twitter for crisis communication. We applied lexicon-based emotion mining to identify and measure emotions, and topic mining to measure crisis response topic scores from this large multi-organization dataset. We performed path analysis to test our research model derived from the SCCT. The analysis showed that instructing and adjusting information can minimize threats to organizational reputation in a victim crisis and manage stakeholder reactions. Positive emotions showed a stronger association with behavioral outcomes. Emotion neutral tweets generated more favorable stakeholder reactions. The paper contributes to the literature on situational crisis communication for a victim crisis. The multi-organization data addresses the sensitive inter-organization dependencies and improves the understanding of crisis communication. It provides practitioners an insight into the effect of the COVID-19 crisis response strategies on stakeholder emotions and behavior.
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5. {"_":"Financial distress prediction using integrated -score and multilayer perceptron neural networks.","i":["Z"]}
5. {"_":"使用集成评分和多层感知器神经网络的财务困境预测。","i":["Z"]}PMID:日期:2022-05-26The COVID-19 pandemic led to a great deal of financial uncertainty in the stock market. An initial drop in March 2020 was followed by unexpected rapid growth over 2021. Therefore, financial risk forecasting continues to be a central issue in financial planning, dealing with new types of uncertainty. This paper presents a stock market forecasting model combining a multi-layer perceptron artificial neural network (MLP-ANN) with the traditional Altman -Score model. The contribution of the paper is presentation of a new hybrid enterprise crisis warning model combining Z-score and MLP-ANN models. The new hybrid default prediction model is demonstrated using Chinese data. The results of empirical analysis show that the average correct classification rate of thew hybrid neural network model (99.40%) is higher than that of the Altman -score model (86.54%) and of the pure neural network method (98.26%). Our model can provide early warning signals of a company's deteriorating financial situation to managers and other related personnel, investors and creditors, government regulators, financial institutions and analysts and others so that they can take timely measures to avoid losses.
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6. Mining voices from self-expressed messages on social-media: Diagnostics of mental distress during COVID-19.
6. 从社交媒体上的自我表达信息中挖掘声音：COVID-19期间心理困扰的诊断PMID:日期:2022-05-06The COVID-19 pandemic has had a severe impact on mankind, causing physical suffering and deaths across the globe. Even those who have not contracted the virus have experienced its far-reaching impacts, particularly on their mental health. The increased incidences of psychological problems, anxiety associated with the infection, social restrictions, economic downturn, etc., are likely to aggravate with the virus spread and leave a longer impact on humankind. These reasons in aggregation have raised concerns on mental health and created a need to identify novel precursors of depression and suicidal tendencies during COVID-19. Identifying factors affecting mental health and causing suicidal ideation is of paramount importance for timely intervention and suicide prevention. This study, thus, bridges this gap by utilizing computational intelligence and Natural Language Processing (NLP) to unveil the factors underlying mental health issues. We observed that the pandemic and subsequent lockdown anxiety emerged as significant factors leading to poor mental health outcomes after the onset of COVID-19. Consistent with previous works, we found that psychological disorders have remained pre-eminent. Interestingly, financial burden was found to cause suicidal ideation before the pandemic, while it led to higher odds of depressive (non-suicidal) thoughts for individuals who lost their jobs. This study offers significant implications for health policy makers, governments, psychiatric practitioners, and psychologists.
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7. Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning.
7. 信息时效性和丰富性对新冠疫情期间社交媒体公众参与度的影响：基于自然语言处理和机器学习的实证研究PMID:日期:2022-02-12This paper investigates how information timeliness and richness affect public engagement using text data from China's largest social media platform during times of the COVID-19 pandemic. We utilize a similarity calculation method based on natural language processing (NLP) and text mining to evaluate three dimensions of information timeliness: retrospectiveness, immediateness, and prospectiveness. Public engagement is divided into breadth and depth. The empirical results show that information retrospectiveness is negatively associated with public engagement breadth but positively with depth. Both information immediateness and prospectiveness improved the breadth and depth of public engagement. Interestingly, information richness has a positive moderating effect on the relationships between information retrospectiveness, prospectiveness, and public engagement breadth but no significant effects on immediateness; meanwhile, it has a negative moderating effect on the relationship between retrospectiveness and depth but a positive effect on immediateness, prospectiveness. In the extension analysis, we constructed a supervised NLP model to identify and classify health emergency-related information (epidemic prevention and help-seeking) automatically. We find that public engagement differs in the two emergency-related information categories. The findings can promote a more responsive public health strategy that magnifies the transfer speed for critical information and mitigates the negative impacts of information uncertainty or false information.
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8. IT-business alignment, big data analytics capability, and strategic decision-making: Moderating roles of event criticality and disruption of COVID-19.
8. IT与业务对齐、大数据分析能力与战略决策：COVID-19事件关键性和颠覆性的调节作用PMID:日期:2022-02-07Prior research has confirmed the importance of IT-business alignment (ITBA) and big data analytics capability (BDAC) in supporting firms' strategic decision-making under normal circumstances. However, the global outbreak of COVID-19 has significantly changed firms' strategic decision-making landscapes and raised questions regarding the effects of ITBA and BDAC on strategic decision-making as conditioned by COVID-19 characteristics. In this study, we contextualize two important event impact factors (i.e., event criticality and event disruption) in the context of COVID-19 and examine their contingent roles in the effects of ITBA and BDAC on strategic decision-making. Our analyses, based on two-round, multi-respondents matched survey data collected from 175 Chinese firms to elucidate the differential moderating roles of event criticality and disruption of COVID-19 in the impact of ITBA and BDAC on strategic decision speed and quality. The results indicate the event criticality of COVID-19 strengthens the effects of ITBA on decision speed and quality but weakens the influence of BDAC on decision quality. Meanwhile, the event disruption of COVID-19 weakens the influence of ITBA on decision speed and quality but strengthens the effect of BDAC on decision speed and quality. These findings have important theoretical and practical implications, which we discuss in the conclusion.
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9. An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions.
9. 研究大流行病的一个可解释机器学习框架：以COVID-19急诊科再入院为例PMID:日期:2022-01-18One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory-predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies.
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10. A decision analytic approach for social distancing policies during early stages of COVID-19 pandemic.
10. COVID-19大流行早期阶段保持社交距离政策的决策分析方法PMID:日期:2021-06-26The COVID-19 pandemic has become a crucial public health problem in the world that disrupted the lives of millions in many countries including the United States. In this study, we present a decision analytic approach which is an efficient tool to assess the effectiveness of early social distancing measures in communities with different population characteristics. First, we empirically estimate the reproduction numbers for two different states. Then, we develop an age-structured compartmental simulation model for the disease spread to demonstrate the variation in the observed outbreak. Finally, we analyze the computational results and show that early trigger social distancing strategies result in smaller death tolls; however, there are relatively larger second waves. Conversely, late trigger social distancing strategies result in higher initial death tolls but relatively smaller second waves. This study shows that decision analytic tools can help policy makers simulate different social distancing scenarios at the early stages of a global outbreak. Policy makers should expect multiple waves of cases as a result of the social distancing policies implemented when there are no vaccines available for mass immunization and appropriate antiviral treatments.