Decision Analytics Research Articles

Causal relationship analysis of high-dimensional time series based on quantile factor model

Finding the causal relationship between variables from observed data is a key issue in many decision analytics researches. Because the traditional Granger causality model is affected by the curse of dimensionality, it is difficult to accurately find causality in high-dimensional time series. To address this problem, we propose a new Granger causality analysis method based on quantile factor model, named QFM-CGC. First, QFM-CGC adopts Akaike information criterion to select models, which avoids setting the lag order relying on human intervention. Then, the quantile factor model is established to reduce the dimensionality of conditional variables in a VAR (Vector Auto-Regressive) model, further reducing the number of coefficients. After that, the reduced-dimensional VAR model is used for a conditional Granger causality analysis. Finally, in order to identify the connectivity structure between the underlying system and observation time series, Monte Carlo simulation is applied to evaluate the performance of different methods. Experiments compared with classical methods on linear simulation systems in different dimensions and two realistic datasets, all demonstrate the effectiveness of QFM-CGC. In simulation experiments, the method proposed in this article has improved the accuracy of identifying causal relationships by an average of 6% and 3.46%. In two real-world data experiments, compared with the optimal comparison methods, QFM-CGC reduced the prediction error of causal relationship sequences.

QLTI-09. INTEGRATION OF NEURO-ONCOLOGY CLINICAL PATHWAYS IN PROVIDENCE SOUTHERN CALIFORNIA CLINICAL RESEARCH NETWORK: UPDATED INFO AFTER 17 MONTHS OF UTILIZATION

Abstract INTRODUCTION ClinicalPath is an evidence-based oncology decision support and analytics tool for cancer care. ClinicalPath’s treatment recommendations are prioritized based on efficacy, toxicity, and cost, by a nationwide committee of oncologists. The pathways are updated quarterly with the most recent evidenced-based treatment options that healthcare providers choose for adoption to help standardize therapy, improve quality, and decrease cost. The pathway system also allows for local clinical trial mapping to promote clinical trial awareness and help increase enrollment. ClinicalPath provides clinical pathways delivering personalized, evidence-based oncology guidance at the point of care. ClinicalPath’s network in North America serves more than 2,000 cancer physicians, within 54 practices in 31 states (15 academic medical centers, 29 hospital systems, and 9 community practices). Population: Our population is derived from multiple hospitals in Southern California within the Providence Health System. Methodology: Medical oncologists and Advanced Practice Providers received training on ClinicalPath before go-live. ClinicalPath was integrated into the Epic EHR in multiple Southern CA hospitals in a single month in Jan 2022. RESULTS On reviewing the utilization data within our Southern California region from Jan 2022-May 2023, 2681 treatment decisions were made across all cancers, with 86.7% of cancer patients treated on pathway. Of these decisions, 114 were made within the neuro-oncology specialty, and 70.2% of those cancer patients were treated on pathway. CONCLUSION We successfully integrated and initiated ClinicalPath in a multiple hospital-affiliated community oncology clinical trial network which included a strong neuro-oncology presence. We are actively working across our Southern California Region to map locally available clinical trials to promote awareness and increase enrollment. Provider utilization and patient on-pathway rates are actively monitored and will be updated.

Order-constrained inference to supplement experimental data analytics in behavioral economics: A motivational case study

A common approach to theory testing in behavioral and experimental economics relies on null hypothesis significance testing via (generalized) linear regression models. Here, we showcase order-constrained inference as an alternative route to theory testing. Order-constrained inference can improve the precision and nuance of behavioral decision analytics. For example, the method can be leveraged to quantify the evidence in support of, or against, a given hypothesis. It also offers advanced model selection tools for quantitative competition among multiple theories. To illustrate our case for order-constrained methods, we re-analyze data from a pre-registered experiment on incentives, cognitive reflection, and dishonest behavior. Building on this publicly available dataset, we further highlight the advantages of Bayesian order-constrained inference. We discuss how the method can deliver more convincing and more nuanced evidence than frequentist null hypothesis significance testing, pointing to new research avenues for supplementing and expanding on experimental designs in behavioral economics.

Investigation the Effect of Covıd-19 Pandemic in The Sales for Online Education Using Machine Learning Methods

Due to the pandemic which is the cause of the COVID-19 virus that emerged in 2019, many educational institutions had to follow online (remote) education. The situation in which the content of the pandemic occurred was also the reason for the education preferences of the users. The aim of this study is to analyze the effect of the pandemic, which includes the number of registered users of one of the online education platforms operating in Turkey thanks to machine learning, on distance education sales, and to create strategies by making sales forecasts for the future. Seven independent and one dependent variable were used to make sales forecasts using the data of the education structure. For accurate modelling, machine learning methods were first applied for decision analytics in a univariate manner and then multivariate applied and the applied methods were tested for error. By testing the success of the prediction models created with machine learning used in the study; 91.43% for support vector machine (SVM), 92.02% for multi-layer perceptron (MLP), and 96% for Long / Short Term Memory (LSTM). K-Folds cross validation method was also used for the success return of the established model.

A cybersecurity risk quantification and classification framework for informed risk mitigation decisions

Data breaches have been causing havoc for many years and continue to rise, demanding a more comprehensive framework to assess their severity. This study proposes a cybersecurity risk quantification and classification framework, addressing a significant research gap in breach severity evaluation. The study has two main objectives: to present a structured model for precise breach severity assessment, and to apply this model to real-world data breaches for practical insights. To achieve these objectives, we utilize a content analysis methodology to gauge severity of data breaches. This is followed by a robust likelihood impact matrix analysis, serving as a decision analytics tool, to effectively quantify potential risks. The integration of these approaches yields a more comprehensive and nuanced evaluation for assessing data breach severity. The proposed framework is then applied to data breaches gathered from Standard & Poor’s (S&P 500) organizations, revealing hacking and loss/theft of portable media as the breach types with the highest impact and probability. Drawing from these insights, our study suggests effective strategies to mitigate data breach risks for organizations. The implications of our research findings offer valuable contributions to both academic research and practical cybersecurity risk management.

Evaluating the Effectiveness of Want Want Group's Integrated Marketing Communications (IMC) Campaign during Chinese New Year 2022

It is vital for businesses to run Integrated Marketing Communications (IMC) campaigns in order to communicate with their target audience in an efficient manner. During the course of this research, a critical evaluation of the IMC campaign that Wants Want Group developed in conjunction with the 2022 Chinese New Year event will take place. The decision analytics of this research is to assess the goals and effectiveness of the campaign, as well as to provide an explanation of the consumer demographics that are being targeted by the campaign, therefore demonstrating understanding of the strategies used to segment markets. In addition, our research will make use of relevant theories to assist our analysis and will conduct an analysis of the benefits and drawbacks of the media channels used by the Want Want Group. The results show Want Want's IMC campaign for the 2022 Chinese New Year was effective in establishing an emotional connection with its audience and in increasing holiday sales. The campaign was successful in accomplishing its objectives of increasing brand recognition, customer loyalty, and customer base by using a range of promotional strategies.

Cost-effectiveness of response-adapted therapy (RAT) for advanced Hodgkin’s Lymphoma compared with conventional treatment in India: a Markov-model based analysis

Cost effectiveness analysis of interim positron emission tomography (PET-2, done after 2 cycles of chemotherapy) based response adaptive therapy (RAT) approaches in advanced Hodgkin lymphoma (aHL) are not available from an Indian perspective. We used a five-year decision analytics model to assess the cost-effectiveness of the two RAT approaches [(escalation (RAT-1) or de-escalation (RAT-2)] compared with standard care (SOC) in aHL (mean age:35 years). Modelling data was derived from secondary sources and sensitivity analyses were performed to assess the robustness of the model. Net monetary benefit (NMB) gained from RAT2 in Indian rupees (INR) (INR 2,26,896) was higher than the RAT1 (INR 1,83,138) when compared with SOC. Proportion achieving the complete response after initial treatment (CR1) was the key determining factor for the RAT1/2 dominance over SOC. Despite higher initial input costs, response-adapted therapy of aHL was cost-effective by minimizing the cost incurred and disutility experienced during relapse and salvage.

Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering

Hydrology is a mature physical science based on application of first principles. However, the water system is complex and its study requires analysis of increasingly large data available from conventional and novel remote sensing and IoT sensor technologies. New data-driven approaches like Artificial Intelligence (AI) and Machine Learning (ML) are attracting much “hype” despite their apparent limitations (transparency, interpretability, ethics). Some AI/ML applications lack in addressing explicitly important hydrological questions, focusing mainly on “black-box” prediction without providing mechanistic insights. We present a typology of four main types of hydrological problems based on their dominant space and time scales, review their current tools and challenges, and identify important opportunities for AI/ML in hydrology around three main topics: data management, insights and knowledge extraction, and modelling structure. Instead of just for prediction, we propose that AI/ML can be a powerful inductive and exploratory dimension-reduction tool within the rich hydrological toolchest to support the development of new theories that address standing gaps in changing hydrological systems. AI/ML can incorporate other forms of structured and non-structured data and traditional knowledge typically not considered in process-based models. This can help us further advance process-based understanding, forecasting and management of hydrological systems, particularly at larger integrated system scales with big models. We call for reimagining the original definition of AI in hydrology to incorporate not only today’s main focus on learning, but on decision analytics and action rules, and on development of autonomous machines in a continuous cycle of learning and refinement in the context of strong ethical, legal, social, and economic constrains. For this, transdisciplinary communities of knowledge and practice will need to be forged with strong investment from the public sector and private engagement to protect water as a common good under accelerated demand and environmental change.

Financial Inclusion in the Industrial Cluster

The Government of India and the concerned state governments are implementing plenty of programmes and schemes for achieving the Sustainable Development Goals (SDGs) on entrepreneurship. Out of the 17 SDGs, goal 8 relates to Decent Work and Economic Growth including Financial Inclusion and goal 9 relates to Industry, Innovation, and Infrastructure. The objective of this paper is to study the business analytics of financial inclusion in industrial clusters in Tamil Nadu and their relevance to sustainable development goals. Several analyses such as diagnostic analysis, descriptive analysis, inferential analysis, predictive analysis, prescriptive analysis, and decision analytics were performed. The methodology adopted by the researcher is to develop a model by collecting primary data, such as, input variables like grants from the Government of India, grants from the state government of Tamil Nadu, special purpose vehicle (SPV) / entrepreneur contribution, and bank loans and output variables like project cost, from various departments and corporations of the Government of Tamil Nadu and secondary data from other government websites. The data was analyzed through percentage analysis, descriptive analysis, correlation analysis, regression analysis, and structural equation modelling. Financial inclusion in industrial clusters is the need of the hour, which is already underway, in the form of grants from the Government of India and Tamil Nadu, Cluster Development Programmes, SPV contribution and bank loans to improve productivity and reduce costs, and common facility centres in clusters.

A Study on the importance of EMR Data Used in Business Analytics for Decision making Using Open Source tools

EMR is used in healthcare system for the managing the day-to-day activities, by providing quality of care and improving the performances of healthcare services. It has been understood by the Healthcare Professionals, students, patients that EMR can be used to solve the operational issues, provide the data to the patient and other facilities through the nationally accepted interoperable standards.

Teachers as self‐regulated learners: The role of multimodal data analytics for instructional decision making

AbstractThe goal of this chapter is to propose a cyclical process of how teachers can use multimodal multichannel data of cognitive, affective, metacognitive, motivational, and social processes to assist with the understanding of their own and their students’ self‐regulated learning (SRL), and their subsequent instructional decision making. What differentiates our proposed framework from theories of self‐regulated learning is that we propose this cycle occurs throughout all phases of self‐regulated learning and in real time. In this chapter, we outline each component of the framework, followed by a discussion of future steps that discuss our future view of the classroom with teachers and how they can use multimodal multichannel data to help them with their instructional decision making. We aim to make it clear that: (1) our proposed stages are non‐linear and interdependent, which represents the foundation of SRL theory and the structure of classroom learning, and (2) our proposed framework highlights that using any technological tool to help teachers, not replace them.

Integration of breast-oncology clinical pathways in Providence Southern California clinical research network.

e18710 Background: ClinicalPath is an evidence-based oncology decision support and analytics tool for cancer care. ClinicalPath’s treatment recommendations are prioritized based on efficacy, toxicity, and cost by a nationwide committee of oncologists. The pathways are updated quarterly and are expected to speed the integration of new treatments into practice, standardize therapy, improve quality, and decrease cost. The pathway system also allows for local clinical trial mapping to promote clinical trial awareness and increase enrollment. ClinicalPath provides clinical pathways delivering personalized, evidence-based oncology guidance at the point of care. ClinicalPath’s network in North America serves more than 2,000 cancer physicians, within 54 practices in 31 states (15 academic medical centers, 29 hospital systems, and 9 community practices). Methods: Providence’s Population: Our population is derived from multiple hospitals in Southern California within the Providence Health System. Methodology: Medical oncologists and Advanced Practice Providers received training on ClinicalPath before go-live. ClinicalPath was integrated into the Epic EHR in multiple Southern CA hospitals in a single month. Results: After 4 months of utilization within our Southern California region, 342 treatment decisions were made across all cancers, and 85.1% of cancer patients were treated on pathway. Of which, 103 treatment decisions were made within the breast-oncology specialty, and 93.2% of those cancer patients were treated on pathway. Conclusions: We successfully integrated and initiated ClinicalPath in a multiple hospital-affiliated community oncology clinical trial network. We are actively working across our Southern California Region to map locally available clinical trials to promote awareness and increase enrollment. Provider utilization and patient on-pathway rates are actively monitored.

Secure and Reliable Big-Data-Based Decision Making Using Quantum Approach in IIoT Systems

Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs; instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories.

The Utility of Decision Tree and Analytics Hierarchy Process in Prioritizing of Social Aid Distribution due to Covid-19 Pandemic in Indonesia

The Indonesian government provided various social assistance programs to local governments during Covid-19. One of the difficulties for the local governments in determining candidates for social aid is ensuring that the number of candidates is in balance with the available quota. Therefore, the local governments must select the most eligible candidates. This study proposes a priority model that can provide recommendations for candidates who meet the criteria for social assistance. The six parameters used in this study were: number of dependents, occupation, income, age, Covid status, and citizen status. The model operates in two stages, namely classification followed by ranking. The classification stage is conducted using a decision tree, while the ranking stage is performed conducted using the Analytical Hierarchy Process (AHP) algorithm. The decision tree separates two classes, namely, eligible and non-eligible. In addition, the classification process is also used to determine the dominant attributes and played a role in the modeling. The proposed model generates a list of the most eligible candidates based on our research. These are sorted by weight from greatest to most eligible using five dominant parameters: number of dependents, income, age, Covid status, and citizen status.

Business Analytics Capabilities and Decision Quality: The Mediating Roles of Decision Speed and Comprehensiveness

ABSTRACT This paper examines the nexus between business analytics and strategic decision facets using the resource-based theory. Analysis with partial least squares demonstrate that decision comprehensiveness and speed mediate the relationship between business analytics and decision quality. While fuzzy sets analysis demonstrates that business analytics, decision comprehensiveness, and speed are sufficient (but not necessary) conditions for decision quality. When firms utilize business analytics, decision speed and comprehensiveness are mutually inclusive. Theoretical and practical implications are offered.

Liberté, Égalité, Fraternité: a power study in signed networks

Power in human societies is a central phenomenon. Even though, it took ages to understand it and – even more – to measure it. Only in the last decades attempts were made to model power relations and to assign respective power indices to actors in a network. The present work goes a step further. It measures power of actors and groups of actors in networks by means of conditional relations. In a probabilistic framework, such relations are specified as conditionals: Which actor receives power given that the adjacent actor has it, and which actor looses power given that the neighbour dominates. This pattern of power relations allows for an exact calculation of an actor’s and groups of actors’ power index. The new decision analytics tool for this is maximizing entropy for the whole net and evaluating each actor’s influence therein. The new concept is applied to a middle size Kronecker net of clans and subclans operating in a today’s society.

A predictive decision analytics approach for primary care operations management: A case study of double-booking strategy design and evaluation

Primary care plays a vital role for individuals and families in accessing care, staying well, and improving quality of life. However, the complexities and uncertainties in the primary care delivery system (e.g., patient no-shows/walk-ins, staffing shortage) have brought significant challenges in its operations management, which can potentially lead to poor patient outcomes and negative primary care operations (e.g., loss of productivity, inefficiency). This paper presents a decision analytics approach developed based on predictive analytics and simulation modeling to better facilitate management of the underlying complexities and uncertainties in primary care operations. A case study was conducted in a local family medicine clinic to demonstrate the use of this approach to manage patient no-shows. In this case study, a patient no-show prediction model was used in conjunction with an integrated agent-based and discrete-event simulation model to design and evaluate double-booking strategies. Using the predicted patient no-show information, a prediction-based double-booking strategy was created and compared against two other strategies, namely random and designated time. Scenario-based experiments were then conducted to examine the impacts of different double-booking strategies on clinic’s operational outcomes, focusing on the trade-offs between the clinic productivity (measured by daily patient throughput) and efficiency (measured by visit cycle and patient wait time for doctor). The results showed that the best productivity-efficiency balance was derived under the prediction-based double-booking strategy. The proposed hybrid decision analytics approach has the potential to better support decision-making in primary care operations management and improve the system’s performance. Further, it can be generalized in the context of various healthcare settings for broader applications.

Projecting COVID-19 cases and hospital burden in Ohio

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

Identification of low voltage distribution transformer–customer connectivity based on unsupervised learning

The increased penetration of smart meters in the low voltage distribution network provides valuable insights that distribution network operators can leverage to produce detailed analytics for decision making. This includes enhanced network observability that can support network planning, phase balancing and planned outage management. In this paper, we propose three unsupervised learning algorithms to model the problem of identifying the transformer–customer connectivity relation at the low voltage network using smart meter voltage measurements of residential units as a classification problem. The algorithms are tested on real-world smart meter datasets and can detect the correct transformer–customer connectivity with 95% to 100% accuracy. This allows distribution network operators to effectively detect inconsistencies between customer data reports and actual physical connection of residential units to distribution transformers to comply with legal regulations.

Decentralized Scheduling in Edge Computing Paradigms: End-to-End Decision Analytics and the Price of Anarchy

Decentralized Scheduling in Edge Computing Paradigms: End-to-End Decision Analytics and the Price of Anarchy