Data Mining-based Survival Analysis and Simulation Modeling for Lung Transplant

The objective of this research is to develop a decision support methodology for the lung transplant procedure by investigating the UNOS nation-wide dataset via data mining-based survival analysis and simulation-based optimization. Traditional statistical techniques have various limitations which hinder the exploration of the information hidden under the voluminous data. The deployment of the structural equation modeling integrated with decision trees provides a more effective matching between the donor organ and the recipient. Such an integration preceded by powerful data mining models to determine which variables to include for survival analysis is validated via the simulation-based optimization.The suggested data mining-based survival analysis was superior to the conventional statistical methods in predicting the lung graft survivability and in determining the critical variables to include in organ matching and allocation. The proposed matching index derived via structural equation model-based decision trees was validated to be a more effective priority-ranking mechanism than the current lung allocation scoring system. This validation was established by a simulation-based optimization model. It was demonstrated that with this novel matching index, a substantial improvement was achieved in the survival rate while only a short delay was caused in the average waiting time of candidate patients on the list. Furthermore, via the response surface methodology-based simulation optimization the optimal weighting scheme for the components of the novel matching index was determined by jointly optimizing the lung transplant performance measures, namely, the justice principle in terms of the waiting time and the utility principle in terms of the survival rate. The study presents uniqueness in that it provides a means to integrate the data mining modeling as well as simulation optimization with the survival analysis so that more useful information hidden in the large amount of data can be discovered. The developed methodology improves the modeling of matching and allocation system in terms of both interpretability and predictability. This will be beneficial to medical professionals at a great deal.Industrial Engineering & Managemen

The Impact of Subjective and Objective Experience on Mobile Banking Usage: An Analytical Approach

This paper aims to investigate mobile banking (MB) usage through the theoretical lens of UTAUT model with its four pillars. The research model will be tested via a hybrid neural networks-based structural equation modeling (SEM-NN) to reveal significant factors. Universal structural modeling (USM) will be then utilized to find the hidden paths and nonlinearity in our research model. To the best of our knowledge, this is the first study to examine the role of subjective and objective experience on MB usage using a multi-analytical approach. Neural network (NN) and USM can identify the most significant determinants and hidden interaction effects, respectively. Thus, both techniques would help to complement SEM and increase our understanding of the influential factors on MB usage. Preliminary results are presented and discussed. Potential contribution and conclusion are communicated to both academia and industry

Measuring and Understanding CSR performance on Social Media

In recent years, business decisions and investment strategies have increasingly incorporated concerns of social issues. With increasing public attention on Corporate Social Responsibility (CSR), companies doing “harm” or “good” to society have eminently impacted the brand image, consumer perception, and employee retainment. To gain public awareness of companies’ improvement on CSR, companies share sustainable and ethical practices and business strategies on social media, such as Twitter. In this study, we aim to analyze the impact of social media as a moderator on the association between strengths and weaknesses of corporate social actions, and investigate whether the negative performance of CSR in the past leads to exemplary performance in one lag year

Deep learning in business analytics and operations research: Models, applications and managerial implications

Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional machine learning. However, our research into the existing body of literature reveals a scarcity of research works utilizing deep learning in our discipline. Accordingly, the objectives of this overview article are as follows: (1) we review research on deep learning for business analytics from an operational point of view. (2) We motivate why researchers and practitioners from business analytics should utilize deep neural networks and review potential use cases, necessary requirements, and benefits. (3) We investigate the added value to operations research in different case studies with real data from entrepreneurial undertakings. All such cases demonstrate improvements in operational performance over traditional machine learning and thus direct value gains. (4) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning. (5) Our computational experiments find that default, out-of-the-box architectures are often suboptimal and thus highlight the value of customized architectures by proposing a novel deep-embedded network.ISSN:0377-2217ISSN:1872-686

Simple Interaction Finding Technique (SIFT) – A Simple Methodology to Generate Novel Hypotheses from Complex Datasets

Machine Learning (ML) models have become ubiquitous in all spheres of research and decision-making. Understanding ML models as well as the data-generation-process (DGP) for the dataset under examination are important. Most highly accurate ML models are blackboxes that aren\u27t interpretable. In this work, we propose a methodology that can help elicit important information from any ML models. Our methodology allows the use of any highly-accurate ML model to find interactions between variables in the dataset. This can allow for a better understanding of the underlying DGP by using a data-and-model agnostic process to synthesize new knowledge about the underlying phenomenon