Organizations recognize the need to adopt Enterprise Resource Planning (ERP) in order to become more competitive, efficient, and productive, although the adoption and implementation of an ERP system is a costly and risky endeavor. Recently, cloud computing has become a viable and competitive means by which most organizations, especially Small and Medium-sized Enterprises (SMEs), can implement an ERP system in a short time frame and cost-effective way. The authors' research examines the feasibility of cloud-based ERP systems for SMEs through a case study. The case emphasizes the potential of cloud-based ERP systems for SMEs as well as some of the challenges and peculiarities involved in their efforts to obtain an affordable and versatile ERP system. Their findings can potentially guide SMEs to make well-informed decisions throughout their cloud-based ERP adoption process.
One 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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