Customer Churn Prediction Based on BG / NBD Model

With the rapid development of information technology, most enterprises have built e-commerce platform, which promotes the revolution of operation mode. The focus of competition gradually becomes the customers rather than the products under the increasingly fierce market competition of the E-commerce model. Because of the non-contractual relationship between the customers and the e-commerce platform, maintaining the stable customer relationship becomes the necessary condition for the e-commerce enterprises to get profit. So predicting the customer churn accurately plays an important role in the development of e-commerce enterprises. In this paper, the BG / NBD model is used to analyze the historical transaction records of an e-commerce platform in order to analyze and predict the purchase behavior of the existing customers, and identify the pre-losing customers, which helps the enterprises to implement the more effective strategies of CRM and restore the pre-loss customers timely

How do Aspiration Shortfalls Interact with Regulatory Incentives and Controls to Drive Innovation in U.S. Hospitals?

Strategic choices about innovation are becoming increasingly relevant in the healthcare industry to meet the changing needs of the marketplace. We draw on the Behavioral Theory of the Firm and Institutional Theory to (1) identify the influence of aspiration shortfalls of Patient Quality and Cost of Care on IT-enabled Clinical Process Innovation and Services Innovation, and (2) identify how the nature of these relationships change based on regulation at the federal (American Recovery and Reinvestment Act of 2009) and state (Certificate of Need programs) level. Our empirical study is situated in the U.S. healthcare industry. We draw on multiple sources of data, such as the American Hospital Association Annual Survey and IT Supplement as well as the Centers of Medicare and Medicaid, to construct a panel dataset of 3,500 hospitals from 2008—2013. We identify measures for our constructs and propose analysis methods to test our model and hypotheses

Analyzing Patients’ EHR: Predicting and Explaining Admission Consequences for COPD and Liver Disease Patients

This study analyzed the admission outcomes in chronic patients (with COPD, and Liver disease) to demonstrate the feasibility of applying prediction methods on EHR records while incorporating an explainable AI technique. We predicted three target variables: 30-day readmission, Medium&Long Length of Stay and Single-day admission and analyzed the features using an explainable AI technique, the SHapley Additive exPlanations (SHAP). The results show that Readmission had higher prediction scores than all other dependent variables. Some features affected all target variables with either positive or negative influence including: Age, Charlson comorbidity index, Day-Shift, Gender, using EHR screens and Insurance cover level. These findings thus point to the value of using Machine-Learning combined with an explainable AI method to understand and assess the risks factors. The assessment of the potential factors leading to multiple complications can bolster prevention-oriented medical decisions to groups of patients but can also be tailored to the patient level

AI-Assisted Diagnosis of Bone Tuberculosis: A Design Science Research Approach

Bone Tuberculosis (TB) is a significant public health challenge requiring early and precise diagnosis for effective treatment. Traditional methods like radiography and biopsy are invasive and costly. Our study introduces a holistic AI-assisted orthopedic clinical diagnosis system developed through an Action Design Research approach. Unlike previous efforts focused solely on algorithmic design, our system is iteratively validated with real-world clinical data, ensuring both theoretical rigor and practical applicability. By fine-tuning AI algorithms to meet actual clinical needs, we bridge the gap between technological innovation and healthcare relevance. Our research offers innovative insights into the design and evaluation of AI-assisted systems, emphasizing the role of empirical data and diverse evaluation metrics. The study is expected to have broader implications for the adoption of AI in clinical settings, offering a more comprehensive and reliable solution for bone TB diagnosis

Exploring the potential of big data on the health care delivery value chain (CDVC): a preliminary literature and research agenda

Big data analytics (BDA) is emerging as a game changer in healthcare. While the practitioner literature has been speculating on the high potential of BDA in transforming the healthcare sector, few rigorous empirical studies have been conducted by scholars to assess the real potential of BDA. Drawing on the health care delivery value chain (CDVC) and an extensive literature review, this exploratory study aims to discuss current peer-reviewed articles dealing with BDA across the CDVC and discuss future research directions

Barriers to Predictive Analytics Use for Policy Decision-Making Effectiveness in Turbulent Times: A Case Study of Fukushima Nuclear Accident

Predictive analytics are data-driven software tools that draw on confirmed relationships between variables to predict future outcomes. Hence they may provide government with new analytical capabilities for enhancing policy decision-making effectiveness in turbulent environments. However, predictive analytics system use research is still lacking. Therefore, this study adapts the existing model of strategic decision-making effectiveness to examine government use of predictive analytics in turbulent times and to identify barriers to using information effectively in enhancing policy decision making effectiveness. We use a case study research to address two research questions in the context of the 2011 Fukushima nuclear accident. Our study found varying levels of proactive use of SPEEDI predictive analytics system during the escalating nuclear reactor meltdowns between Japan’s central government agencies and between the central and the state government levels. Using the model, we argue that procedural rationality and political behavior can be used to explain some observed variations

Readmission risk prediction for patients after total hip or knee arthroplasty

Cybersecurity intelligence sharing (CIS) has the potential to help organisations improving their situational awareness. Although CIS has received more attention from organisations, participation in CIS operation is not satisfactory, and there is not too much information about the factors that are antecedent to CIS among organisations . Thus, this study aims to investigate technical and non-technical factors including organisational and environmental factors influence organisational participation in CIS practices