In this paper, the authors investigate the impact of in-store promotion and its spillover effect on private label introductions. Studying different retail supply chain scenarios in which the retailer carrying a national brand may introduce its own private label product and promote either the national brand or the private label inside the store, they find the in-store promotion on one product has a positive spillover effect on the other product. Without in-store promotion and spillover effect, the conventional wisdom indicates that, in a retail supply chain, the national brand manufacturer will be negatively affected by the introduction of a private label product. With in-store promotion and spillover effect, however, the national brand manufacturer can actually benefit from the private label introduction. When the spillover from national brand to private label is high, the retailer prefers to promote the national brand product. When the spillover from private label to national brand is high, promoting the private label product can also benefit the national brand manufacturer. With a symmetric spillover rate, the national brand manufacturer can still benefit from the private label introduction, as long as the retailer promotes the national brand product, the horizontal competition is not intense, or the private label product quality is sufficiently low.

Diabetic retinopathy (DR), a microvascular complication of diabetes, is the leading cause of vision loss among working-aged adults. However, due to the low compliance rate of DR screening and expensive medical devices for ophthalmic exams, many DR patients did not seek proper medical attention until DR develops to irreversible stages (i.e., vision loss). Fortunately, the widely available electronic health record (EHR) databases provide an unprecedented opportunity to develop cost-effective machine-learning tools for DR detection. This paper proposes a Multi-branching Temporal Convolutional Network with Tensor Data Completion (MB-TCN-TC) model to analyze the longitudinal EHRs collected from diabetic patients for DR prediction. Experimental results demonstrate that the proposed MB-TCN-TC model not only effectively copes with the imbalanced data and missing value issues commonly seen in EHR datasets but also captures the temporal correlation and complicated interactions among medical variables in the longitudinal clinical records, yielding superior prediction performance compared to existing methods. Specifically, our MB-TCN-TC model provides AUROC and AUPRC scores of 0.949 and 0.793 respectively, achieving an improvement of 6.27% on AUROC, 11.85% on AUPRC, and 19.3% on F1 score compared with the traditional TCN model.

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records. We conducted a retrospective analysis on a derivation cohort with 3749 DR and $94,127$ non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications and laboratory results. A predictive model and a risk index were built based on the selected, essential predictors, and then validated using another independent validation cohort with 869 DR and 6448 non-DR diabetic patients. Out of the 26 variables, 10 were identified to be essential for predicting DR. The predictive model achieved a $0.85$ AUC on the derivation cohort and a $0.77$ AUC on the validation cohort. For the risk index, the AUCs were $0.81$ and $0.73$ on the derivation and validation cohorts, respectively. The predictive technologies can provide an early warning sign that motivates patients to comply with eye examinations for early screening and potential treatments.