Identifying which clients will cancel their insurance policy machine learning methods - Narain, K.; Pang, Y. (Proceedings - 2024 IEEE 48th Annual Computers, Software, and Applications Conference, COMPSAC 2024, 2024)
Using the 2013 data set provided by Insurance Inc., logistic regression and linear discriminant analysis models were created along with data visualizations to find out which factors recorded in the data set and the state of those factors causes a client to cancel their policy. The factors that impact whether a client will cancel are those that directly pertain to the policy. For example, the coverage type and the premium the client is paying for the policy impacts the probability the client will cancel their policy. Factors that go into forming the policy and have a relationship between one another such as age and premium, also impact the probability that a client will cancel their policy. The credit status of a client, whether it is low, medium, or high, and the type of coverage they have, has the most impact on a client's inevitability to cancel. If a client's credit score is classified as low, then that client is has a high probability of cancelling their policy according to the LDA (Linear Discriminant Analysis) classifier and logistic regression model. Likewise, if a client has coverage type B, the probability that they will cancel their policy is higher. The sales channel used to sell a client a policy also impacts the probability they will cancel. According to the LDA classifier and the logistic regression model, if a client was sold a policy over the phone, they are more likely to cancel. © 2024 IEEE.
Keywords: Gaussian Distribution; Linear Discriminant Analysis (LDA) Classifier; Logistic Regression; Naive Bayes Classifier; dept.mat

Identification of adipogenic and osteogenic differentiation using transfer learning of ResNet-18 - Mai, M.; Luo, S.; Wang, S.; Pang, Y. (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, 2023)
Human mesenchymal stem cells (hMSCs) have great potential in cell-based therapies and regenerative medicine due to their self-renewal and multipotency. hMSCs can be differentiated into several cell types, including adipocytes and osteblast. Conventional approaches for determining adipocyte formation include staining of lipid droplets (i.e., oil-red-O) during adipogenesis, which is time-consuming and uneconomical. Thus, there is an emerging need for a more effective and accurate approach to the prediction of adipogenic differentiation. Here, by combining live-cell imaging with a deep learning method, we developed a convolutional neural network-based approach to precisely predict lipid droplet formation during adipogenic differentiation of hMSCs. © 2023 IEEE.
Keywords: Adipogenic differentiation; CNN; Deep learning; ResNet; Transfer learning; adipogenisis; computer vision; convolution; dept.mat; hMSCs; human mesenchymal stem cells; osteogenic differentiation

Reactive axillary lymph nodes after COVID-19 mRNA vaccination: Comparison of mRNA vs. attenuated whole-virus vaccines - Adin, M.E.; Isufi, E.; Wu, J.; Pang, Y.; Nguyen, D.; Simsek Has, D.; Caner, C.; Aboueldaha, N.; Mossa-Basha, M.; Pucar, D. (Nuclear Medicine Communications, 2024)
Objective To compare the incidence and natural course of reactive axillary lymph nodes (RAL) between mRNA and attenuated whole-virus vaccines using Deauville criteria. Methods In this multi-institutional PET-CT study comprising multiple vaccine types (Pfizer-BioNTech/Comirnaty, Moderna/Spikevax, Sinovac/CoronaVac and Janssen vaccines), we evaluated the incidence and natural course of RAL in a large cohort of oncological patients utilizing a standardized Deauville scaling system (n=522; 293 Female, Deauville 3-5 positive for RAL). Univariate and multivariate analyses were conducted to evaluate the predictive value of clinical parameters (absolute neutrophil count [ANC], platelets, age, sex, tumor type, and vaccine-to-PET interval) for PET positivity. Results Pfizer-BioNTech/Comirnaty and Moderna vaccines revealed similar RAL incidences for the first 20 days after the second dose of vaccine administration (44% for the first 10 days for both groups, 26% vs. 20% for 10-20 days, respectively for Moderna and Pfizer). However, Moderna recipients revealed significantly higher incidences of RAL after 20 days compared to Pfizer-BioNTech/Comirnaty, with nodal reactivity spanning up to the 9th week post-vaccination (15% vs. 4%, respectively P<0.001). No RAL was observed in patients who received either a single dose of J&J vaccine or two doses of CroronaVac. Younger patients showed increased likelihood of RAL, otherwise, clinical/demographic parameters were not predictive of RAL (P=0.014 for age, P>0.05 for additional clinical/demographic parameters). Conclusion RAL based on strict PET criteria was observed with mRNA but not with attenuated whole-virus vaccines, in line with higher immunogenicity and stronger protection offered by mRNA vaccines. © 2024 Wolters Kluwer Health. All rights reserved.
Keywords: COVID-19; CoronaVac; Janssen; axillary lymph node; dept.mat; mRNA vaccine; peer-reviewed