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BackgroundBreast cancer is the most common cancer and the most common cause of cancer death in women. Although survival rates have improved, unmet psychosocial needs remain challenging because the quality of life (QoL) and QoL-related factors change over time. In addition, traditional statistical models have limitations in identifying factors associated with QoL over time, particularly concerning the physical, psychological, economic, spiritual, and social dimensions. ObjectiveThis study aimed to identify patient-centered factors associated with QoL among patients with breast cancer using a machine learning (ML) algorithm to analyze data collected along different survivorship trajectories. MethodsThe study used 2 data sets. The first data set was the cross-sectional survey data from the Breast Cancer Information Grand Round for Survivorship (BIG-S) study, which recruited consecutive breast cancer survivors who visited the outpatient breast cancer clinic at the Samsung Medical Center in Seoul, Korea, between 2018 and 2019. The second data set was the longitudinal cohort data from the Beauty Education for Distressed Breast Cancer (BEST) cohort study, which was conducted at 2 university-based cancer hospitals in Seoul, Korea, between 2011 and 2016. QoL was measured using European Organization for Research and Treatment of Cancer QoL Questionnaire Core 30 questionnaire. Feature importance was interpreted using Shapley Additive Explanations (SHAP). The final model was selected based on the highest mean area under the receiver operating characteristic curve (AUC). The analyses were performed using the Python 3.7 programming environment (Python Software Foundation). ResultsThe study included 6265 breast cancer survivors in the training data set and 432 patients in the validation set. The mean age was 50.6 (SD 8.66) years and 46.8% (n=2004) had stage 1 cancer. In the training data set, 48.3% (n=3026) of survivors had poor QoL. The study developed ML models for QoL prediction based on 6 algorithms. Performance was good for all survival trajectories: overall (AUC 0.823), baseline (AUC 0.835), within 1 year (AUC 0.860), between 2 and 3 years (AUC 0.808), between 3 and 4 years (AUC 0.820), and between 4 and 5 years (AUC 0.826). Emotional and physical functions were the most important features before surgery and within 1 year after surgery, respectively. Fatigue was the most important feature between 1 and 4 years. Despite the survival period, hopefulness was the most influential feature on QoL. External validation of the models showed good performance with AUCs between 0.770 and 0.862. ConclusionsThe study identified important factors associated with QoL among breast cancer survivors across different survival trajectories. Understanding the changing trends of these factors could help to intervene more precisely and timely, and potentially prevent or alleviate QoL-related issues for patients. The good performance of our ML models in both training and external validation sets suggests the potential use of this approach in identifying patient-centered factors and improving survivorship care.

In this paper, a MEMS (Micro Electro Mechanical Systems)-based frequency-tunable metamaterial absorber for millimeter-wave application was demonstrated. To achieve the resonant-frequency tunability of the absorber, the unit cell of the proposed metamaterial was designed to be a symmetric split-ring resonator with a stress-induced MEMS cantilever array having initial out-of-plane deflections, and the cantilevers were electrostatically actuated to generate a capacitance change. The dimensional parameters of the absorber were determined via impedance matching using a full electromagnetic simulation. The designed absorber was fabricated on a glass wafer with surface micromachining processes using a photoresist sacrificial layer and the oxygen-plasma-ashing process to release the cantilevers. The performance of the fabricated absorber was experimentally validated using a waveguide measurement setup. The absorption frequency shifted down according to the applied DC (direct current) bias voltage from 28 GHz in the initial off state to 25.5 GHz in the pull-down state with the applied voltage of 15 V. The measured reflection coefficients at those frequencies were −5.68 dB and −33.60 dB, corresponding to the peak absorptivity rates of 72.9 and 99.9%, respectively.

Prior to the rapid growth of Artificial Intelligence ("AI") in the consumer market, smart products received great attention from marketers and consumers. Given the recent increase in attention to AI technologies, this research explores consumers' preferences and intentions when products are framed as "smart" versus "AI-powered." While previous literature has explored AI products and smart products individually, little is known about consumers' preferences between the two products simultaneously. Three empirical experiments demonstrate that consumers show preference for products labeled as "smart" over those labeled as "AI-powered." This preference is mediated by enhanced learning anxiety related to AI. The findings provide insights for marketers applying message framing, suggesting that labeling products as "smart" may evoke more favorable consumer behavior compared to the "AI-powered" label. Moreover, this research significantly contributes to the existing literature on perceptions and intentions related to AI and smart products by concurrently exploring consumer preferences regarding both smart products and AI-powered products. • This research sheds light on consumer behavior regarding products framed as "smart" versus "AI-powered.". • Across three empirical studies, consumers prefer products labeled as "smart" over their "AI-powered" counterparts. • The preference for smart products was shown to be mediated by enhanced learning anxiety to AI. • For marketers applying message framing, "smart" may evoke more favorable consumer behavior compared to "AI-powered.". [ABSTRACT FROM AUTHOR]