Understanding Cancer Caregiving and Predicting Burden: An Analytics and Machine Learning Approach.

Cancer caregivers are often informal family members who may not be prepared to adequately meet the needs of patients and often experience high stress along with significant physical, emotional, and financial burdens. Accurate prediction of caregiver's burden level is highly valuable for early intervention and support. In this study, we used several machine learning approaches to build prediction models from the National Alliance for Caregiving/AARP dataset. We performed data cleansing and imputation on the raw data to give us a working dataset of cancer caregivers. Then a series of feature selection methods were used to identify predictive risk factors for burden level. Using supervised machine learning classifiers, we achieved reasonably good prediction performance (Accuracy ∼ 0.94; AUC ∼ 0.97; F1∼ 0.93). We identify a small set of 15 features that are strong predictors of burden and can be used to build Clinical Decision Support Systems.

Survey examination of resilience, psychological, and relational well-being during COVID-19: A developmental and cross-cultural dataset.

The datasets include relevant psychological and demographic variables relating to people's relationships, perceptions, and reactions to the Covid-19 pandemic. Participants were recruited from the United States (N = 396), China (N = 156), and Iran (N = 248). Participants were directed to an online survey that assessed their psychological well-being, affective states, factors related to life satisfaction, and their experiences with the Covid-19 pandemic. For the United States, participants were separated by developmental stage (e.g., young adults between 18 and 35 years old and older adults who were 55 years old or older). Participants from China and Iran were 18 years old or older. Participants from the United States also provided qualitative data in the form of a text-box response where they described their reactions to the Covid-19 pandemic. These data may be relevant for researchers who want to investigate cross-cultural or developmental differences in people's psychological states, perceptions, and reactions in the beginning phases of the Covid-19 pandemic.