Making Early and Accurate Deep Learning Predictions to Help Disadvantaged Individuals in Medical Crowdfunding

Abstract

Medical crowdfunding is a popular channel for people in need of financial help with paying medical bills to collect donations from large numbers of people. However, large heterogeneity exists in the amount of donations each case receives and such uncertainty in fundraising outcomes hinders making timely treatment plans for patients. It is important to provide early and accurate predictions for medical crowdfunding performance, and help fundraisers engage in timely interventions. In this study, we propose a new approach that effectively combines time-varying features and time-invariant features in a deep learning model. This model provides dynamic predictions of fundraising outcomes, based on fixed case-level attributes and daily updated measures of social media activities. Compared with a rich set of baseline models, our model consistently demonstrates higher predictive accuracy while requiring a shorter observation window of data, thus achieving both accurate and early prediction objectives. We conduct a temporal clustering analysis to analyze the heterogeneous patterns in how the time-varying features relate to fundraising outcomes. In addition, we conduct a simulation analysis to demonstrate that interventions from fundraisers can significantly improve the fundraising performance of disadvantaged cases that are predicted to receive the lowest donation amounts, particularly when the interventions are taken early. These findings show that our deep learning prediction model and the actionable insights can provide timely feedback to fundraisers and promote equal access to resources for all. Our proposed approach is generalizable to different contexts, enabling effective processing of diverse sources of data and informing timely interventions early on.

Keywords: early predictions, deep learning, temporal clustering, medical crowdfunding, disadvantaged individuals