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

Abstract

Medical crowdfunding is a popular channel for people seeking financial assistance to cover their medical expenses, allowing them to collect donations from a large number of donors. However, a mismatch between the supply and demand of donations creates large heterogeneity in the fundraising outcomes across medical crowdfunding campaigns, and such uncertainty can impede the timely planning of treatment for patients. Providing early and accurate forecasts for medical crowdfunding performance can better inform fundraisers and assist them in optimizing timely interventions to improve fundraising outcomes. In this study, we propose a new approach that effectively combines time-varying features and time-invariant features in a deep learning model, to provide dynamic predictions of fundraising outcomes. When compared with a comprehensive 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 further conduct a temporal clustering analysis to analyze the heterogeneous patterns in how the time-varying features relate to fundraising outcomes. In addition, we perform simulation analyses 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 implemented 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 applicable to various contexts, enabling effective processing of diverse sources of data and facilitating early interventions.