A Novel Deep Learning Approach to Predicting Medical Crowdfunding Donations

Medical crowdfunding has seen rapid growth in recent years and it has become a popular channel for people needing financial help. However, there exists large heterogeneity in donations across cases and fundraisers face significant uncertainty in whether their crowdfunding campaigns can meet fundraising goals. We aim to develop novel algorithms to provide accurate and timely predictions of fundraising performance, to better inform fundraisers. For this purpose, we use a combination of machine learning techniques to extract interpretable insights and provide accurate predictions. We start with a model using only the time-invariant features of cases, to provide an immediate evaluation of fundraising performance. Then we analyze the time-varying features from daily observations of case metrics, conduct a multivariate time series clustering and identify four typical temporal donation patterns. Finally, we incorporate the clustering patterns to design a deep learning model that provides daily updated predictions of the total amount of money fundraisers likely receive. Compared with baseline models, our model achieves better accuracy on average and requires a shorter observation window of the time-varying features from the campaign launch to provide robust predictions with high confidence. Our modeling approach can be applied to assist fundraisers’ decisions on promoting their campaigns better and can potentially help crowdfunding platforms design more customized suggestions to improve the chances of success for all cases. The proposed framework is generalizable to apply to other fields with both time-varying and time-invariant information.