Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs

Posted: 18 Nov 2019

See all articles by Tong Wang

Tong Wang

University of Iowa

Fujie Jin

Kelley School of Business, Indiana University

Yu Jeffrey Hu

Georgia Institute of Technology - Scheller College of Business

Yuan Cheng

Tsinghua University - School of Economics & Management

Date Written: October 27, 2019

Abstract

Medical crowdfunding is a popular channel for people needing financial help paying medical bills to collect donations from large numbers of people. However, large heterogeneity exists in donations across cases, and fundraisers face significant uncertainty in whether their crowdfunding campaigns can meet fundraising goals. Therefore, it is important to provide early warnings for fundraisers if such a channel will eventually fail. In this study, we aim to develop novel algorithms to provide accurate and timely predictions of fundraising performance, to better inform fundraisers. In particular, we propose a new approach to combine time-series features and time-invariant features in the deep learning model, to process diverse sources of input data. Compared with baseline models, our model achieves better accuracy and requires a shorter observation window of the time-varying features from the campaign launch to provide robust predictions with high confidence. To extract interpretable insights, we further conduct a multivariate time-series clustering analysis and identify four typical temporal donation patterns. This demonstrates the heterogeneity in the features and how they relate to the fundraising outcome. The prediction model and the interpretable insights can be applied to assist fundraisers with better promoting their fundraising campaigns and can potentially help crowdfunding platforms to provide more timely feedback to all fundraisers. Our proposed framework is also generalizable to other fields where diverse structured and unstructured data are valuable for predictions.

Suggested Citation

Wang, Tong and Jin, Fujie and Hu, Yu Jeffrey and Cheng, Yuan, Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs (October 27, 2019). Available at SSRN: https://ssrn.com/abstract=3476364

Tong Wang (Contact Author)

University of Iowa ( email )

21 East Market St
PBB
Iowa City, IA 52241
United States

Fujie Jin

Kelley School of Business, Indiana University ( email )

Business 670
1309 E. Tenth Street
Bloomington, IN 47401
United States
812-855-0943 (Phone)

HOME PAGE: http://https://kelley.iu.edu/facultyglobal/directory/FacultyProfile.cfm?netID=jinf

Yu Jeffrey Hu

Georgia Institute of Technology - Scheller College of Business ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States

Yuan Cheng

Tsinghua University - School of Economics & Management ( email )

Beijing, 100084
China

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