HINT: Hierarchical interaction network for clinical-trial-outcome predictions

Tianfan Fu; Kexin Huang; Cao Xiao; Lucas M Glass; Jimeng Sun

doi:10.1016/j.patter.2022.100445

HINT: Hierarchical interaction network for clinical-trial-outcome predictions

Patterns (N Y). 2022 Feb 3;3(4):100445. doi: 10.1016/j.patter.2022.100445. eCollection 2022 Apr 8.

Authors

Tianfan Fu¹, Kexin Huang², Cao Xiao³, Lucas M Glass^{4

5}, Jimeng Sun⁶

Affiliations

¹ Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
² Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
³ Amplitude, San Francisco, CA 94105, USA.
⁴ Analytics Center of Excellence, IQVIA, Cambridge, MA 02139, USA.
⁵ Department of Statistics, Temple University, Philadelphia, PA 19122, USA.
⁶ Computer Science Department and Carle's Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61820, USA.

Abstract

Clinical trials are crucial for drug development but often face uncertain outcomes due to safety, efficacy, or patient-recruitment problems. We propose the Hierarchical Interaction Network (HINT) to predict clinical trial outcomes. First, HINT encodes multi-modal data (drug molecule, target disease, trial eligibility criteria) into embeddings. Then, HINT trains knowledge-embedding modules using drug pharmacokinetic and historical trial data. Finally, a hierarchical interaction graph connects all of the embeddings to capture their interactions and predict trial outcomes. HINT was trained and validated on 1,160 phase I trials, 4,449 phase II trials, and 3,436 phase III trials. It obtained 0.665, 0.620, and 0.847 F1 scores on separate test sets of 627 phase I, 1,653 phase II, and 1,140 phase III trials, respectively. HINT significantly outperforms the best baseline method on most metrics. The benchmark dataset and codes are released at https://github.com/futianfan/clinical-trial-outcome-prediction.

Keywords: clinical-trial-outcome prediction; data mining; deep learning; graph neural network; neural network; trial outcome prediction benchmark.