Deep learning methods for clinical trial design, execution, and analysis

Wang, Zifeng

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https://hdl.handle.net/2142/129374 Copy

Description

Title

Deep learning methods for clinical trial design, execution, and analysis

Author(s)

Wang, Zifeng

Issue Date

2025-03-18

Director of Research (if dissertation) or Advisor (if thesis)

Sun, Jimeng

Doctoral Committee Chair(s)

Sun, Jimeng

Committee Member(s)

• Han, Jiawei
• Tong, Hanghang
• Lu, Zhiyong
• Wang, Sheng

Department of Study

Siebel School Comp & Data Sci

Discipline

Computer Science

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• deep learning
• clinical trial
• large language model

Abstract

Clinical trials are essential for developing new treatments, where drugs are tested on carefully selected cohorts in controlled settings and must pass rigorous safety and efficacy standards across all trial phases before receiving approval and becoming available to patients. Despite their importance, the current process of conducting clinical trials is often lengthy, costly, and burdensome for both patients and researchers. On average, it takes 10~15 years and costs $2.87 billion for a drug to complete all trial phases, with most drug candidates failing the trials. The vast and diverse data generated throughout this process, including clinical documents, medical images, and patient records, presents an opportunity for deep learning methods to leverage historical patterns and streamline clinical trial tasks. This thesis focuses on developing deep learning methods to enhance clinical trials across their entire lifecycle, encompassing design, execution, and analysis. First, we explore AI-driven approaches to streamline the review of clinical studies in the literature, facilitating the generation of clinical evidence and novel hypotheses. Additionally, we investigate patient and trial outcome prediction models, which serve as a foundation for trial simulation and design optimization. Next, we introduce methods that improve clinical trial document drafting through enhanced retrieval and generation, alongside techniques for assessing participant eligibility to streamline recruitment. Finally, we explore AI-driven code generation methods for analyzing clinical trial data, enabling more efficient result reporting and evidence generation. Through these contributions, this thesis advances the application of deep learning in medical science discovery, with a particular emphasis on accelerating and optimizing clinical trials.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129374

Copyright and License Information

Copyright 2025 Zifeng Wang

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