TrialPanorama: Database and Benchmark for Systematic Review and Design of Clinical Trials

• URL: http://arxiv.org/abs/2505.16097v1
• Date: Thu, 22 May 2025 00:58:43 GMT
• Title: TrialPanorama: Database and Benchmark for Systematic Review and Design of Clinical Trials
• Authors: Zifeng Wang, Qiao Jin, Jiacheng Lin, Junyi Gao, Jathurshan Pradeepkumar, Pengcheng Jiang, Benjamin Danek, Zhiyong Lu, Jimeng Sun,
• Abstract summary: We introduce TrialPanorama, a large-scale, structured database comprising 1,657,476 clinical trial records from 15 global sources.<n>The database captures key aspects of trial design and execution, including trial setups, interventions, conditions, biomarkers, and outcomes.<n>To demonstrate its utility, we derive a suite of benchmark tasks directly from the TrialPanorama database.
• Score: 27.101541130364343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing artificial intelligence (AI) for vertical domains requires a solid data foundation for both training and evaluation. In this work, we introduce TrialPanorama, a large-scale, structured database comprising 1,657,476 clinical trial records aggregated from 15 global sources. The database captures key aspects of trial design and execution, including trial setups, interventions, conditions, biomarkers, and outcomes, and links them to standard biomedical ontologies such as DrugBank and MedDRA. This structured and ontology-grounded design enables TrialPanorama to serve as a unified, extensible resource for a wide range of clinical trial tasks, including trial planning, design, and summarization. To demonstrate its utility, we derive a suite of benchmark tasks directly from the TrialPanorama database. The benchmark spans eight tasks across two categories: three for systematic review (study search, study screening, and evidence summarization) and five for trial design (arm design, eligibility criteria, endpoint selection, sample size estimation, and trial completion assessment). The experiments using five state-of-the-art large language models (LLMs) show that while general-purpose LLMs exhibit some zero-shot capability, their performance is still inadequate for high-stakes clinical trial workflows. We release TrialPanorama database and the benchmark to facilitate further research on AI for clinical trials.

Related papers

• Towards Evaluating and Building Versatile Large Language Models for Medicine [57.49547766838095]
  We present MedS-Bench, a benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts. MedS-Bench spans 11 high-level clinical tasks, including clinical report summarization, treatment recommendations, diagnosis, named entity recognition, and medical concept explanation. MedS-Ins comprises 58 medically oriented language corpora, totaling 13.5 million samples across 122 tasks.
  arXiv  Detail & Related papers  (2024-08-22T17:01:34Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• Panacea: A foundation model for clinical trial search, summarization, design, and recruitment [29.099676641424384]
  We propose a clinical trial foundation model named Panacea. Panacea is designed to handle multiple tasks, including trial search, trial summarization, trial design, and patient-trial matching. We also assemble a large-scale dataset, named TrialAlign, of 793,279 trial documents and 1,113,207 trial-related scientific papers.
  arXiv  Detail & Related papers  (2024-06-25T21:29:25Z)
• Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology [17.214664001970526]
  We conduct a systematic study on scaling clinical trial matching using large language models (LLMs) Our study is grounded in a clinical trial matching system currently in test deployment at a large U.S. health network.
  arXiv  Detail & Related papers  (2023-08-04T07:51:15Z)
• PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications [49.69824178329405]
  PyTrial provides benchmarks and open-source implementations of a series of machine learning algorithms for clinical trial design and operations. We thoroughly investigate 34 ML algorithms for clinical trials across 6 different tasks, including patient outcome prediction, trial site selection, trial outcome prediction, patient-trial matching, trial similarity search, and synthetic data generation. PyTrial defines each task through a simple four-step process: data loading, model specification, model training, and model evaluation, all achievable with just a few lines of code.
  arXiv  Detail & Related papers  (2023-06-06T21:19:03Z)
• AutoTrial: Prompting Language Models for Clinical Trial Design [53.630479619856516]
  We present a method named AutoTrial to aid the design of clinical eligibility criteria using language models. Experiments on over 70K clinical trials verify that AutoTrial generates high-quality criteria texts.
  arXiv  Detail & Related papers  (2023-05-19T01:04:16Z)
• SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning [67.8195828626489]
  Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. We propose Sequential Predictive mOdeling of clinical Trial outcome (SPOT) that first identifies trial topics to cluster the multi-sourced trial data into relevant trial topics. With the consideration of each trial sequence as a task, it uses a meta-learning strategy to achieve a point where the model can rapidly adapt to new tasks with minimal updates.
  arXiv  Detail & Related papers  (2023-04-07T23:04:27Z)
• Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse [53.797797404164946]
  The study highlights the difficulties faced in sharing tools and resources in this domain. We annotated a corpus of clinical documents according to 12 types of identifying entities. We build a hybrid system, merging the results of a deep learning model as well as manual rules.
  arXiv  Detail & Related papers  (2023-03-23T17:17:46Z)
• Artificial Intelligence for In Silico Clinical Trials: A Review [41.85196749088317]
  In silico trials are clinical trials conducted digitally through simulation and modeling. This article systematically reviews papers under three main topics: clinical simulation, individualized predictive modeling, and computer-aided trial design.
  arXiv  Detail & Related papers  (2022-09-16T14:59:31Z)
• Clinical trial site matching with improved diversity using fair policy learning [56.01170456417214]
  We learn a model that maps a clinical trial description to a ranked list of potential trial sites. Unlike existing fairness frameworks, the group membership of each trial site is non-binary. We propose fairness criteria based on demographic parity to address such a multi-group membership scenario.
  arXiv  Detail & Related papers  (2022-04-13T16:35:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.