TrialSynth: Generation of Synthetic Sequential Clinical Trial Data

• URL: http://arxiv.org/abs/2409.07089v1
• Date: Wed, 11 Sep 2024 08:20:30 GMT
• Title: TrialSynth: Generation of Synthetic Sequential Clinical Trial Data
• Authors: Chufan Gao, Mandis Beigi, Afrah Shafquat, Jacob Aptekar, Jimeng Sun,
• Abstract summary: Variational Autoencoder (VAE) designed to address challenges of generating synthetic time-sequence clinical trial data. Our experiments demonstrate that Trial Synth surpasses the performance of other comparable methods.
• Score: 21.799655542003677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Analyzing data from past clinical trials is part of the ongoing effort to optimize the design, implementation, and execution of new clinical trials and more efficiently bring life-saving interventions to market. While there have been recent advances in the generation of static context synthetic clinical trial data, due to both limited patient availability and constraints imposed by patient privacy needs, the generation of fine-grained synthetic time-sequential clinical trial data has been challenging. Given that patient trajectories over an entire clinical trial are of high importance for optimizing trial design and efforts to prevent harmful adverse events, there is a significant need for the generation of high-fidelity time-sequence clinical trial data. Here we introduce TrialSynth, a Variational Autoencoder (VAE) designed to address the specific challenges of generating synthetic time-sequence clinical trial data. Distinct from related clinical data VAE methods, the core of our method leverages Hawkes Processes (HP), which are particularly well-suited for modeling event-type and time gap prediction needed to capture the structure of sequential clinical trial data. Our experiments demonstrate that TrialSynth surpasses the performance of other comparable methods that can generate sequential clinical trial data, in terms of both fidelity and in enabling the generation of highly accurate event sequences across multiple real-world sequential event datasets with small patient source populations when using minimal external information. Notably, our empirical findings highlight that TrialSynth not only outperforms existing clinical sequence-generating methods but also produces data with superior utility while empirically preserving patient privacy.

Related papers

• Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation [16.067841125848688]
  We introduce a novel Retrieval-Reasoning framework that leverages large language models to generate synthetic clinical trials. Experiments conducted on real clinical trials from the urlClinicalTrials.gov database demonstrate that our synthetic data can effectively augment real datasets. Our findings suggest that LLMs for synthetic clinical trial generation hold promise for accelerating clinical research and upholding ethical standards for patient privacy.
  arXiv  Detail & Related papers  (2024-10-16T11:46:32Z)
• 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)
• Language Interaction Network for Clinical Trial Approval Estimation [37.60098683485169]
  We introduce the Language Interaction Network (LINT), a novel approach that predicts trial outcomes using only the free-text descriptions of the trials. We have rigorously tested LINT across three phases of clinical trials, where it achieved ROC-AUC scores of 0.770, 0.740, and 0.748.
  arXiv  Detail & Related papers  (2024-04-26T14:50:59Z)
• TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction [19.084936647082632]
  We propose TrialDura, a machine learning-based method that estimates the duration of clinical trials using multimodal data. We encode them into Bio-BERT embeddings specifically tuned for biomedical contexts to provide a deeper and more relevant semantic understanding. Our proposed model demonstrated superior performance with a mean absolute error (MAE) of 1.04 years and a root mean square error (RMSE) of 1.39 years compared to the other models.
  arXiv  Detail & Related papers  (2024-04-20T02:12:59Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• 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)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Hide-and-Seek Privacy Challenge [88.49671206936259]
  The NeurIPS 2020 Hide-and-Seek Privacy Challenge is a novel two-tracked competition to accelerate progress in tackling both problems. In our head-to-head format, participants in the synthetic data generation track (i.e. "hiders") and the patient re-identification track (i.e. "seekers") are directly pitted against each other by way of a new, high-quality intensive care time-series dataset.
  arXiv  Detail & Related papers  (2020-07-23T15:50:59Z)

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.