Zero-shot generation of synthetic neurosurgical data with large language models

• URL: http://arxiv.org/abs/2502.09566v2
• Date: Mon, 17 Feb 2025 08:04:06 GMT
• Title: Zero-shot generation of synthetic neurosurgical data with large language models
• Authors: Austin A. Barr, Eddie Guo, Emre Sezgin,
• Abstract summary: This study aims to evaluate the capability of zero-shot generation of synthetic neurosurgical data with a large language model (LLM), GPT-4o. Data synthesized with GPT-4o can effectively augment clinical data with small sample sizes, and train ML models for prediction of neurosurgical outcomes.
• Score: 0.7373617024876725
• License:
• Abstract: Clinical data is fundamental to advance neurosurgical research, but access is often constrained by data availability, small sample sizes, privacy regulations, and resource-intensive preprocessing and de-identification procedures. Synthetic data offers a potential solution to challenges associated with accessing and using real-world data (RWD). This study aims to evaluate the capability of zero-shot generation of synthetic neurosurgical data with a large language model (LLM), GPT-4o, by benchmarking with the conditional tabular generative adversarial network (CTGAN). Synthetic datasets were compared to real-world neurosurgical data to assess fidelity (means, proportions, distributions, and bivariate correlations), utility (ML classifier performance on RWD), and privacy (duplication of records from RWD). The GPT-4o-generated datasets matched or exceeded CTGAN performance, despite no fine-tuning or access to RWD for pre-training. Datasets demonstrated high univariate and bivariate fidelity to RWD without directly exposing any real patient records, even at amplified sample size. Training an ML classifier on GPT-4o-generated data and testing on RWD for a binary prediction task showed an F1 score (0.706) with comparable performance to training on the CTGAN data (0.705) for predicting postoperative functional status deterioration. GPT-4o demonstrated a promising ability to generate high-fidelity synthetic neurosurgical data. These findings also indicate that data synthesized with GPT-4o can effectively augment clinical data with small sample sizes, and train ML models for prediction of neurosurgical outcomes. Further investigation is necessary to improve the preservation of distributional characteristics and boost classifier performance.

Related papers

• Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generation [0.7373617024876725]
  We demonstrate the ability to generate high-language tabular data without task-specific fine-tuning or access to real-world data for pre-training. To benchmark GPT-4o, we compared the fidelity and privacy of LLM-generated synthetic data against data generated with the conditional generative adversarial network (CTGAN) Despite the zero-shot approach, GPT-4o outperformed CTGAN in preserving means, 95% confidence intervals, bivariate correlations, and data privacy of RWD, even at amplified sample sizes.
  arXiv  Detail & Related papers  (2025-02-20T12:56:16Z)
• Predicting Extubation Failure in Intensive Care: The Development of a Novel, End-to-End Actionable and Interpretable Prediction System [0.0]
  Predicting extubation failure in intensive care is challenging due to complex data and the severe consequences of inaccurate predictions. Machine learning shows promise in improving clinical decision-making but often fails to account for temporal patient trajectories and model interpretability. This study aimed to develop an actionable, interpretable prediction system for extubation failure using temporal modelling approaches.
  arXiv  Detail & Related papers  (2024-11-27T22:19:47Z)
• Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification [1.7614607439356635]
  We explore the usefulness of synthetic data generated with different generative models from Deep Learning. We investigate the effects of transfer learning, by fine-tuning a synthetically pre-trained model and then adding increasing proportions of real data.
  arXiv  Detail & Related papers  (2024-11-27T15:46:34Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Retrosynthesis prediction enhanced by in-silico reaction data augmentation [66.5643280109899]
  We present RetroWISE, a framework that employs a base model inferred from real paired data to perform in-silico reaction generation and augmentation. On three benchmark datasets, RetroWISE achieves the best overall performance against state-of-the-art models.
  arXiv  Detail & Related papers  (2024-01-31T07:40:37Z)
• Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research [4.475998415951477]
  Generative AI offers a promising approach to generating synthetic images, enhancing dataset diversity. This study investigates the impact of synthetic data supplementation on the performance and generalizability of medical imaging research.
  arXiv  Detail & Related papers  (2023-11-15T21:58:01Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)

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.