A text-to-tabular approach to generate synthetic patient data using LLMs | Paper Citation Record

Observation 7c74edc3-ff4b-4702-a912-7bc3b5cfc453 · outbound

This paper cites Synthetic data in health care: A narrative review,.

A text-to-tabular approach to generate synthetic patient data using LLMs Synthetic data in health care: A narrative review,

Reference 1

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This paper cites Generation and evaluation of privacy preserving synthetic health data,.

A text-to-tabular approach to generate synthetic patient data using LLMs Generation and evaluation of privacy preserving synthetic health data,

Reference 2

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This paper cites Syn- thetic data generation for tabular health records: A systematic review,.

A text-to-tabular approach to generate synthetic patient data using LLMs Syn- thetic data generation for tabular health records: A systematic review,

Reference 3

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This paper cites Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence,.

A text-to-tabular approach to generate synthetic patient data using LLMs Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence,

Reference 4

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This paper cites The Effectiveness of Data Augmentation in Image Classification using Deep Learning.

A text-to-tabular approach to generate synthetic patient data using LLMs The Effectiveness of Data Augmentation in Image Classification using Deep Learning

Reference 5

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This paper cites Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes.

A text-to-tabular approach to generate synthetic patient data using LLMs Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes

Reference 6

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This paper cites Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance,.

A text-to-tabular approach to generate synthetic patient data using LLMs Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance,

Reference 7

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This paper cites T1dCteGui: A User- Friendly Clinical Trial Enrichment Tool to Optimize T1D Prevention Studies by Leveraging AI/ML Based Synthetic Patient Population,.

A text-to-tabular approach to generate synthetic patient data using LLMs T1dCteGui: A User- Friendly Clinical Trial Enrichment Tool to Optimize T1D Prevention Studies by Leveraging AI/ML Based Synthetic Patient Population,

Reference 8

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This paper cites Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma,.

A text-to-tabular approach to generate synthetic patient data using LLMs Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma,

Reference 9

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Observation c1a1ae47-b6f1-41a7-b55d-c2919ab1002d · outbound

This paper cites Digital Twin Generators for Disease Modeling.

A text-to-tabular approach to generate synthetic patient data using LLMs Digital Twin Generators for Disease Modeling

Reference 10

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This paper cites Language Models are Realistic Tabular Data Generators.

A text-to-tabular approach to generate synthetic patient data using LLMs Language Models are Realistic Tabular Data Generators

Reference 11

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This paper cites Deep Neural Networks and Tabular Data: A Survey.

A text-to-tabular approach to generate synthetic patient data using LLMs Deep Neural Networks and Tabular Data: A Survey

Reference 12

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This paper cites Using Bayesian Networks to Create Synthetic Data,.

A text-to-tabular approach to generate synthetic patient data using LLMs Using Bayesian Networks to Create Synthetic Data,

Reference 13

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This paper cites PrivBayes: Private Data Release via Bayesian Networks,.

A text-to-tabular approach to generate synthetic patient data using LLMs PrivBayes: Private Data Release via Bayesian Networks,

Reference 14

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This paper cites The Synthetic Data Vault,.

A text-to-tabular approach to generate synthetic patient data using LLMs The Synthetic Data Vault,

Reference 15

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Observation b2acc7eb-bce7-4cbe-9ca7-cf5de5765ecb · outbound

This paper cites Copula Flows for Synthetic Data Generation.

A text-to-tabular approach to generate synthetic patient data using LLMs Copula Flows for Synthetic Data Generation

Reference 16

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This paper cites Generative Adversarial Networks.

A text-to-tabular approach to generate synthetic patient data using LLMs Generative Adversarial Networks

Reference 17

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This paper cites Modeling Tabular data using Conditional GAN.

A text-to-tabular approach to generate synthetic patient data using LLMs Modeling Tabular data using Conditional GAN

Reference 18

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This paper cites CTAB-GAN: Effective Table Data Synthesizing.

A text-to-tabular approach to generate synthetic patient data using LLMs CTAB-GAN: Effective Table Data Synthesizing

Reference 19

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This paper cites Differentially Private Generative Adversarial Network.

A text-to-tabular approach to generate synthetic patient data using LLMs Differentially Private Generative Adversarial Network

Reference 20

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This paper cites PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees,.

A text-to-tabular approach to generate synthetic patient data using LLMs PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees,

Reference 21

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This paper cites CTAB-GAN+: Enhancing Tabular Data Synthesis.

A text-to-tabular approach to generate synthetic patient data using LLMs CTAB-GAN+: Enhancing Tabular Data Synthesis

Reference 22

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This paper cites Structured Denoising Diffusion Models in Discrete State- Spaces,.

A text-to-tabular approach to generate synthetic patient data using LLMs Structured Denoising Diffusion Models in Discrete State- Spaces,

Reference 23

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This paper cites TabDDPM: Modelling Tabular Data with Diffusion Models.

A text-to-tabular approach to generate synthetic patient data using LLMs TabDDPM: Modelling Tabular Data with Diffusion Models

Reference 24

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A text-to-tabular approach to generate synthetic patient data using LLMs Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees,

Reference 25

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This paper cites REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers.

A text-to-tabular approach to generate synthetic patient data using LLMs REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 26

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This paper cites TabuLa: Harnessing Language Models for Tabular Data Synthesis.

A text-to-tabular approach to generate synthetic patient data using LLMs TabuLa: Harnessing Language Models for Tabular Data Synthesis

Reference 27

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This paper cites Differentially Private Tabular Data Synthesis using Large Language Models.

A text-to-tabular approach to generate synthetic patient data using LLMs Differentially Private Tabular Data Synthesis using Large Language Models

Reference 28

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This paper cites MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data.

A text-to-tabular approach to generate synthetic patient data using LLMs MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data

Reference 29

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This paper cites Why LLMs Are Bad at Synthetic Table Generation (and what to do about it).

A text-to-tabular approach to generate synthetic patient data using LLMs Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)

Reference 30

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This paper cites Creating virtual patients using large language models: scalable, global, and low cost,.

A text-to-tabular approach to generate synthetic patient data using LLMs Creating virtual patients using large language models: scalable, global, and low cost,

Reference 31

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This paper cites Parkinson’s disease symptoms: The patient’s perspective,.

A text-to-tabular approach to generate synthetic patient data using LLMs Parkinson’s disease symptoms: The patient’s perspective,

Reference 32

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A text-to-tabular approach to generate synthetic patient data using LLMs Alzheimer Disease,

Reference 33

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This paper cites Elephants Never Forget: Testing Language Models for Memorization of Tabular Data.

A text-to-tabular approach to generate synthetic patient data using LLMs Elephants Never Forget: Testing Language Models for Memorization of Tabular Data

Reference 34

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This paper cites The Parkinson’s progression markers initiative (PPMI) – establishing a PD biomarker cohort,.

A text-to-tabular approach to generate synthetic patient data using LLMs The Parkinson’s progression markers initiative (PPMI) – establishing a PD biomarker cohort,

Reference 35

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This paper cites A Comparison of Accelerated and Non-accelerated MRI Scans for Brain V olume and Boundary Shift Integral Measures of V olume Change: Evidence from the ADNI Dataset,.

A text-to-tabular approach to generate synthetic patient data using LLMs A Comparison of Accelerated and Non-accelerated MRI Scans for Brain V olume and Boundary Shift Integral Measures of V olume Change: Evidence from the ADNI Dataset,

Reference 36

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This paper cites Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data,.

A text-to-tabular approach to generate synthetic patient data using LLMs Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data,

Reference 37

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This paper cites [Online].

A text-to-tabular approach to generate synthetic patient data using LLMs [Online]

Reference 38

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This paper cites On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests.

A text-to-tabular approach to generate synthetic patient data using LLMs On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests

Reference 39

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This paper cites Divergence measures based on the Shannon entropy,.

A text-to-tabular approach to generate synthetic patient data using LLMs Divergence measures based on the Shannon entropy,

Reference 40

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This paper cites Distinctive Image Features from Scale-Invariant Keypoints,.

A text-to-tabular approach to generate synthetic patient data using LLMs Distinctive Image Features from Scale-Invariant Keypoints,

Reference 41

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This paper cites Gender Differences in the Prevalence of Parkinson’s Disease,.

A text-to-tabular approach to generate synthetic patient data using LLMs Gender Differences in the Prevalence of Parkinson’s Disease,

Reference 42

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This paper cites Quantitative Measurement of Rigidity in Parkinson’s Disease: A Systematic Review,.

A text-to-tabular approach to generate synthetic patient data using LLMs Quantitative Measurement of Rigidity in Parkinson’s Disease: A Systematic Review,

Reference 43

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This paper cites Generation and evaluation of synthetic patient data,.

A text-to-tabular approach to generate synthetic patient data using LLMs Generation and evaluation of synthetic patient data,

Reference 44

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This paper cites How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models.

A text-to-tabular approach to generate synthetic patient data using LLMs How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models

Reference 45

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A text-to-tabular approach to generate synthetic patient data using LLMs Synthetic data, real errors: how (not) to publish and use synthetic data

Reference 46

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This paper cites For up-to-date information on the study, visit www.ppmi-info.org.

A text-to-tabular approach to generate synthetic patient data using LLMs For up-to-date information on the study, visit www.ppmi-info.org

Reference 49

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This paper cites As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analy- sis or writing of this report.

A text-to-tabular approach to generate synthetic patient data using LLMs As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analy- sis or writing of this report

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:46.955340Z

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A text-to-tabular approach to generate synthetic patient data using LLMs Unresolved cited work

Reference 51

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This paper cites The Euclidean distance is computed for each point of the synthetic data and then averaged.

A text-to-tabular approach to generate synthetic patient data using LLMs The Euclidean distance is computed for each point of the synthetic data and then averaged

Reference 52

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A text-to-tabular approach to generate synthetic patient data using LLMs The evaluation process involves training the ML algorithm with synthetic data

Reference 53

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This paper cites Available: http://ieeexplore.ieee.org/document/7796926/.

A text-to-tabular approach to generate synthetic patient data using LLMs Available: http://ieeexplore.ieee.org/document/7796926/

Reference 410

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This paper cites Available: https://bmcmedresmethodol.biomedcentral.

A text-to-tabular approach to generate synthetic patient data using LLMs Available: https://bmcmedresmethodol.biomedcentral

Reference 2020

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