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arxiv: 2606.06990 · v1 · pith:L4RVPEVBnew · submitted 2026-06-05 · 💻 cs.LG

Accelerating Reproducible Research in Synthetic EHR Generation

Jalen Jiang , Chufan Gao , Ethan Rasmussen , Stephen Z. Xie , Jimeng Sun This is my paper

Pith reviewed 2026-06-27 22:36 UTC · model grok-4.3

classification 💻 cs.LG
keywords synthetic EHRbenchmarking frameworkgenerative modelsICD-9 codesprivacy evaluationreproducibilitylongitudinal dataPyHealth
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The pith

A unified pipeline standardizes data handling, training and evaluation for synthetic EHR generators.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a single lightweight benchmarking framework to overcome incompatible codebases and inconsistent protocols that currently block fair comparisons of synthetic EHR models. It organizes the entire process into one pipeline covering data ingestion, standardized training, and an evaluation suite that works the same way for GAN-based and transformer-based generators. The implementation focuses on longitudinal ICD-9 diagnosis codes, reimplements four existing models plus a GPT-2 baseline, and supplies privacy-utility metrics with bootstrapped intervals. A sympathetic reader would care because high-fidelity synthetic records could advance medical research without privacy violations, yet progress is stalled by the inability to compare results reliably. The work also examines long-tailed performance problems and notes how the framework can extend to other data types.

Core claim

We introduce a lightweight, end-to-end benchmarking framework for reproducible synthetic EHR evaluation, organized as a unified pipeline spanning data ingestion, standardized model training, and architecture-agnostic evaluation. Our current implementation targets the generation of longitudinal ICD diagnosis codes and is built on the community-maintained PyHealth library. We reimplement and unify strong baselines (MedGAN, CorGAN, PromptEHR, HALO) under full ICD-9 vocabulary granularity, and add a lightweight GPT-2 baseline. We contribute a rigorous, architecture-agnostic privacy-utility evaluation suite that applies identically to GAN- and transformer-based generators, and report bootstrapped

What carries the argument

The unified pipeline that standardizes data ingestion, model training, and architecture-agnostic privacy-utility evaluation.

If this is right

  • Head-to-head comparisons become possible between GAN-based and transformer-based generators using identical metrics and confidence intervals.
  • Long-tailed performance shortfalls on rare diagnosis codes can be measured consistently across models.
  • The engineering effort to add and test a new generator drops because data loading and evaluation are already standardized.
  • Bootstrapped intervals provide a uniform way to judge whether one model outperforms another on privacy or utility.
  • The same pipeline can be extended to modalities beyond diagnosis codes without rewriting evaluation code.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The framework could become a shared testbed that lets the community track progress on synthetic EHR generation the way image or language benchmarks do.
  • If rare-code performance remains weak under this standardized setup, it points to a deeper limitation in current sequence generators rather than implementation differences.
  • Applying the pipeline to data from multiple hospitals could expose whether models overfit to the training site's coding patterns.
  • A GPT-2 baseline succeeding here would suggest that general-purpose language models warrant more attention as starting points for medical sequence generation.

Load-bearing premise

The reimplementations of MedGAN, CorGAN, PromptEHR, and HALO under full ICD-9 granularity faithfully reproduce the behavior of the original published models.

What would settle it

Running the original published code for any of the reimplemented baselines on the same dataset and obtaining substantially different metric values from those produced inside the unified framework.

Figures

Figures reproduced from arXiv: 2606.06990 by Chufan Gao, Ethan Rasmussen, Jalen Jiang, Jimeng Sun, Stephen Z. Xie.

Figure 1
Figure 1. Figure 1: (Top) A longitudinal patient trajectory is transformed into two formats for gen￾erative modeling. (Left) Flattened approaches remove the temporal dimension. (Middle) In contrast, sequential approaches maintain full sequential informa￾tion, e.g. by using special delimiters for visit boundaries. (Right) To ensure consistent benchmarking, all synthetic outputs are converted into a standardized long-format sch… view at source ↗
Figure 2
Figure 2. Figure 2: Figure that shows that full ICD-9 codes in MIMIC are much more complex and long-tailed. I.e., a minority of common codes dominate the absolute counts of all ICD codes in the patient EHR compared to the simpler first 3 characters. A Lack of Consideration for Long-tailed Codes Removing granularity from syn￾thetic codes has become an entrenched norm: researchers commonly reduce ICD-9’s 6,955 diagnosis codes t… view at source ↗
Figure 2
Figure 2. Figure 2: Impact of Long-Tailed Code Distributions on Generative Utility and [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Rank–frequency distribution of patient-level ICD-9 prevalence (log–log). HALO best tracks the real long tail; CorGAN and PromptEHR truncate earliest. Top-20 code prevalence [PITH_FULL_IMAGE:figures/full_fig_p030_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Patient-level prevalence of the 20 most common real ICD-9 codes, across each synthetic generator. CorGAN under-predicts even the head of the distribution. Per-code scatter [PITH_FULL_IMAGE:figures/full_fig_p031_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Per-code real vs. synthetic patient-level prevalence (symlog scale). Each dot is one ICD-9 code. Dashed line is y = x. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Cumulative prevalence mass as a function of the top-k codes. Real MIMIC-III decays smoothly; models that saturate early concentrate too much mass in a narrow head of the distribution. Per-bucket prevalence fit [PITH_FULL_IMAGE:figures/full_fig_p032_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Per-bucket Pearson R2 between real and synthetic prevalence, in 10 equal-size frequency buckets (Q1 = most common 10% of real codes, Q10 = rarest 10%). The aggregate Prevalence R2 reported in the main text is effectively dominated by the leftmost buckets [PITH_FULL_IMAGE:figures/full_fig_p033_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of visits per patient for sequential generators vs. real MIMIC-III. Flattened baselines are omitted because they produce one pseudo-visit per patient by construction. Codes per visit [PITH_FULL_IMAGE:figures/full_fig_p034_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Distribution of unique codes per visit for sequential generators. All three models under-cover the upper tail of real visit lengths. G.4. ICD-9 chapter-level coverage To detect coarse category-level bias, we group codes into ICD-9-CM chapters (Circulatory, Endocrine/Metabolic, Respiratory, Supplementary “V” codes, etc.) and plot the fraction of patients whose record contains at least one code in each chap… view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of unique codes per patient. Flattened baselines benefit from pool￾ing all of a patient’s codes into a single multi-hot vector, while sequential models must recover this distribution implicitly through visit-level generation [PITH_FULL_IMAGE:figures/full_fig_p036_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Fraction of patients with at least one code in each ICD-9 chapter. This coarse￾grained view complements the per-code prevalence analysis by checking for cat￾egorical bias. Real HALO PromptEHR GPT MedGAN CorGAN 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Co-occurrence rate Pairwise code co-occurrence matrix (top-50 codes) [PITH_FULL_IMAGE:figures/full_fig_p037_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Patient-level pairwise code co-occurrence matrix on the 50 most common real ICD-9 codes. Rows/columns are sorted by real prevalence. All heatmaps share the same color scale, set by the real matrix maximum. long tail, which is consistent with its smaller vocabulary size and relatively cheap training budget. We view these findings as evidence that the single Prevalence R2 / Discriminative Score pair reporte… view at source ↗
read the original abstract

The generation of high-fidelity synthetic Electronic Health Records (EHR) is crucial for advancing medical research while preserving patient privacy. However, head-to-head comparison of existing generative models is hindered by disjointed codebases, incompatible data loaders, conflicting library dependencies, and inconsistent evaluation protocols. To address these gaps, we introduce a lightweight, end-to-end benchmarking framework for reproducible synthetic EHR evaluation, organized as a unified pipeline spanning data ingestion, standardized model training, and architecture-agnostic evaluation. Our current implementation targets the generation of longitudinal ICD diagnosis codes -- the most commonly studied modality in this literature -- and is built on the community-maintained PyHealth library. We reimplement and unify strong baselines (MedGAN, CorGAN, PromptEHR, HALO) under full ICD-9 vocabulary granularity, and add a lightweight GPT-2 baseline from the general-purpose sequence-modeling literature. We contribute a rigorous, architecture-agnostic privacy-utility evaluation suite that applies identically to GAN- and transformer-based generators, and report bootstrapped confidence intervals across all metrics. We further analyze the poor long-tailed performance of existing models and discuss the extensibility of our framework beyond diagnosis codes. By lowering the engineering barrier to running, extending, and evaluating under a single pipeline, we introduce a starting point for community-driven reproducibility and benchmarking synthetic EHR models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces a lightweight end-to-end benchmarking framework for reproducible synthetic EHR evaluation. The framework unifies data ingestion, standardized training of generative models (reimplementations of MedGAN, CorGAN, PromptEHR, HALO at full ICD-9 granularity, plus a GPT-2 baseline), and an architecture-agnostic evaluation suite for privacy and utility metrics with bootstrapped confidence intervals. It is built on PyHealth and targets longitudinal ICD diagnosis codes, while analyzing long-tailed performance and discussing extensibility.

Significance. If the reimplementations are faithful and the framework sees community adoption, the work could meaningfully lower the engineering barrier to reproducible benchmarking in synthetic EHR generation. The architecture-agnostic evaluation protocol and use of bootstrapped CIs represent concrete strengths that would support fairer cross-model comparisons if validated.

major comments (2)
  1. [Reimplementation of baselines] The central claim of a meaningful unified benchmarking pipeline rests on the assumption that the reimplementations of MedGAN, CorGAN, PromptEHR, and HALO at full ICD-9 granularity faithfully recover the original models' behavior. The manuscript supplies no side-by-side metric tables or numerical comparisons against the performance numbers reported in the source papers for these models. This verification step is load-bearing; without it, observed differences in the new evaluation suite cannot be attributed to model properties rather than implementation drift or vocabulary changes.
  2. [Evaluation and results] The abstract states that bootstrapped confidence intervals are reported across all metrics and that the poor long-tailed performance of existing models is analyzed, yet the manuscript text contains no tables, figures, or numerical results demonstrating these outputs. This absence leaves the evaluation suite without direct evidence of its operation or findings.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, clarifying the manuscript's scope while committing to revisions that strengthen the presentation of results and verification.

read point-by-point responses

  1. Referee: [Reimplementation of baselines] The central claim of a meaningful unified benchmarking pipeline rests on the assumption that the reimplementations of MedGAN, CorGAN, PromptEHR, and HALO at full ICD-9 granularity faithfully recover the original models' behavior. The manuscript supplies no side-by-side metric tables or numerical comparisons against the performance numbers reported in the source papers for these models. This verification step is load-bearing; without it, observed differences in the new evaluation suite cannot be attributed to model properties rather than implementation drift or vocabulary changes.

    Authors: We agree that explicit verification would strengthen confidence in the reimplementations. The originals often employ different preprocessing pipelines, vocabulary granularities, and train/test splits, which complicates direct numerical matching. Our primary contribution is the unified pipeline under consistent conditions rather than exact replication of prior numbers. In revision we will add a supplementary table with available side-by-side metrics from our reimplementations versus the source papers (where setups permit), together with a discussion of any discrepancies and a pointer to the open-source code for independent inspection. revision: yes

  2. Referee: [Evaluation and results] The abstract states that bootstrapped confidence intervals are reported across all metrics and that the poor long-tailed performance of existing models is analyzed, yet the manuscript text contains no tables, figures, or numerical results demonstrating these outputs. This absence leaves the evaluation suite without direct evidence of its operation or findings.

    Authors: We acknowledge the referee's observation. The current manuscript version emphasizes the framework design and leaves the concrete numerical demonstration of the evaluation suite implicit. In the revised manuscript we will insert the missing tables and figures that report bootstrapped confidence intervals for all privacy and utility metrics, along with the long-tailed performance analysis, ensuring they are explicitly referenced from the abstract and methods sections. revision: yes

Circularity Check

0 steps flagged

No circularity: engineering framework paper with no derivations, fits, or self-referential predictions

full rationale

The manuscript introduces a unified benchmarking pipeline and reimplementations of existing models (MedGAN, CorGAN, PromptEHR, HALO) plus a GPT-2 baseline, together with an architecture-agnostic evaluation suite. No mathematical derivations, parameter-fitting steps, uniqueness theorems, or predictive claims appear in the provided text. The contribution is a software artifact whose correctness is intended to be verified externally via adoption and community use rather than by internal reduction to fitted inputs or self-citations. Consequently the derivation chain is empty and the circularity score is 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is a software framework contribution with no free parameters, mathematical axioms, or invented entities; all components are reimplementations or extensions of existing libraries and models.

pith-pipeline@v0.9.1-grok · 5776 in / 1088 out tokens · 20375 ms · 2026-06-27T22:36:07.616792+00:00 · methodology

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Reference graph

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