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REVIEW 4 major objections 5 minor 57 references

Early Diagnosis of Atrial Fibrillation Recurrence: A Large Tabular Model Approach with Structured and Unstructured Clinical Data

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A large tabular model trained on merged structured and free-text EHR data predicts atrial fibrillation recurrence one to two years after onset better than clinical scores (CHADS2-VASc, HATCH, APPLE) and a feature-engineered SVM, reaching…

desk verdict Solid applied study with a reusable NLP-plus-EHR pipeline and an honest small effect; the 'surpassing' claim overreaches the evidence, but the paper deserves peer review with required revisions. read the letter →

arxiv 2505.14643 v1 pith:BHOIIA7A submitted 2025-05-20 cs.LG

Ane G. Domingo-Aldama , Marcos Merino Prado , Alain García Olea , Koldo Gojenola Galletebeitia , Josu Goikoetxea Salutregi , Aitziber Atutxa Salazar This is my paper · ORCID
classification cs.LG
keywords AtrialfibrillationrecurrenceElectronichealthrecordsLargetabularmodelsNaturallanguageprocessingClinicalscoresSilverlabelsTabPFNEarlydiagnosis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to show that a large tabular model, TabPFN, can predict whether atrial fibrillation will recur between one month and two years after a first documented episode, and that it beats the clinical scores currently used for this task. To do this, the authors build a tabular dataset for 1,508 patients by merging codified electronic health record variables with features automatically extracted from Spanish discharge reports using a regex and language-model pipeline. The training labels are generated automatically ("silver") and checked against manually annotated test labels, with 83% agreement. On the manually annotated test set of 278 patients, TabPFN reaches a Matthews correlation coefficient of 0.1886, versus 0.1626 for SVM and 0.0832 for the best clinical score, HATCH. If true, this would give clinicians a data-driven, minimally annotated tool for earlier rhythm-control decisions.

What carries the argument

The load-bearing object is TabPFN, a large tabular model pretrained on millions of synthetic tables that is designed to perform well on small datasets with missing values and many features, requiring no hyperparameter tuning or feature preprocessing. The second component is the three-step text-to-tabular pipeline: Report2Vector converts each Spanish discharge report into an 86-variable vector using section identification, medical entity recognition with negation detection, and cardiologist-designed regular expressions; Structured2Vector extracts codified variables from the hospital's business intelligence system; VectorMerger merges them into one row per patient with time-window constraints, and also assigns the silver recurrence labels by looking for AF evidence in follow-up reports one month to two years after onset. The claim depends on this pipeline producing both the predictive features and the training labels.

What would settle it

Have clinicians re-annotate the 978-patient training set manually, retrain TabPFN on those gold labels, and evaluate on the same 278-patient test set; if the advantage over HATCH largely disappears, or if the original model performs at chance on test cases where the silver and gold labels disagree, the reported MCC was an artifact of learning the automatic labelling pipeline.

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Extended reading notes

Core claim

The paper's central claim is that a tabular foundation model, TabPFN, predicts atrial fibrillation recurrence between one month and two years after a first documented episode with higher accuracy than the commonly used clinical scores CHADS2-VASc, HATCH, and APPLE, and also than a support vector machine that requires extensive preprocessing. The model is trained on a dataset of 978 automatically labeled patients and evaluated on 278 manually annotated patients; the best variant, TabPFN-pre, achieves a Matthews correlation coefficient of 0.1886 on the held-out set, compared with 0.1626 for SVM and 0.0832 for HATCH, with the difference from SVM reported as statistically significant (paired t-test p=0.0118). The study also claims that the underlying data-generation methodology—merging structured codified EHR data with features extracted from free-text discharge reports through a regex and language-model pipeline—reduces annotation effort and mitigates coding errors, and that this methodology is generalizable to other diseases. In subgroup analyses, the paper finds that models and scores perform worse on male patients and that for patients under 75 TabPFN reaches MCC around 0.27, roughly double the HATCH score.

Load-bearing premise

The automatic silver labels for the training set are generated by the same regular-expression and language-model pipeline that extracts the predictive features, and they agree with expert manual labels in only 83% of cases, so systematic labelling errors could make the model learn the pipeline's output rather than true recurrence.

Editorial extensions

If this is right

  • If the result holds, cardiologists could obtain a recurrence risk estimate for the one-month-to-two-year window from a model that needs no manual feature engineering, only tabular data plus discharge reports.
  • The comparison quantifies how much predictive information the three clinical scores leave unused: HATCH, the best score, reaches only MCC 0.0832, close to chance-level discrimination for this outcome.
  • The silver-labelling approach would allow other health systems to build predictive models for outcomes that are not explicitly coded, provided they have discharge reports and a small set of manual labels for validation.
  • For patients under 75, where rhythm-control decisions matter most, TabPFN's MCC roughly doubles that of HATCH, suggesting the model could support treatment personalization precisely in the group where clinical guidance is least settled.
  • The gender-stratified results imply that a model acceptable on the full cohort may underperform on male patients; age differences between male and female cohorts account for part of the gap.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct corollary the authors do not stress: since features and labels are produced by the same regexes, the model's superiority over HATCH may in part reflect learning the annotator's decision rules; an independent gold-label retraining is needed to know how much real clinical signal remains.
  • The 83% silver-gold agreement effectively caps the achievable gold MCC under label noise; if the noise is non-random, even a perfect model could not exceed the observed 0.19 on this test set.
  • The claimed generalizability to other diseases is stronger for the vector-merging procedure than for the label generator, since the regular expressions and AF-specific flags would have to be redesigned per pathology.
  • A testable extension: run the same TabPFN-versus-scores comparison using only structured data, isolating how much of the performance gain comes from the NLP-extracted features rather than from the model architecture.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript proposes a method for predicting atrial fibrillation (AF) recurrence between one month and two years after the first documented AF onset, using a tabular dataset generated by merging structured EHR data from the Osakidetza Business Intelligence system with information extracted from Spanish discharge reports. A silver training set of 978 patients is labeled automatically by a three-step NLP/regex pipeline, while a gold test set of 278 patients is manually annotated by the cardiology department. The study compares clinical scores (CHADS2-VASc, HATCH, APPLE), classical ML models (SVM, LR, XGB), and the TabPFN large tabular model in two variants (preprocessed and raw). The central claim is that TabPFN-pre achieves the highest predictive performance, with MCC 0.1886 versus 0.1626 for SVM and 0.0832 for HATCH; additional experiments examine gender and age subgroups. The paper positions the dataset-generation methodology and the two-year prediction horizon as the main contributions, and it makes code publicly available.

Significance. If the results are validated, the paper would make a useful contribution to clinically oriented ML for AF recurrence: it addresses a less-studied time window, combines structured and free-text EHR sources, and uses a manually annotated gold test set while reducing annotation effort through silver labels. The public code, the explicit comparison with established clinical scores, and the gender/age subgroup analyses are all strengths. However, the central superiority claim rests on a small MCC gap with an unspecified significance test, and the silver-labeling procedure creates a potential feedback loop between feature extraction and label assignment. These issues affect the main scientific conclusion and need to be addressed before the claims can be accepted.

major comments (4)
  1. [Section 5.2 and Section 6] The central claim in the abstract that the LTM 'surpassing both traditional clinical scores and ML models' is not robustly supported by Table 3. The difference between TabPFN-pre (MCC 0.1886) and SVM (MCC 0.1626) is only 0.026 on a 278-patient test set, and the reported paired t-test (footnote 9, p=0.0118) does not specify what quantity was tested; a paired t-test on per-patient predicted probabilities does not establish a statistically significant difference in MCC, and no multiple-comparison correction is reported. In addition, Section 6 concludes that the LTM performed 'a la par' with the SVM-based model, directly contradicting the abstract's stronger claim. Please provide bootstrap or permutation confidence intervals for MCC itself, and reconcile the wording between the abstract and the conclusion.
  2. [Section 3.2.2 and Appendix C] The silver training labels are produced by the same Report2Vector and VectorMerger pipeline that generates the predictive features; the recurrence label is a downstream function of the same discharge-report sections and AF flags that enter the feature vector. Because the silver labels agree with expert manual labels in only 83% of cases (Section 3.2.2), systematic pipeline errors could be learned by the model, making the reported MCC partly a measure of agreement with the pipeline rather than with true recurrence. The paper should quantify this risk, for example by retraining on the gold-labeled patients only, by examining feature distributions for concordant versus discordant cases, or by a label-noise robustness analysis.
  3. [Section 3.2.1 and Section 5.2] The paper's methodological claim that integrating unstructured free text with structured codified data enhances dataset quality and predictive performance is not tested by an ablation. All models in Table 3 are evaluated on the enriched dataset, so the reader cannot determine whether the NLP-extracted variables add any predictive value beyond the structured OBI variables alone. Please report results for models trained on structured-only data and on the enriched data, using the same gold test set and the same model architectures.
  4. [Section 5.3 and Section 5.4] The gender and age subgroup conclusions are based on the same unspecified paired t-test (footnotes 10, 11, and 13) and on small subgroups. For example, the TabPFN-pre MCC difference between the female and male subsets in Table 5 is 0.1747 versus 0.0830, but no confidence intervals or interaction test are provided, and the reported p-values are not adjusted for multiple comparisons. The disparity claims should be supported by an appropriate subgroup analysis, ideally with a formal interaction term or bootstrap intervals.
minor comments (5)
  1. [Section 3.3 and Table 1] Table 1 reports a training set of 1,200 patients, while Section 3.3 states that the final training set after exclusions consisted of 978 patients; the table should be updated or the relationship between the two numbers clarified.
  2. [Section 4.4] The note that 'ROC-AUC is generated including predictions that obtained insufficient sensitivity and specificity' is unclear and should be rephrased to describe the metric's threshold-free property more precisely.
  3. [Table 4] The value '68,49' uses a comma as a decimal separator while other values use periods; please standardize the number formatting throughout.
  4. [Section 6] The phrase 'a la par' should be replaced with a precise comparative statement, since it directly conflicts with the abstract's claim of surpassing ML models.
  5. [Appendix A] The 'Type' column for 'Atrial Fibrillation' lists 'Categorical (c = 4)' but the percentages show only three non-zero values; please check the consistency of the reported categories.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central TabPFN-vs-baselines comparison is evaluated on a manually annotated gold test set, and the self-cited NLP components are auxiliary, not load-bearing.

full rationale

The manuscript's central claim is an empirical performance comparison on a held-out test set manually annotated by the BioBizkaia cardiology department (Section 3.2.2 and Table 3), so the superiority claim is not defined in terms of the fitted model's own outputs. The automatic silver labels for the 978-patient training set are produced by the same Report2Vector / regular-expression pipeline used to build the tabular features (Sections 3.2.1-3.2.2 and Appendix C); this is a real data-quality and possible label-noise concern, but the paper reports only 83% agreement with expert annotation and does not equate the silver label with the gold label, nor does any equation reduce a predicted metric to a fitted value. Self-citations (EriBERTa in de la Iglesia et al. 2023 and the section-identification tool in Goenaga et al. 2021) support auxiliary text-processing components with their own validation and are not used to forbid alternatives. The statistical-significance footnote (paired t-test, p=0.0118) is under-specified and the abstract's 'surpassing ML models' is in tension with the conclusion's 'a la par' phrasing, but these are correctness/reporting issues, not circularity. Therefore the derivation chain is self-contained for circularity purposes.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper relies on three resources: manual cardiology annotation as ground truth, a regex and LM pipeline's silver labels as training signal, and TabPFN's pretrained prior. None are derived here, and the silver-label agreement of 83% is the largest unmeasured source of uncertainty. Clinical score thresholds from the literature are used as fixed cut-offs rather than fitted, but they influence the comparison.

free parameters (3)
  • Clinical score thresholds = CHA2DS2-VASc >=2, HATCH >=2, APPLE >=2
    Chosen by the collaborating cardiologist based on literature; they convert continuous scores into binary recurrence labels for comparison, and the thresholds affect the reported MCC.
  • LSFM feature retention fraction = top 25% features
    Hand-chosen in preprocessing for XGBoost; affects XGB's reported MCC of 0.14.
  • Undersampling target balance = 50-50 recurrence to non-recurrence
    Chosen to address class imbalance; affects the classification threshold and reported metrics for the classic ML models.
assumptions (4)
  • domain assumption Manual annotation by the cardiology department is the ground truth for recurrence.
    Test label quality is assumed perfect and is used to evaluate all models and to estimate the 83% silver-label accuracy in Section 3.2.2.
  • domain assumption The automatic NLP and regex pipeline's silver labels are sufficiently accurate for training.
    The 978-patient training set is labelled by the same regex and LM tools that extract features; the paper relies on 83% agreement with manual labels but does not model the effect of label noise on training.
  • domain assumption The recurrence definition (a clinical AF event between one month and two years after onset) is the clinically relevant target.
    The entire label and feature design follows this definition set by the cardiology unit in Section 3.2.2; changing the window would change all reported results.
  • domain assumption TabPFN's pretraining on synthetic tabular datasets transfers to this clinical table.
    The paper argues TabPFN is suited to small, high-dimensional data without fine-tuning (Section 4.3); if the prior-data distribution mismatches EHR data, the reported performance may not transfer.

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Pith. "Pith review of Early Diagnosis of Atrial Fibrillation Recurrence: A Large Tabular Model Approach with Structured and Unstructured Clinical Data." pith.science (2026). https://pith.science/paper/BHOIIA7A

@misc{pith2026250514643,
  author       = {Pith},
  title        = {Pith review of: Early Diagnosis of Atrial Fibrillation Recurrence: A Large Tabular Model Approach with Structured and Unstructured Clinical Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BHOIIA7A}},
  note         = {Machine review of arXiv:2505.14643}
}
read the original abstract

BACKGROUND: Atrial fibrillation (AF), the most common arrhythmia, is linked to high morbidity and mortality. In a fast-evolving AF rhythm control treatment era, predicting AF recurrence after its onset may be crucial to achieve the optimal therapeutic approach, yet traditional scores like CHADS2-VASc, HATCH, and APPLE show limited predictive accuracy. Moreover, early diagnosis studies often rely on codified electronic health record (EHR) data, which may contain errors and missing information. OBJECTIVE: This study aims to predict AF recurrence between one month and two years after onset by evaluating traditional clinical scores, ML models, and our LTM approach. Moreover, another objective is to develop a methodology for integrating structured and unstructured data to enhance tabular dataset quality. METHODS: A tabular dataset was generated by combining structured clinical data with free-text discharge reports processed through natural language processing techniques, reducing errors and annotation effort. A total of 1,508 patients with documented AF onset were identified, and models were evaluated on a manually annotated test set. The proposed approach includes a LTM compared against traditional clinical scores and ML models. RESULTS: The proposed LTM approach achieved the highest predictive performance, surpassing both traditional clinical scores and ML models. Additionally, the gender and age bias analyses revealed demographic disparities. CONCLUSION: The integration of structured data and free-text sources resulted in a high-quality dataset. The findings emphasize the limitations of traditional clinical scores in predicting AF recurrence and highlight the potential of ML-based approaches, particularly our LTM model.

Figures

Figures reproduced from arXiv: 2505.14643 by the authors.

Figure 1
Figure 1. Overview of the generated dataset and the experimental setup for AF recurrence prediction. A.G. Domingo-Aldama et al.: Preprint submitted to Elsevier Page 3 of 20 [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Summary of the AF recurrence cohort generation process. First, patients with an AF onset are identified, followed by the assignment of the AF recurrence label. First, we introduce details on the detection of AF onset episodes, including the patient inclusion criteria (subsec￾tion 3.1). Second, we describe the generation of a silver dataset (subsection 3.2), starting with the feature vector generation process for eac… view at source ↗
Figure 3
Figure 3. Summary of the vector generation process. Information from both free-text discharge reports and structured codified data from OBI system are merged in a single vector per patient describing its health status around the time of AF onset. Additional details on each tool can be found in Ap￾pendix C. We must stress that the vector generation method￾ology proposed in this study can be adapted to other health￾care systems… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Age distribution comparison by gender, with females in orange and males in blue. 5.4. Results for age experiments Given the observed differences in age distribution, an additional experiment was conducted on patients under the age of 75 to assess the performance of bot…

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.