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REVIEW 3 major objections 5 minor 58 references

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Bi-Axial Transformer reaches state-of-the-art sepsis prediction

desk verdict A clear architecture paper for EHR classification whose headline sepsis result depends on an ambiguous hyperparameter-selection protocol that must be resolved before the SOTA claim can be trusted. read the letter →

arxiv 2508.12418 v1 pith:67PKAH3A submitted 2025-08-17 cs.LG

Rachael DeVries , Casper Christensen , Marie Lisandra Zepeda Mendoza , Ole Winther This is my paper · ORCID
classification cs.LG
keywords ElectronicHealthRecordsPredictiveModelsTimeSeriesAnalysisAxialAttentionTransformerMissingDataSepsisPredictionTransferLearning
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 introduces the Bi-Axial Transformer (BAT), a classifier that applies attention along both the time axis and the sensor axis of electronic health record data instead of attending along a single flattened axis. The paper claims BAT reaches state-of-the-art sepsis prediction on the P19 benchmark and is competitive with the best RNN methods for mortality prediction on two ICU datasets, while outperforming other transformers under increasing data sparsity. It argues that retaining informative missingness (the pattern of which readings are absent) and learning explicit per-sensor identity embeddings are what give BAT its edge. If true, the model is a practical route to combining multiple EHR datasets through shared sensor embeddings for large-scale pretraining, and a reusable benchmark suite for EHR classification.

What carries the argument

The central object is bi-axial attention: instead of flattening every (sensor, time) observation into one long sequence or attending along only one dimension, BAT clones the embedding matrix and runs self-attention along the time axis in one track and along the sensor axis in the other, switching the axis order between tracks. This keeps the sequence length at $T + D$ per track rather than $T \times D$, avoiding the quadratic blow-up of a dense tuple representation while still letting information flow across both axes. The design also preserves the binary observation mask inside every embedding and learns a separate embedding per sensor identity, which is what later enables sharing sensors across datasets.

What would settle it

Re-run the P19 comparison with a nested procedure: hold out one test split untouched until after hyperparameter selection, or select hyperparameters using only training and validation data; if BAT's reported 2.32-point AUPRC margin over the second-best model disappears or reverses, the state-of-the-art claim is not established.

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Extended reading notes

Core claim

BAT's central claim is that EHR classification improves when a transformer is allowed to attend along both axes of the observation matrix at once. Each observation embedding concatenates the sensor reading with a binary observed/missing indicator, a learned sensor-identity embedding, and a sinusoidal time encoding; the data are then duplicated into two attention tracks that perform self-attention over time points and over sensors in opposite orders, with weights shared inside each track. On sepsis prediction for P19, BAT reports AUPRC 77.05 and AUROC 95.41, ahead of every baseline; on P12 and MIMIC-III mortality it is competitive with GRU-D, the best overall method. The paper also reports that BAT maintains higher AUPRC than two transformer baselines at every induced sparsity level on a human-activity dataset, and that sharing sensor embeddings between P12 and MIMIC-III reduces variance and mostly improves performance. The authors read this as evidence that cross-axis attention and explicit sensor identities address sparsity, informative missingness, and dataset integration.

Load-bearing premise

The reported state-of-the-art sepsis result rests on the assumption that the benchmark comparison is unbiased—in particular, that selecting hyperparameters by best AUROC on the same five splits used to report test performance, and the modified GRU-D and IP-Nets baselines, did not favor BAT.

Editorial extensions

If this is right

  • On the P19 sepsis benchmark, BAT reports the best AUPRC and AUROC among all compared models; if the comparison is sound, bi-axial attention is a stronger transformer design for sparse ICU time series than one-axis attention.
  • Attention visualizations show observed measurements attending strongly to absent time points, supporting the claim that BAT uses informative missingness rather than treating missingness as noise.
  • In HAR experiments with induced missingness, BAT outperforms both Transformer and iTransformer at every sparsity level up to 99%, where all models reach chance; this points to cross-axis attention as the source of robustness.
  • Shared sensor embeddings across P12 and MIMIC-III reduce variance and improve most metrics, suggesting that datasets with overlapping sensors can be trained jointly or used for transfer.
  • The re-implemented baselines and BAT are released in one open-source repository, making the benchmark reproducible.

Reading between the lines

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

  • A direct extension the authors leave implicit: because sensor identities are stored as separate embeddings, a model pretrained on one EHR system could be adapted to a new hospital by only learning new sensor embeddings and freezing the rest—this is testable on any pair of datasets with overlapping measurements.
  • The sparsity result on HAR suggests the advantage may extend beyond EHR to other irregularly sampled multivariate signals such as wearables; the same induced-missingness protocol would settle that.
  • If the supplementary selection procedure is taken literally—choosing the candidate with highest AUROC on the same five test splits—reported margins over baselines could shrink under a properly nested validation scheme; the authors do not discuss this, so the robustness of the state-of-the-art claim remains an open question.
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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

3 major / 5 minor

Summary. The paper proposes the Bi-Axial Transformer (BAT), an architecture for EHR classification that applies axial attention over both the sensor and time axes of multivariate irregularly sampled time series, while retaining explicit missingness indicators and learning per-sensor embeddings. The authors evaluate BAT on mortality prediction (P12, MIMIC-III), sepsis prediction (P19), and a controlled sparsity experiment on HAR, comparing against SeFT, GRU-D, IP-Nets, a standard Transformer, and iTransformer. They report that BAT achieves state-of-the-art sepsis prediction on P19, is competitive on mortality tasks, is more robust to missing data than other transformers, and that shared sensor embeddings enable joint training across datasets. All baselines were re-implemented in PyTorch and released in a public repository. The core empirical claim is the P19 SOTA result and the associated architectural advantages.

Significance. If the empirical results are unbiased, BAT is a sensible and modular extension of axial attention to sparse, irregular EHR time series. The sensor-embedding transfer experiment is a useful idea for multi-dataset pretraining, and the reimplementation of several baselines in a modern framework is a practical community contribution. The ablations and sparsity experiments are thoughtful and give insight into where the architecture helps. However, the central claims rest entirely on the benchmark numbers. The hyperparameter selection protocol described in the supplement appears to use test-set labels for model selection, which would invalidate the reported metrics and the SOTA statement. Until that issue is resolved, the significance of the contribution cannot be assessed.

major comments (3)
  1. [Supplementary §7.1] The hyperparameter sweep protocol states that 'each candidate was trained and tested on 5 splits of train, validation, and test data' and that 'the model with the highest resulting AUROC was selected.' Taken literally, this selects hyperparameters based on test-set AUROC, which makes the reported AUROC and AUPRC values in Table 2 in-sample selection results rather than unbiased estimates of generalization. This directly affects the central claim in the Abstract and Section 5 that BAT achieves state-of-the-art performance on P19 sepsis prediction. Please clarify whether the selection AUROC was computed on the validation splits or the test splits. If test splits were used, the experiments must be re-run with hyperparameters selected on validation data (or via nested cross-validation) and all tables regenerated; the repository contains the sweep code, so this is feasible.
  2. [§4.3.1] The modification of GRU-D and IP-Nets for P19 — pooling hidden states across all time points instead of using the final hidden state — is a substantial deviation from the original model definitions. The authors state this was done to avoid missed diagnoses when sepsis resolves before the end of the sequence, but this changes the inductive bias of the baselines. To establish a fair comparison, please report results for the original final-state versions as well, or justify with a citation that this pooling modification is standard practice for P19. Without this, it is unclear whether the reported baseline numbers are the strongest reasonable comparators. This issue is compounded by the hyperparameter-selection problem in §7.1, since the modified baselines' hyperparameters were selected under the same protocol.
  3. [Abstract; §5] The claim of 'state-of-the-art performance on sepsis prediction' is supported only by comparisons against the authors' reimplemented baselines. No published state-of-the-art numbers for the PhysioNet 2019 sepsis challenge or other recent P19 sepsis-prediction results are cited or compared. If the term 'state-of-the-art' is meant globally, the paper needs to benchmark against published results; if it is meant only relative to the included baselines, the wording should be changed accordingly. This is a substantive point because the P19 result is the paper's main empirical contribution.
minor comments (5)
  1. [§3.2.1] In the paragraph after Eq. (3), 'concatentated' should be 'concatenated.' Also, the description of tau as 'the maximum possible time point' is ambiguous; since the encoding uses tau in the denominator as a scaling factor, please clarify whether tau is the largest timestamp in the dataset or a fixed hyperparameter analogous to max_timescale in SeFT.
  2. [Table 2] The entry for Transformer on P19 AUPRC appears to be missing from the table (only the AUROC is visible in the text). Please ensure all cells are reported consistently, as the claim of a 2.32-point AUPRC improvement over the second-best model depends on that value.
  3. [§4.4.2] In the HAR sparsity experiment, the statement that all models perform 'equivalently to random guessing' at 99% sparsity would be more informative if the random-guess performance level were stated explicitly and shown as a reference line in Fig. 6.
  4. [§4.3.1] The sentences describing the average improvements over iTransformer and Transformer would benefit from a small table or explicit arithmetic, since the numbers are not directly readable from Table 2 due to the formatting of the bold/italic markers.
  5. [Fig. 4] The attention visualization is described in the text, but the figure itself is hard to interpret without a more detailed legend explaining the circles, arrows, and density plot. Consider moving some of the supplementary description into the main caption.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: BAT's claims are empirical and evaluated against external baselines; no equation reduces to a fitted prediction.

full rationale

The paper's contribution is an empirical architecture, the Bi-Axial Transformer, whose performance is measured against re-implemented baselines on P12, MIMIC-III, P19, and HAR. Walking the derivation chain, I found no step where a predicted quantity is defined in terms of the model's own inputs or fitted parameters. The loss in Eq. (10) is standard cross-entropy; the architecture in Section 3.2 is a composition of linear embeddings, sinusoidal time encodings taken from Horn et al. [43], two axial attention tracks, pooling, demographic fusion, and a classification head. The learned sensor embeddings are an architectural component, not a renamed benchmark result, and the transfer-learning experiment in Section 4.5 compares shared versus separate embeddings rather than deriving a prediction from a fit. The paper's only self-citation, reference [20], is a preliminary report of the same model and is used merely as a pointer ('A preliminary version of this work has been reported [20]'); it is not invoked to justify the architecture, to rule out alternatives, or to supply a uniqueness theorem. The concern raised in Supplementary Section 7.1, that each hyperparameter candidate was 'trained and tested on 5 splits of train, validation, and test data' with the highest-AUROC candidate selected, is a real evaluation-validity problem because test labels appear to be used for model selection, but it is not circular reasoning as defined here: it does not make the reported AUROC equal by construction to a fitted parameter, nor does it rename an input as a prediction. That issue belongs to correctness/benchmark-fairness, not to circular derivation. Accordingly, there are no circular steps and the circularity score is 0.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

No invented entities. The central empirical claims rest on hyperparameters chosen per dataset and a set of domain assumptions about missingness, axial attention, and sensor-transferability. The most fragile assumption is the fair-comparison premise, because the reported selection protocol uses test AUROC.

free parameters (3)
  • BAT hyperparameters = P12 embed 128, heads 2; MIMIC embed 16, heads 1; P19 embed 32, heads 1; full list in Table 4
    Selected by random sweep of 20 candidates per dataset to maximize AUROC; Supp. §7.1 indicates test AUROC was used for selection.
  • Baseline hyperparameters = Table 4 (SeFT widths, GRU-D units, IP-Nets impute steps, Transformer and iTransformer settings)
    Also selected via random sweep on AUROC; the paper does not report a shared validation-only selection protocol, so a fair comparison depends on unstated details.
  • Time encoding scale tau (Eq. 3) = not reported for BAT
    Scaling factor for the sinusoidal temporal encoding; a design choice that changes how time differences are represented.
assumptions (5)
  • standard math Scaled dot-product attention and sinusoidal positional encodings are taken as given from Vaswani et al. (2017).
    Equations (2)-(4) in §3.2.1-3.2.2 rely on these as the attention core.
  • domain assumption Axial attention from Ho et al. (2019) can be adapted by transposing time and sensor dimensions onto the batch axis, with shared encoder parameters across axes.
    This is assumption #2 in §3.2.2, the design premise that cross-axis parameter sharing enables learning time-sensor relationships.
  • domain assumption Informative missingness is present in EHRs and the binary indicator mask conveys clinically meaningful signal.
    Motivates including the mask in the embedding (§3.2.1) and is supported by cited literature and Fig. 5, but remains a modeling assumption.
  • domain assumption Sensor names are comparable across datasets when they overlap, so shared sensor embeddings transfer.
    Required for the Table 3 transfer experiment; results are mixed (MIMIC-III AUPRC drops with shared embeddings), so the premise is only weakly supported.
  • domain assumption The reimplemented baselines and their modifications are faithful enough for a fair comparison.
    §4.2-4.3.1; GRU-D and IP-Nets were modified for P19 (pooling all hidden states), which may change their behavior relative to original implementations.

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Cite this review

Pith. "Pith review of Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification." pith.science (2026). https://pith.science/paper/67PKAH3A

@misc{pith2026250812418,
  author       = {Pith},
  title        = {Pith review of: Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/67PKAH3A}},
  note         = {Machine review of arXiv:2508.12418}
}
read the original abstract

Electronic Health Records (EHRs), the digital representation of a patient's medical history, are a valuable resource for epidemiological and clinical research. They are also becoming increasingly complex, with recent trends indicating larger datasets, longer time series, and multi-modal integrations. Transformers, which have rapidly gained popularity due to their success in natural language processing and other domains, are well-suited to address these challenges due to their ability to model long-range dependencies and process data in parallel. But their application to EHR classification remains limited by data representations, which can reduce performance or fail to capture informative missingness. In this paper, we present the Bi-Axial Transformer (BAT), which attends to both the clinical variable and time point axes of EHR data to learn richer data relationships and address the difficulties of data sparsity. BAT achieves state-of-the-art performance on sepsis prediction and is competitive to top methods for mortality classification. In comparison to other transformers, BAT demonstrates increased robustness to data missingness, and learns unique sensor embeddings which can be used in transfer learning. Baseline models, which were previously located across multiple repositories or utilized deprecated libraries, were re-implemented with PyTorch and made available for reproduction and future benchmarking.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.