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REVIEW 5 major objections 5 minor 60 references

Towards Foundation Models for Critical Care Time Series

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper introduces the largest harmonized critical care time series dataset to date—about 600,000 stays from nine ICU and emergency department databases on three continents—and benchmarks how well machine learning models transfer…

desk verdict Solid harmonization-and-benchmark work with an overclaim and an unvalidated core assumption; worth reviewing, not worth citing yet. read the letter →

arxiv 2411.16346 v1 pith:45QHOTKF submitted 2024-11-25 cs.LG stat.ML

Manuel Burger , Fedor Sergeev , Malte Londschien , Daphné Chopard , Hugo Yèche , Eike Gerdes , Polina Leshetkina , Alexander Morgenroth
show 5 more authors
Zeynep Babür Jasmina Bogojeska Martin Faltys Rita Kuznetsova Gunnar Rätsch
This is my paper · ORCID
classification cs.LGstat.ML
keywords criticalcaretimeseriesdatasetharmonizationtreatmentvariablestransferlearningearlyeventpredictionfoundationmodelsmulti-centerICUdataelectronichealthrecords
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

The paper aims to lay the groundwork for foundation models on critical care time series by assembling a single harmonized dataset from nine publicly available ICU and emergency department databases, spanning the USA, Europe, and China and containing roughly 600,000 patient stays and close to one billion extracted data points. It claims this is the first such collection to harmonize core treatment variables rather than only vital signs and lab values, to include both ICU and ED data, and to incorporate Asian data alongside North American and European data. On top of the dataset, the authors build a transfer learning benchmark for early event prediction of circulatory, respiratory, and kidney failure and decompensation, covering in-distribution, held-out-hospital, and fine-tuning settings. A sympathetic reading is that, if the harmonization preserves clinical meaning across sites, the collection gives the field a reusable resource for studying distribution shift and for pretraining models that smaller hospitals can fine-tune.

What carries the argument

The central object is the harmonized concept layer built on the ricu package's data-source-agnostic concept abstraction. A concept is one clinical quantity—heart rate, norepinephrine rate, antibiotic indicator, and so on—that has a source-variable mapping for each underlying database. The paper's extension defines 141 concepts: 6 static demographics, 80 observations, and 55 treatment variables, grouping individual drugs into wider treatment concepts and adding both indicators and, for core medications, administration rates. This layer does the load-bearing work: it turns nine heterogeneous hospital schemas into a common grid, lets models train jointly across sites, and is what makes the transfer benchmark interpretable as transfer of clinical meaning rather than mere format.

What would settle it

Take a core treatment concept such as norepinephrine rate and compare its distribution, its relation to patient physiology, and its association with outcomes across MIMIC-IV, HiRID, SICdb, and UMCdb; if the harmonized rates show systematically different dosing conventions or the same code maps to different actual drugs at different sites, the clinical comparability behind the transfer numbers breaks down.

Watch

Extended reading notes

Core claim

The central claim, on the paper's own terms, is that a sufficiently broad and carefully harmonized multi-center dataset makes cross-hospital transfer for critical care time series viable. The authors construct that dataset by mapping local coding schemes into shared concepts using the ricu abstraction layer, extending it with new observation concepts, treatment variables, and the SICdb, PICdb, Zigong EHR, and MIMIC-IV-ED sources. Treatment harmonization is the novel piece: individual drugs are grouped into abstract concepts such as vasopressors, sedatives, antibiotics, and diuretics, with administration rates recorded for core medications like norepinephrine, epinephrine, and propofol. The benchmark then shows that gradient-boosted trees with hand-extracted features are the strongest predictors, that deep sequence models come within one or two AUROC points, and that supervised pretraining on the other hospitals improves performance on a new hospital for small and medium training-set sizes. The authors do not claim a finished foundation model; they claim the foundation: the dataset, task annotations, and transfer results needed to build one.

Load-bearing premise

The load-bearing premise is that mapping each hospital's local drug and measurement codes into shared clinical concepts preserves what the treatment actually means across hospitals; if a norepinephrine rate in one database is not the same clinical intervention as in another, the transfer benchmark and the harmonization claim are both confounded.

Editorial extensions

If this is right

  • Training on the harmonized collection should become the reference starting point for small- and medium-scale critical care time series studies, since supervised pretraining on the other hospitals outperformed training from scratch on HiRID up to tens of thousands of admissions.
  • The transfer heatmaps supply a quantitative map of which hospital pairs are clinically similar: models transfer well within US/European groups and poorly to Chinese and pediatric datasets, so dataset selection for a new site can be guided by recorded resolution and geography.
  • The treatment concepts, with indicators plus rates for core vasopressors, sedatives, antibiotics, and diuretics, make it possible to study whether and how medication variables affect predictive accuracy, addressing the earlier finding that including them can hurt performance.
  • Because task labels use clinically defined early-event horizons of 8 hours for circulatory failure, 24 hours for decompensation and respiratory failure, and 48 hours for kidney failure, the benchmark gives future foundation-model work standardized endpoints comparable to prior single-center benchmarks.
  • Joint ED-ICU modeling becomes feasible for the first time on harmonized data, potentially leading to a unified prediction model that works regardless of hospital unit.

Reading between the lines

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

  • Editorial extension: the treatment harmonization could be externally validated by comparing norepinephrine rate distributions and their association with blood-pressure changes across hospitals; that would test whether 'norepinephrine' means the same intervention everywhere.
  • Editorial extension: a controlled ablation on the harmonized dataset, training with and without the 55 treatment concepts, would isolate whether treatments help transfer or simply add site-specific confounds, something the current benchmark does not separate.
  • Editorial extension: the tokenized data format opens a natural next experiment in self-supervised pretraining, such as masked event prediction, before fine-tuning on the early-event tasks; the paper provides the data and benchmark but does not run that pretraining.
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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

5 major / 5 minor

Summary. This manuscript introduces a harmonized multi-center critical care time-series dataset assembled from nine publicly available ICU and ED sources (MIMIC-III/IV, eICU, UMCdb, HiRID, SICdb, PICdb, Zigong EHR, and MIMIC-IV-ED), totaling roughly 600,000 stays, and reports an extensive benchmark of seven machine learning models on four early-event-prediction tasks. The authors claim the dataset is the largest harmonized critical care time-series collection, the first to harmonize core treatment variables as both indicators and administration rates, the first to combine ICU and ED data, and the first to include data from Asia. They evaluate in-distribution performance, leave-one-dataset-out transfer, and a supervised pretraining/fine-tuning study on HiRID.

Significance. If the claims hold, this would be a valuable community resource: a multi-continent, multi-unit harmonized dataset with treatment variables, plus a transfer benchmark far broader than prior single-center or smaller multi-center efforts. The experimental design is genuinely extensive, with seven models, four tasks, nine datasets, both single- and multi-center training, and a fine-tuning study using three seeds. The observed result that gradient-boosted trees with feature extraction remain competitive with modern deep sequence models, and that supervised pretraining helps for small target cohorts, is useful and credible. However, the central dataset claim is currently unverifiable because no dataset or code is released, and the treatment harmonization that underpins the headline contribution is not audited. The paper is a promising benchmark report, but it is not yet a verifiable dataset contribution.

major comments (5)
  1. [Abstract and Section 3.3] The paper is framed as a dataset contribution, yet it provides no dataset access mechanism, URL, repository, or code release anywhere in the manuscript. Without the harmonized artifacts or a clear procedure for obtaining them, the central claims (largest harmonized dataset, first to harmonize treatment variables, first to include Asia) cannot be checked and the benchmark cannot be reproduced. This is load-bearing for the paper's main contribution and must be addressed, either by releasing the processed data and code or by explicitly stating access conditions and providing a public placeholder/release plan.
  2. [Section 3.2, Tables 6-8] The load-bearing premise of the treatment harmonization is that ricu-based concepts such as norepinephrine rate, propofol rate, and heparin rate are clinically comparable across MIMIC-IV, eICU, HiRID, UMCdb, SICdb, PICdb, and Zigong. The paper gives no audit of drug identification, unit conversion (including weight-based mcg/kg/min to mcg/min conversion in pediatric PICdb), bolus-versus-infusion disambiguation, or time alignment, and no external validation such as dual expert coding, reference-standard comparison, or dose-response sanity checks. If rates are mis-scaled or mis-mapped, the out-of-distribution transfer results in Section 4.3 no longer measure clinical generalization but instead reflect site-specific charting artifacts. The paper needs either a detailed validation appendix for the treatment concepts or a clear statement of which treatment concepts are validated and how.
  3. [Table 7] The Used/Not used/Total columns do not reconcile for any dataset. For example, MIMIC-IV reports 442 used, 292 not used, and 453 total, but 442 + 292 = 734, not 453; the same inconsistency appears in every row, including the Total row (26,143 + 20,943 = 47,086, not 26,264). This table is central evidence for the treatment harmonization claim, and as presented it is internally inconsistent. The authors must clarify what the counts represent and correct the arithmetic.
  4. [Table 2] Several in-distribution multi-center values for SICdb are implausible. For Circulatory 8h, multi-center logistic regression reports 95.7 AUROC while multi-center LightGBM with features reports 91.7 and the best single-center model reports 91.6; for Kidney 48h, multi-center logistic regression reports 93.4 while all other multi-center models are around 89-90. These look like transposed or copied values. Because Tables 2 and 4 are the main benchmark evidence, the authors need to audit all numbers and provide a corrected table, including standard deviations where the text promises them.
  5. [Table 5 and Section 4] Table 5 lists a 0% label prevalence for Zigong on Respiratory 24h and Kidney 48h, yet the paper states in Section 1 that it provides annotations and results on multiple organ failure tasks on the same data. If no positive labels exist for these tasks on Zigong, the absence of Zigong respiratory and kidney rows in Tables 2, 4, 9, and 10 should be explicitly explained; if the 0% entries are errors, they must be corrected. As written, the task coverage claim and the label statistics are in tension.
minor comments (5)
  1. [Table 6] There are several typographical errors in the treatment concept tables (e.g., 'Antibotics' instead of 'Antibiotics', 'Elektrolytes-Mg' instead of 'Electrolytes-Mg', 'teophyllin' instead of 'theophylline') that should be corrected for a dataset reference document.
  2. [Table 8] The concept reference table lists the indicator concepts dobu_ind, levo_ind, norepi_ind, epi_ind, milrin_ind, teophyllin_ind, dopa_ind, adh_ind, hep_ind, prop_ind, benzdia_ind, and loop_diur_ind twice, which makes the table hard to use as a reference.
  3. [Figure 3] Each panel of Figure 3 appears to contain two y-axes with different scales (one around 0.89-0.93 and another around 0.5-0.8), but the axes are not labeled separately in the caption; please clarify which curve uses which scale.
  4. [Section 3.2] The sentence claiming to include 'all ICU datasets that are freely available to the academic community' is immediately qualified by the exclusion of RICD because it is not free access; please rephrase to 'all datasets that are freely available without additional contracts' to avoid the apparent contradiction.
  5. [Table 5] The mean length of stay for MIMIC-IV is reported as 11 days, which is much higher than typical reported values for MIMIC-IV ICU stays (usually several days); please verify this number and the corresponding mean LoS values for other datasets.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the dataset and benchmark claims rest on held-out evaluations and leave-one-dataset-out transfer, not on recycled fitted quantities.

full rationale

The paper's contributions are a harmonized multi-center dataset and an empirical benchmark. There is no derivation chain in which a predicted quantity is definitionally or statistically identical to an input. The treatment concepts in Section 3.2 and Table 6 were selected by expert opinion and literature importance; that is feature selection, not parameter fitting, and the benchmark labels (Section A.5.1) come from clinical definitions in prior work rather than from model outputs. In-distribution and out-of-distribution results use held-out test sets, and multi-center hold-out models are trained on all datasets except the target, with hyperparameters tuned only on training validation sets. No fitted value is recycled as an evaluation target. Citations to earlier related work by overlapping groups (e.g., HiRID, Hyland et al., Hüser et al.) supply task definitions and baselines but are not used as unverified justifications for the benchmark conclusions, which are computed directly in this paper. The internal inconsistencies in Table 7 and the absence of external validation of drug-rate mappings are data-quality and validation concerns, not circularity. The mild provenance concern that treatment concepts were chosen with knowledge of which variables prior work found important does not force any reported AUROC or transfer result, so no circular step is exhibited.

Assumptions & free parameters 3 free parameters · 3 assumptions · 1 invented entities

The main dataset-level claims rest on choices made by hand: inclusion thresholds, grid resolutions, and the expert-defined concept mappings. The benchmark claims add domain assumptions about label comparability and sufficiency of the hyperparameter budget. No numeric parameter is fitted to data to produce the central claim; model hyperparameters are tuned but not part of the dataset claim.

free parameters (3)
  • Minimum length of stay threshold = 4 hours
    Appendix A.2 sets LoS > 4 hours, broadened from 6 hours in Van De Water et al.; this choice changes the cohort composition and all downstream statistics.
  • Maximum measurement gap = 48 hours
    Appendix A.2 raises the allowed gap from 12 to 48 hours versus prior work, expanding inclusion but mixing very differently sampled stays.
  • Grid resolution per source dataset = 5 or 60 minutes
    Section 3.3 and Table 5 assign 5 minute grids to HiRID, UMCdb, and SICdb and 60 minute grids to others; this hand-chosen design parameter affects multi-resolution training and transfer conclusions.
assumptions (3)
  • domain assumption ricu concept mappings are clinically correct for every source dataset
    Section 3.2 builds harmonization on ricu and expert-defined concepts without external validation against a reference standard.
  • domain assumption Task annotations are comparable across hospitals
    Appendix A.5.1 labels a time step only if the source data provides enough evidence for a failure or stable state; varying documentation practices across hospitals change label prevalence and may bias benchmarks.
  • domain assumption The hyperparameter budget is sufficient for fair model comparison
    Appendix B uses about 12 grid points per model and three seeds; conclusions about small AUROC differences may change with more tuning.
invented entities (1)
  • Abstract treatment concepts (vasopressor and inotrope rates, drug class groups)
    purpose: Unify medication records from different hospitals into comparable variables
    Table 6: These groupings come from expert opinion and literature importance, not from an external ontology or outcome validation; they are not falsifiable outside this paper.

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Cite this review

Pith. "Pith review of Towards Foundation Models for Critical Care Time Series." pith.science (2026). https://pith.science/paper/45QHOTKF

@misc{pith2026241116346,
  author       = {Pith},
  title        = {Pith review of: Towards Foundation Models for Critical Care Time Series},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/45QHOTKF}},
  note         = {Machine review of arXiv:2411.16346}
}
read the original abstract

Notable progress has been made in generalist medical large language models across various healthcare areas. However, large-scale modeling of in-hospital time series data - such as vital signs, lab results, and treatments in critical care - remains underexplored. Existing datasets are relatively small, but combining them can enhance patient diversity and improve model robustness. To effectively utilize these combined datasets for large-scale modeling, it is essential to address the distribution shifts caused by varying treatment policies, necessitating the harmonization of treatment variables across the different datasets. This work aims to establish a foundation for training large-scale multi-variate time series models on critical care data and to provide a benchmark for machine learning models in transfer learning across hospitals to study and address distribution shift challenges. We introduce a harmonized dataset for sequence modeling and transfer learning research, representing the first large-scale collection to include core treatment variables. Future plans involve expanding this dataset to support further advancements in transfer learning and the development of scalable, generalizable models for critical healthcare applications.

Figures

Figures reproduced from arXiv: 2411.16346 by the authors.

Figure 1
Figure 1. Visualization of harmonized and pro￾cessed data by t-SNE [van der Maaten and Hinton, 2008]. Each point represents a time step [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Single-center transfer performance heatmaps (AUROC). Figure 2a shows separate heatmaps [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Supervised fine-tuning study performed on HiRID for circulatory failure prediction (Fig [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Single-center transfer performance heatmaps (AUROC). [PITH_FULL_IMAGE:figures/full_fig_p021_4.png]
Figure 5
Figure 5. Figure 5: Supervised fine-tuning study performed on HiRID for respiratory failure (Figures 5b and 5c) [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.