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REVIEW 4 major objections 6 minor 95 references

A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A shared pretrained medical encoder plus a small learnable prompt per hospital can serve many hospitals at once, with the largest accuracy gains where data is scarce.

desk verdict Solid empirical paper on a new multi-center medication recommendation setting, but the record-level split risks patient leakage and needs to be fixed before the SOTA claim is credible. read the letter →

arxiv 2412.20040 v1 pith:HRCWXEFZ submitted 2024-12-28 cs.IR

Qidong Liu , Zhaopeng Qiu , Xiangyu Zhao , Xian Wu , Zijian Zhang , Tong Xu , Feng Tian This is my paper · ORCID
classification cs.IR
keywords medicationrecommendationmulti-centerlearningelectronichealthrecordscontrastivepretrainingprompttuningtransformerencodereICUdatasetcatastrophicforgetting
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 aims to show that medication recommendation can work across many hospitals at once, even when most hospitals have few records. It proposes TEMPT, a two-stage model: first pretrain a transformer encoder on diagnosis and procedure codes from all hospitals using masked-code prediction and a contrastive task that aligns diagnosis and procedure representations; then, for each hospital, freeze the pretrained weights and learn only a small hospital-specific prompt vector plus a prediction head. The authors claim this beats standard medication-recommendation models, multi-domain recommendation models, LLM baselines, and full finetuning on the eICU dataset, and that the gain is largest for small hospitals. A sympathetic reader should care because most hospitals lack enough data to train their own recommender, and the paper offers a way to share medical knowledge across hospitals without overwriting it.

What carries the argument

The load-bearing mechanism is a two-stage training scheme built on a shared transformer medical encoder. In pretraining, a mask prediction task randomly masks diagnosis and procedure codes and reconstructs them, capturing intra-set co-occurrence, while a contrastive task aligns the diagnosis representation and procedure representation of the same record against negatives from other records, capturing inter-set relationships. In the second stage, each hospital gets a small set of learnable prompt embeddings inserted at the start of the diagnosis and procedure sequences; the pretrained embedding matrices and encoder are frozen, and only the prompts and a medication-prediction MLP are updated. The prompt vector is what carries the hospital-specific adaptation without modifying the shared medical knowledge.

What would settle it

Re-run the comparison with the original baseline components intact, giving GAMENet its DDI graph and dynamic memory, COGNet its copy module, and G-Bert its historical medications, and check whether each still trails TEMPT on eICU. If any of these full baselines matches or exceeds TEMPT's PRAUC of 0.5468, the reported superiority would be an artifact of weakened competitors rather than a property of TEMPT's design.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that hospital heterogeneity in prescribing can be handled by separating general medical knowledge from hospital-specific adaptation: pretrain on all hospitals with two self-supervised tasks, then adapt per hospital with continuous prompts rather than finetuning all parameters. The empirical claim is that TEMPT reaches PRAUC 0.5468, Jaccard 0.3318, and F1-score 0.4790 on eICU, with statistically significant improvements over the best baseline, and improves small-hospital PRAUC by 9.20% over Single-Train. The authors also claim the prompt-tuning stage is cheaper in both training time and storage than full finetuning and that it relieves catastrophic forgetting.

Load-bearing premise

The comparison against prior methods assumes the re-implemented baselines are faithful to their original designs, but several were run in stripped-down form: GAMENet without its drug-drug interaction graph and memory module, COGNet without its copy module, and G-Bert with procedures substituted for medications.

Editorial extensions

If this is right

  • If TEMPT's result holds, hospitals with fewer than 1,000 records gain the most from multi-center pretraining, with a 9.20% PRAUC improvement over training on their own data.
  • Freezing the shared medical encoder and learning only small prompts means the per-hospital adaptation cost is a small fraction of full finetuning, making deployment across dozens of hospitals feasible.
  • The two self-supervised pretraining tasks each contribute to the result; removing mask prediction or the contrastive task lowers performance, so learning relationships among diagnosis and procedure codes is part of the recipe.
  • The result suggests that a unified pretrained medication model can serve many hospitals, challenging the assumption that each hospital needs its own recommender trained from scratch.

Reading between the lines

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

  • The same pretrain-and-prompt recipe could carry over to other multi-site clinical prediction tasks, such as predicting in-hospital mortality or length of stay, where site differences are known to hurt a single global model.
  • Because the prompt embeddings are indexed by hospital, TEMPT opens a natural path to federated learning: hospitals could update only their own prompts locally and share the pretrained encoder without transferring raw patient data.
  • The paper's own stated limitation, ignoring drug-drug interactions, suggests a testable extension: add a DDI-constrained loss at the recommendation head and measure whether the multi-center gains survive when safety is enforced.
  • A direct testable implication is that the contrastive task's benefit should grow with distribution shift; comparing TEMPT's margin over pretraining without the contrastive task on hospital pairs with high versus low Jensen-Shannon divergence would isolate that effect.
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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 / 6 minor

Summary. TEMPT is a two-stage model for multi-center (multi-hospital) medication recommendation. In stage 1, a shared transformer encoder is pretrained on diagnosis and procedure sets from all hospitals with a masked-token prediction loss and a contrastive alignment loss between diagnosis and procedure representations; the pretraining does not use medication labels. In stage 2, the encoder and code embeddings are frozen and, for each hospital, only a small set of prompt vectors and a medication-prediction MLP are trained. Experiments on eICU (102,363 records from 80 hospitals) compare TEMPT against medication recommendation baselines (Leap, GAMENet, G-Bert, COGNet), LLM baselines (GPT-4, TALLRec), multi-domain recommenders (MMOE, PLE, STAR), and TEMPT variants, using PRAUC, Jaccard, and F1. The paper reports statistically significant improvements over all baselines, with particularly large gains for small hospitals, plus ablations, PEFT comparisons, efficiency analysis, and hyperparameter sensitivity. Code is released.

Significance. If the reported results are valid, TEMPT makes a useful contribution: it identifies a realistic low-resource multi-center medication recommendation setting, shows that self-supervised pretraining on diagnosis and procedure co-occurrence transfers across hospitals, and demonstrates that per-hospital prompt tuning is more parameter-efficient than full finetuning. The paper is strong on reproducibility: code is public, five random splits and seeds are used, standard deviations and t-tests are reported, and the ablation and hyperparameter studies are reasonably comprehensive. The main caveat is that the headline SOTA claim rests on an evaluation protocol whose leakage risk is not addressed, a nonstandard PRAUC definition, and several heavily modified baselines; these issues affect the interpretation of nearly every table.

major comments (4)
  1. [Section 4.1.1, Table 2] The train/validation/test split is described only at the level of records (we divide the data into train/validation/test by the ratio of 8:1:1 for each hospital), and Table 2 reports 102,363 records but no patient count. eICU contains multiple ICU stays per patient, so a record-level split can place the same patient's records in both training and test sets. Because medication regimens are strongly patient-specific and repeat across stays, this can inflate all metrics in Tables 3-7 and may exaggerate TEMPT's advantage, especially given its pretraining and hospital-specific prompts. Please re-run the experiments with a patient-level split (or otherwise demonstrate that patients do not cross splits) and report the patient counts per split.
  2. [Section 4.1.4, Eq. (17)] The PRAUC definition is not the standard area under the precision-recall curve. Eq. (17) sums Precision_i times Recall_i over k=1 to |M| without defining a threshold index or a change in recall, whereas PR-AUC normally integrates Precision as a function of Recall (for example, average precision with (Recall_k - Recall_{k-1}) times Precision_k). Since PRAUC is the headline metric in Tables 3, 4, 5, and 7, please clarify the exact computation, including how thresholds are generated, and if the intention is a different metric, rename it and justify the choice.
  3. [Section 4.1.2, Table 3] The claim that TEMPT outperforms state-of-the-art medication recommendation models is weakened by the baseline adaptations: GAMENet is run without its DDI graph and dynamic memory module, COGNet without its copy module, and G-Bert with procedures substituted for medications. These are central components of the original methods, so the comparisons are against substantially altered versions. TEMPT's advantage over MMOE, PLE, and STAR is less affected, but the state-of-the-art medication recommendation wording should be revised, or faithful implementations should be provided.
  4. [Section 3.3.2, Eq. (9)] In the contrastive loss, the denominator sums over j not equal to i only and omits the positive pair exp(sim(u_i^d, u_i^p)/tau). Standard InfoNCE-style losses include the positive in the denominator. Please clarify whether this omission is intentional and how it affects the optimization; if it is a typo, correct the equation and re-run the pretraining experiments.
minor comments (6)
  1. [Algorithm 1, line 6] Algorithm 1 updates only E_d, E_p, and Theta_encoder, but the pretraining objective in Eq. (11) also contains the mask-prediction MLPs and the contrastive projectors; please list all updated parameters.
  2. [Section 3.5] The inference paragraph says the recommending probability is given as Equation (12) shows, but Eq. (12) is the non-prompted finetuning formulation; inference should use the prompted representation in Eq. (14).
  3. [Table 1] Table 1 lists E_p^h as prompt embedding matrices of diagnosis and medication; medication should be procedure.
  4. [Table 7, Section 4.7] Table 7 introduces TEMPT (finetune-freeze) without defining it in Section 4.7 or in the implementation details; please clarify what this variant is.
  5. [Figure 7] The Figure 7 caption says randomly sampled 8 hospitals, but the figure contains nine panels labeled (a) through (i).
  6. [Section 4.1.4] The text says the reported metrics are averaged over all of the records and hospitals; please specify whether the averaging is per record then per hospital or per record pooled across hospitals, since the two procedures weight hospitals differently.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning found: TEMPT's pretraining tasks are label-free, prompt tuning is evaluated on held-out hospital records, and no claimed prediction reduces to a fitted input or self-citation chain.

full rationale

The paper's derivation chain is empirical rather than analytic, and none of its load-bearing steps reduces to its own inputs. In the pretraining stage, the mask prediction task reconstructs masked diagnosis and procedure codes (Eqs. 5-7) and the contrastive task aligns diagnosis/procedure representations within the same record (Eqs. 8-10); neither objective uses the medication labels y that are the target of the downstream recommendation task. In the prompt tuning stage, hospital-specific prompt embeddings O_h and MLP parameters Theta_MLP are trained on each hospital's training split and evaluated on that hospital's held-out test split, so the reported PRAUC, Jaccard, and F1 numbers in Tables 3-7 are not fit values renamed as predictions. Hyper-parameters (gamma, tau, b) are selected on validation performance, and results are averaged over five random split/seed runs. The self-citations in the related-work section (e.g., the authors' own LLM-distillation and MoE papers) are contextual and do not carry any uniqueness claim or theorem that forces the proposed design. The reviewers' concerns about a record-level rather than patient-level 8:1:1 split, and about modified baselines such as GAMENet without its DDI graph, are validity or fairness concerns, not circularity: they question whether the comparison is favorable, not whether the model's output is definitionally identical to a fitted input. No equation in the paper defines the prediction in terms of the target, and no fitted parameter is relabeled as a prediction. Therefore the appropriate finding is no significant circularity.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The model introduces no new physical entities; prompt embeddings are learned parameter matrices. The central claim depends on validation-selected hyper-parameters and on the eICU dataset as a proxy for multi-center practice, plus the fairness of the modified baselines.

free parameters (5)
  • Temperature of contrastive loss (tau) = 0.8
    Selected by validation performance; Figure 6b shows a peak at tau=0.8.
  • Weight of contrastive loss (gamma) = 1e-2
    Selected by validation performance; Figure 6a shows best PRAUC at 1e-2.
  • Number of prompt vectors (b) = 2
    Selected by validation performance; Figure 6c shows best PRAUC with b=2.
  • Recommendation threshold (t) = 0.3
    Chosen because the authors state 'this value performs better in total'; it is not tuned per model or per hospital.
  • Mask proportion in mask prediction task = not reported
    The paper randomly masks a proportion of diagnosis and procedure tokens but does not state the proportion, which is a free choice that can affect pretraining.
assumptions (4)
  • domain assumption eICU is representative of real-world multi-center hospital medication practices
    All conclusions rest on this single ICU database from 2014-2015; results may not generalize to other coding systems or outpatient settings.
  • domain assumption Diagnosis and procedure codes are sufficient inputs for medication recommendation
    The model ignores demographics, allergies, labs, and drug-drug interactions; the authors acknowledge this limitation in Section 6.
  • domain assumption Modified baselines retain the core capabilities of the original models
    GAMENet, COGNet, and G-Bert are altered to fit the instance-based setting; if these modifications weaken them, the SOTA comparison is invalid. Section 4.1.2.
  • domain assumption Random per-hospital 8:1:1 split produces independent training and test distributions
    No temporal ordering is applied, so patients with multiple visits could straddle the split and leak information. Section 4.1.1.

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Pith. "Pith review of A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation." pith.science (2026). https://pith.science/paper/HRCWXEFZ

@misc{pith2026241220040,
  author       = {Pith},
  title        = {Pith review of: A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HRCWXEFZ}},
  note         = {Machine review of arXiv:2412.20040}
}
read the original abstract

Medication recommendation is one of the most critical health-related applications, which has attracted extensive research interest recently. Most existing works focus on a single hospital with abundant medical data. However, many small hospitals only have a few records, which hinders applying existing medication recommendation works to the real world. Thus, we seek to explore a more practical setting, i.e., multi-center medication recommendation. In this setting, most hospitals have few records, but the total number of records is large. Though small hospitals may benefit from total affluent records, it is also faced with the challenge that the data distributions between various hospitals are much different. In this work, we introduce a novel conTrastive prEtrain Model with Prompt Tuning (TEMPT) for multi-center medication recommendation, which includes two stages of pretraining and finetuning. We first design two self-supervised tasks for the pretraining stage to learn general medical knowledge. They are mask prediction and contrastive tasks, which extract the intra- and inter-relationships of input diagnosis and procedures. Furthermore, we devise a novel prompt tuning method to capture the specific information of each hospital rather than adopting the common finetuning. On the one hand, the proposed prompt tuning can better learn the heterogeneity of each hospital to fit various distributions. On the other hand, it can also relieve the catastrophic forgetting problem of finetuning. To validate the proposed model, we conduct extensive experiments on the public eICU, a multi-center medical dataset. The experimental results illustrate the effectiveness of our model. The implementation code is available to ease the reproducibility https://github.com/Applied-Machine-Learning-Lab/TEMPT.

Figures

Figures reproduced from arXiv: 2412.20040 by the authors.

Figure 1
Figure 1. The histogram of medical record counts of each hospital and the histogram of the average number of [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The heatmap to visualize Jensen–Shannon divergence of prescription distribution for all pairs of [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The pretraining stage for TEMPT. 3.3 Pretraining During the pretraining stage, we train the model on the whole multi-center records V to learn the general medical knowledge from all hospitals. However, it is challenging to capture such knowledge because of the data sparsity [94]. Besides, the correlation between diagnosis and procedures has rarely been considered before. Therefore, we design two self-supervised task… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The prompt tuning stage for TEMPT. In practice, we adopt the gradient descent method to train the whole model. The learnt general medical knowledge are embedded into the parameters E𝑑 , E𝑝 and Θ𝑒𝑛𝑐𝑜𝑑𝑒𝑟. 3.4 Prompt Tuning For recommending a proper medication set, we use…
Figure 5
Figure 5. Figure 5: The computation and storage cost of each model. [PITH_FULL_IMAGE:figures/full_fig_p017_5.png]
Figure 6
Figure 6. Figure 6: The performance of the model with various values of three hyper-parameters, [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]
Figure 7
Figure 7. Figure 7: The performance on randomly sampled 8 hospitals. “rec” represents the record number of the hospital [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.