Pith. sign in

REVIEW 3 major objections 7 minor 3 references

Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models

T0 review · 3 major / 7 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Large language models can write accurate, interpretable computable phenotypes for hypertension, and an iterative feedback loop brings a gpt-4o-generated phenotype to parity with a published symbolic-regression phenotype on held-out data.

desk verdict Worthwhile and methodologically transparent paper on LLM-written computable phenotypes, but the headline parity with FEAT is inflated by best-of-runs selection; still deserves peer review. read the letter →

arxiv 2508.05581 v1 pith:TBLHWBXB submitted 2025-08-07 cs.LG cs.AIcs.CL

Guilherme Seidyo Imai Aldeia , Daniel S. Herman , William G. La Cava This is my paper
classification cs.LGcs.AIcs.CL
keywords largelanguagemodelscomputablephenotypestreatment-resistanthypertensionprogramsynthesisinterpretablemachinelearningelectronichealthrecordsiterativerefinementSEDI
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 asks whether large language models can write computable phenotypes—small Python programs that flag patients from electronic health record data—for hypertension and its subtypes. It proposes a synthesize-execute-debug-instruct loop that feeds misclassified patients back to the model, and tests it on 1,200 chart-reviewed patients. The central result is that gpt-4o with this loop produces an interpretable phenotype for apparent treatment-resistant hypertension that matches, and after tuning its constants slightly exceeds, the AUPRC of a published symbolic-regression phenotype, while using at most 200 labeled examples. A sympathetic reader would care because computable phenotypes are today built by expensive manual chart review; this suggests a scalable, transparent route to clinical decision support for conditions like primary aldosteronism.

What carries the argument

The SEDI loop (synthesize, execute, debug, instruct): the LLM emits a Python phenotype, the code runs on the training data, and the model receives either an execution traceback or performance metrics plus up to 10 false-positive and 10 false-negative patient examples, then revises. Capped at 10 iterations, the loop shows the model at most 200 labeled patients, converting an LLM from a zero-shot code generator into a data-driven learner without fine-tuning.

What would settle it

Run the published gpt-4o+SEDI aTRH CP on a new cohort from a different health system; if its held-out AUPRC falls below the FEAT phenotype's 0.80 by more than the overlapping confidence intervals (below roughly 0.70), parity fails. Alternatively, run SEDI once without best-of-runs selection and check whether the first run's held-out AUPRC stays above 0.70.

Watch

Extended reading notes

Core claim

The paper's central claim is that an LLM, when given a natural-language phenotype description and data-driven feedback, can generate concise, interpretable Python code that identifies patients with hypertension, hypertension with hypokalemia, and apparent treatment-resistant hypertension. For the most complex target, aTRH, the best gpt-4o+SEDI program reaches held-out AUPRC 0.79 and AUROC 0.95; after a black-box optimizer tunes its constants, AUPRC rises to 0.85 with AUROC 0.94, with confidence intervals overlapping the FEAT symbolic-regression phenotype's 0.80 and 0.94. The authors conclude that LLM-generated CPs, after iterative learning, are competitive with supervised interpretable ML wh

Load-bearing premise

The parity claim assumes that the held-out performance of the single best CP picked from all cross-validation folds, seeds, and SEDI iterations is a fair estimate of what a fresh SEDI run produces, rather than an artifact of validation-set selection.

Editorial extensions

If this is right

  • LLM-drafted CPs for all three hypertension phenotypes are concise Python programs, with model sizes (8-68 syntax-tree nodes) far smaller than random-forest ensembles, so they can be inspected by clinicians.
  • SEDI improved AUPRC for gpt-4o and gpt-4o-mini on simple prompts and did not hurt performance in the cases where it did not help.
  • A gpt-4o+SEDI aTRH phenotype reaches held-out AUPRC 0.79 / AUROC 0.95, and tuning its constants raises AUPRC to 0.85, overlapping the FEAT baseline's 0.80 / 0.94.
  • Because SEDI consumes at most 200 labeled patients, the approach could cut the labeling burden that currently limits supervised phenotype modeling in new clinical sites.

Reading between the lines

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

  • The paper leaves implicit that its parity claim rests on choosing the single best CP across folds, seeds, and iterations; a prospective SEDI run without best-of-runs selection could underperform, so a preregistered replication would be the honest test.
  • Because the CP is standalone Python, a health system could inspect, adapt, and re-validate it on local EHR data without retraining any model; the paper notes CPs must be adapted across practices but does not develop this deployment workflow.
  • The model's AUPRC is lower for Black women and white women than for white men, which suggests fairness-aware SEDI feedback (subgroup metrics or per-group FP/FN sampling) as a concrete extension; the paper lists fairness in prompts only as future work.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 7 minor

Summary. The paper asks whether large language models can generate interpretable computable phenotypes (CPs) from natural-language phenotype descriptions, and introduces a synthesize-execute-debug-instruct (SEDI) loop in which an LLM iteratively refines a Python CP using execution feedback. The authors evaluate three LLMs (gpt-3.5-turbo, gpt-4o-mini, gpt-4o) on six phenotype-label combinations (HTN, HTN-HypoK, aTRH, each with heuristic and chart-review labels), under varying prompt richness and feature-set size. They compare these CPs to decision trees, logistic regression, random forests, and the FEAT symbolic regression method. The headline result is that the best gpt-4o+SEDI aTRH CP, selected from all CV folds, seeds, and iterations, reaches held-out AUPRC 0.79 (0.85 after constant tuning) and AUROC 0.94-0.95, overlapping the FEAT CP's 0.80 AUPRC / 0.94 AUROC, while producing a substantially smaller, human-readable program. The abstract claims that LLMs with iterative learning 'approach the performance of state-of-the-art ML methods while requiring significantly fewer training examples.'

Significance. If the central claim held as stated, the paper would make a useful contribution: it would demonstrate a workflow in which an LLM drafts an interpretable phenotype program from a text description and data-driven feedback, potentially lowering the cost of computable-phenotype development in clinical settings. The paper has real strengths: the SEDI framework and code are publicly released, the experimental protocol is described in unusual detail, the authors include a model-size/interpretability analysis, report subgroup performance, and explicitly list limitations. However, the headline parity with FEAT rests on selecting the single best model from a large pool of runs, while the average SEDI run is statistically worse than FEAT (Appendix E). The 'fewer training examples' claim is also undercut by the method's use of full-training-set metrics for feedback and model selection. These issues are central to the abstract's claim and require correction.

major comments (3)
  1. [Section 6, Table 4, Appendix E] The headline comparison is selected best-of-runs versus a single published FEAT result. Section 6 states the final gpt-4o model was chosen as 'the one with the highest performance' from all CPs generated across CV folds and iterations, i.e., over 5 folds × 10 seeds × up to 10 SEDI iterations. The held-out AUPRC of 0.79 (0.85 after tuning) is unbiased for that particular selected program, but it is not an unbiased estimate of the SEDI strategy's expected performance. Appendix E/Fig. 7 shows that, on average, gpt-4o+SEDI is statistically worse than FEAT for aTRH, and the Discussion itself concedes that 'gpt-4o+SEDI is outperformed by FEAT in individual trials.' The abstract's 'approach the performance' claim should be restricted to the best-found model or supported by a selection-corrected estimate (e.g., mean/median over selection protocol, or nested selection).
  2. [Section 4.1, Section 4.2] The evaluations against heuristic phenotypes are partly circular. Section 4.1 reports that the heuristics were 'iteratively refined based on evaluation in subsets of the training data in this study,' and Section 4.2 states that the rich prompts contain the heuristic definitions verbatim. Thus, for the heuristic labels, the LLM is largely being tested on prompt-following rather than on learning a phenotype from data. Since the paper presents results for six phenotype-label combinations and draws conclusions from heuristic outcomes (e.g., Fig. 3, Section 7), this is a load-bearing caveat for those claims. The aTRH chart-review comparison is less affected because those labels come from physician chart review rather than the heuristics.
  3. [Section 3.1, Appendix A, Abstract] The 'significantly fewer training examples' claim is overstated. In SEDI, the LLM receives AUROC/AUPRC computed on the full training set and FP/FN rates, and the final CP is selected by best performance on training data. The 200-sample cap applies only to the per-iteration FP/FN example lists shown to the model, not to the total label information used for feedback and selection. Consequently, the experiment does not demonstrate that CP generation 'requires significantly fewer training examples' than supervised ML; it demonstrates that the LLM can use a limited number of illustrative examples within each iteration, while still receiving aggregate labels from the entire training cohort. Please revise the claim or report the actual label usage.
minor comments (7)
  1. [Abstract] 'zero-short' should be 'zero-shot'.
  2. [Appendix A] The acronym SEDI is inconsistently expanded: the appendix title uses 'debug-inspect' while the main text uses 'debug-instruct'.
  3. [Appendix A] In the performance report message, 'Area under the precision-recall curve (AUPRC)' is typed as 'AUPCR'.
  4. [Section 4.2] 'T reatment resistant hypertension' contains a stray space; also 'resistent' appears in Appendix H.
  5. [Section 1] 'corpuses' should be 'corpora'.
  6. [Table 4] The zero-shot gpt-4o row reports identical confidence bounds (0.86–0.86, 0.61–0.61), presumably because bootstrap resampling of a single deterministic model produces degenerate intervals. This should be noted in the table footnote or the interval method should be reconsidered.
  7. [Section 7] The sentence about SEDI 'may play an important in refining models' is missing the word 'role'.

Circularity Check

1 steps flagged · score 3.0 of 10

Heuristic-label runs are partly self-definitional (rich prompt = training-tuned heuristic rule), but the main aTRH-vs-FEAT claim rests on chart-review labels and a held-out set; circularity is limited.

  1. self definitional [Section 4.1 (Target computable phenotypes) and Section 4.2 (Prompt construction)]
    "Heuristics were initially developed on the basis of clinical data expertise and iteratively refined based on evaluation in subsets of the training data in this study. ... the rich prompt is formatted as follows: <phenotype>, which we will define as <description of the heuristic>."

    For the heuristic-labeled outcomes, the evaluation target is the heuristic rule itself, and the rich prompt supplies that rule to the LLM as the phenotype description. Because the heuristics were additionally iteratively tuned on this study's training data, a rich-prompt CP that scores well against heuristic labels is reproducing the input definition (up to code-generation errors) rather than independently predicting the phenotype from data. The held-out heuristic score is therefore forced by the prompt/label construction, not by the LLM's learned phenotype concept. This circularity is confined to the heuristic-label analyses; the headline aTRH comparison uses chart-review (Dx) labels and a separate 300-patient held-out set.

full rationale

The paper's central derivation is the held-out comparison of gpt-4o+SEDI to FEAT on aTRH Dx (chart-review) labels. That comparison is not circular: the labels are clinician chart reviews, the held-out 300 patients were not used in SEDI, and FEAT is an externally published, code-released benchmark, so citing the authors' prior FEAT work is legitimate support rather than a self-citation chain. The SEDI loop is ordinary supervised training with data-driven feedback; selecting the best CP from CV folds and iterations creates a selection-bias/optimism risk in the headline parity claim, but that is a statistical correctness issue, not a circular definition. The one genuine circular element is the evaluation against heuristic labels with rich prompts: the heuristic labels were iteratively refined on training data, and the rich prompt gives the LLM their definition, so those runs largely test prompt-following and code generation against the target rule itself. The paper concedes this effect: 'This is expected, particularly for the heuristic phenotypes, as providing a precise specification in the prompt narrows the complexity of the task for the LLM.' I also flag a non-circular data-accounting issue: Appendix A shows the SEDI feedback includes AUROC/AUPRC and FP/FN rates computed on the full training set, so the 'at most 200 samples' statement undercounts the label information actually used; this weakens the 'fewer training examples' claim but is not circularity. Overall, circularity is limited to the secondary heuristic-label analyses, so the score is 3.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claim rests on expert-chosen and partly data-refined labels, a fixed feature schema, and an unverified interpretability proxy. The headline number additionally depends on best-of-runs selection and on constants fitted by nevergrad, so those fitted quantities are separated from the LLM-generated program structure.

free parameters (5)
  • LLM sampling hyperparameters = temperature = 0.5, top-p = 1.0
    Chosen 'based on preliminary experiments' (Section 5); affects every generated program and all results downstream.
  • SEDI example and iteration caps = 10 FP + 10 FN examples per iteration, 10 iterations
    Hand-set bounds on the feedback loop (Section 3.1); define the 'at most 200 labeled patients' claim.
  • Feature exclusion thresholds = variance < 0.05, < 5% non-zero, implausible values removed
    Preprocessing choices in Section 4 that determine the 311 features offered to the LLM; the LLM cannot use anything outside this schema.
  • Heuristic label rule thresholds = e.g., 2+ HTN Dx codes; 2+ of low K results, K supplements, or HypoK Dx; 2+ high BP on 3 meds or 2+ encounters on 4+ meds
    Expert-defined but 'iteratively refined based on evaluation in subsets of the training data in this study' (Section 4.1). They are the heuristic labels and are disclosed verbatim in the rich prompts.
  • Optimized constants in the final aTRH CP = values shown in Figure 4 (original LLM constants in comments)
    Fitted by nevergrad to maximize training AUPRC (Appendix F); the tuned variant drives the headline 0.85 AUPRC.
assumptions (6)
  • domain assumption Chart-reviewed JNC7-based labels are an accurate gold standard for HTN, HTN-HypoK, and aTRH
    Section 4.1 and Appendix B; all Dx-label evaluations rest on this.
  • domain assumption The 331 pre-extracted, pruned, median-imputed features sufficiently represent patient state for these phenotypes
    Section 4 and Appendix B; the LLM can only use these features, so schema adequacy bounds all results.
  • domain assumption AST node count is a valid proxy for interpretability
    Section 5; underpins the claim that LLM CPs are more interpretable than random forests.
  • domain assumption The FP/FN examples shown to the LLM are representative of its errors
    Section 3.1: examples are randomly sampled, up to 10 per class per iteration; refinement quality depends on this sampling.
  • domain assumption LLM code that passes execution is semantically consistent with the feature dictionary on unseen data
    Section 3.1 and Appendix A; no semantic verification beyond execution and performance feedback.
  • domain assumption The SEDI refinement objective does not overfit the training sample
    The final CP adjusts for 'bias in the subject sampling in the training set' (Section 7), showing the feedback loop learned a training-set artifact.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models." pith.science (2026). https://pith.science/paper/TBLHWBXB

@misc{pith2026250805581,
  author       = {Pith},
  title        = {Pith review of: Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TBLHWBXB}},
  note         = {Machine review of arXiv:2508.05581}
}
read the original abstract

Large language models (LLMs) have demonstrated remarkable capabilities for medical question answering and programming, but their potential for generating interpretable computable phenotypes (CPs) is under-explored. In this work, we investigate whether LLMs can generate accurate and concise CPs for six clinical phenotypes of varying complexity, which could be leveraged to enable scalable clinical decision support to improve care for patients with hypertension. In addition to evaluating zero-short performance, we propose and test a synthesize, execute, debug, instruct strategy that uses LLMs to generate and iteratively refine CPs using data-driven feedback. Our results show that LLMs, coupled with iterative learning, can generate interpretable and reasonably accurate programs that approach the performance of state-of-the-art ML methods while requiring significantly fewer training examples.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

3 extracted references · 1 canonical work pages

  1. [8]

    fitness”,“size

    URL http://dx.doi.org/10.1038/s41591-023-02448-8 . Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, L ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing System...

    arXiv 2017

  2. [742]

    URL https://doi.org/10.1186/s44342-024- 00023-2

    doi: 10 .1186/s44342-024-00023-2. URL https://doi.org/10.1186/s44342-024- 00023-2. Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. Large language models in medicine. Nature Medicine, 29(8):1930–1940, July 2023. ISSN 1546-170X. doi: 10 .1038/s41591-023-02448-

    work page doi:10.1186/s44342-024- 1930

  3. [3414]

    right to explanation

    doi: 10 .1146/annurev-biodatasci-080917-013315. Cristiana Catena, GianLuca Colussi, Roberta Lapenna, Elisa Nadalini, Alessandra Chi- uch, Pasquale Gianfagna, and Leonardo A. Sechi. Long-Term Cardiac Effects of Adrenalectomy or Mineralocorticoid Antagonists in Patients With Primary Aldostero- nism. Hypertension, 50(5):911–918, November 2007. ISSN 0194-911X...

    arXiv 2007

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.