LLM-based Prompt Ensemble for Reliable Medical Entity Recognition from EHRs

Ayesha Siddika Nipu; Jiawei Wu; K M Sajjadul Islam; Praveen Madiraju

REVIEW 4 major objections 5 minor 21 references

LLM-based Prompt Ensemble for Reliable Medical Entity Recognition from EHRs

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that a prompt ensemble—four prompts aggregated by embedding similarity and majority voting—lets GPT-4o classify medical entities from EHRs with F1 0.95 and recall 0.98, exceeding both DeepSeek-R1 and the 2010 challenge…

desk verdict The F1=0.95 headline is conditional on a matched subset covering only 37% of gold entities; end-to-end ensemble F1 is about 0.53. read the letter →

arxiv 2505.08704 v2 pith:5RMGFCMT submitted 2025-05-13 cs.AI cs.CL

K M Sajjadul Islam , Ayesha Siddika Nipu , Jiawei Wu , Praveen Madiraju This is my paper · ORCID

classification cs.AIcs.CL

keywords medicalentityrecognitionelectronichealthrecordspromptengineeringensembleGPT-4oDeepSeek-R1ClinicalBERTembeddingsnaturallanguageprocessing

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 tries to show that prompt engineering can make general-purpose large language models reliable enough for medical entity recognition in electronic health records without any fine-tuning. Its central result is that GPT-4o, when asked to extract entities through four different prompts and then combining those outputs by embedding similarity and majority voting, classifies medical entities with F1 0.95 and recall 0.98, above DeepSeek-R1's best F1 of 0.94 and above the 0.924 scored by the 2010 concept-extraction challenge system. The ensemble's purpose is reliability: requiring agreement across prompt formats filters hallucinations and label noise, and an 'unknown' label lets the model abstain. The paper reports the same ensemble extracts only 70 of the test document's 190 gold entities, so the headline F1 describes classification on the matched subset.

What carries the argument

The central mechanism is the prompt ensemble: four prompts (zero-shot, one-document few-shot, 100-sentence few-shot, and 5,355-entity few-shot) each return entity–label pairs; the pairs are embedded with ClinicalBERT, greedily clustered when cosine similarity is at least 0.92, and each cluster is given the majority label if two or more prompts agree, otherwise 'unknown'. This turns several individually noisy prompt outputs into a single consensus output with an explicit abstention option, and it is the mechanism the paper credits for reducing hallucination and label noise.

What would settle it

Recompute precision, recall, and F1 over all 190 gold entities in the test document, treating each unmatched gold mention as a false negative and each ensemble output that matches no gold mention as a false positive, then compare with each single-prompt result and with 0.924. If the full-extraction F1 drops below the best single prompt or below the benchmark, the claim that the prompt ensemble delivers reliable medical entity recognition would be refuted.

Watch

Extended reading notes

Core claim

On the standard 2010 clinical concept extraction benchmark, the authors claim that GPT-4o with a prompt ensemble outperforms all other tested configurations: an F1 of 0.95 and recall of 0.98 for classifying entities as Problem, Test, or Treatment, surpassing DeepSeek-R1's best F1 of 0.94 and exceeding the challenge baseline's 0.924. The ensemble uses three few-shot prompts—one annotated document, 100 annotated sentences, and 5,355 annotated entity examples—plus a zero-shot prompt, embeds each extracted entity with ClinicalBERT, clusters mentions with cosine similarity at least 0.92, and assigns the majority label when at least two prompts agree, otherwise 'unknown'. The paper also reports that entity extraction coverage is low: GPT-4o's ensemble matched only 70 of 190 gold entities in the single test document, giving extraction accuracy 0.37, and the classification metrics are conditional on that matched set. It interprets the ensemble as a reliability mechanism rather than a coverage booster, with the 'unknown' abstention designed to reduce hallucination in clinical use.

Load-bearing premise

The load-bearing assumption is that it is fair to grade classification only on the 70 gold entities whose extracted mentions cleared the 0.92 similarity cutoff, ignoring the other 120 gold entities the ensemble missed; if those misses were counted as errors, the reported F1 and recall would drop.

Editorial extensions

If this is right

GPT-4o's ensemble F1 of 0.95 is the highest among all prompt strategies tested, supporting the paper's claim that consensus across prompt formats is more reliable than any single prompt format.
DeepSeek-R1 performs nearly as well in zero-shot (F1 0.91) and with one document (0.94), but its ensemble score drops to 0.89, so the benefit of prompt ensembling is model-specific.
GPT-4o runs faster than DeepSeek-R1 in every configuration, with the largest gap in zero-shot (8.88 seconds versus 34.88 seconds), suggesting that prompt-based clinical NER can be practical in time-sensitive settings.
Entity extraction coverage did not improve with ensembling: GPT-4o's ensemble matched 70 of 190 gold entities, the same extraction accuracy as zero-shot (0.37), meaning the ensemble's gain is in classification reliability, not recall of mentions.

Reading between the lines

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

My reading: the purported superiority over the 2010 challenge's 0.924 F1 would only hold as a like-for-like comparison if the baseline were also scored on matched entities; with the 120 missed gold mentions counted as false negatives, GPT-4o's effective F1 would be far below 0.95.
A direct test of the ensemble's value is to run the same four prompts on several held-out documents and report recall over all gold mentions, with and without the 0.92 matching gate; if full recall does not beat single few-shot prompts, the ensemble should be described as a classification filter rather than a recognizer.
Because the threshold 0.92 is tuned on one test document, a useful extension would sweep the threshold and report precision/recall curves, which would show whether the headline numbers are stable or an artifact of one cutoff.
The abstention label shifts errors from the model to human reviewers; deployment would need to weigh how many 'unknown' items a clinical team can manually adjudicate.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The thing to know about arXiv:2505.08704 is that the headline F1 of 0.95 is not an end-to-end entity recognition score. It is classification accuracy computed only on the 70 entities that survived a cosine-similarity filter at threshold 0.92. Table IV shows the ensemble matched only 70 of 190 gold entities (37% extraction recall). If you compute standard NER metrics over the full gold set, the ensemble's F1 is about 0.53, not 0.95.

What is genuinely new here is a concrete test of a prompt ensemble—three few-shot variants whose outputs are aligned via ClinicalBERT embeddings and resolved by majority voting—on two current LLMs, GPT-4o and DeepSeek-R1, on the 2010 i2b2/VA challenge. I don't know of another paper that runs exactly this combination on these models. The writing is clear, the authors separate extraction from classification, and they report timing and token-limit problems, which is useful practical detail.

The soft spot is the evaluation. The 0.95 F1 and 0.98 recall are conditional on the matched subset, and the paper itself concedes 'entity extraction performance is low.' The comparison with the i2b2/VA system's 0.924 F1 is invalid because that is an end-to-end extraction score, not a classification-only score on matched entities. On top of that, the similarity threshold is empirically chosen on the same test document, so the headline metric is partly fitted. The test set is a single document with 190 entities, so there is no confidence interval and no evidence of generality. The 'reliability' claim is also hard to swallow: the ensemble has the lowest extraction accuracy of any GPT-4o prompt (0.37, tied with zero-shot), meaning it reduces coverage while improving consistency on a small selected subset.

Who gets value from this? Someone working on prompt-based clinical NER might read it as a reminder of the difference between extraction and classification, and as a worked example of embedding-based alignment. But the abstract oversells the result, and the evaluation needs rework. I would not cite it in its current form.

I'd send it to a serious referee rather than desk reject, because the experiment is checkable and the flaws are fixable. But the referee should be told that the F1 metric needs to be recomputed end-to-end, the dataset split expanded, and the claims scaled back. As it stands, the central claim is unsupported.

Referee Report

4 major / 5 minor

Summary. The paper proposes a prompt-ensemble method for medical named entity recognition (NER) from EHRs, combining zero-shot, few-shot (document, sentence, and entity levels), and a majority-voting ensemble over four prompting configurations. The authors evaluate GPT-4o and DeepSeek-R1 on a single test document from the 2010 i2b2/VA dataset, reporting entity extraction accuracy (Table IV) and classification precision/recall/F1 (Table V). The abstract claims that GPT-4o with the prompt ensemble achieves an F1-score of 0.95 and recall of 0.98, outperforming DeepSeek-R1 and surpassing the i2b2/VA system's F1 of 0.924.

Significance. If the reported F1 of 0.95 represented end-to-end NER performance, the paper would demonstrate a practical and flexible LLM-based alternative to specialized clinical NER systems, with the prompt ensemble providing a novel aggregation mechanism. The use of a public benchmark (i2b2/VA) is a strength, as is the explicit pseudocode for the ensemble and the attempt to mitigate hallucination via an 'unknown' label. However, the central evaluation is fundamentally flawed: the classification metrics are computed only on entities that survive a data-dependent cosine-similarity threshold, so the headline numbers do not measure full extraction and classification. The paper's own acknowledgment that 'entity extraction performance is low' (Section IV) is in direct tension with the abstract's unqualified claim, and the comparison with the i2b2/VA end-to-end baseline is not valid. As a result, the claimed significance is not supported by the evidence in the manuscript.

major comments (4)

[Section IV, Tables IV and V] The abstract's claim of F1=0.95 and recall=0.98 is unsupported by the paper's own tables. Table IV reports that the GPT-4o ensemble predicted 75 entities, matched only 70 of 190 gold entities, and achieved an extraction accuracy of 0.37. Table V reports precision=0.92, recall=0.98, and F1=0.95 for the same configuration. These numbers can only be reconciled if precision, recall, and F1 are computed exclusively on the 70 matched entities. Under standard NER evaluation, the 120 unmatched gold mentions are false negatives, giving recall at most 70/190≈0.37 and F1 at most approximately 0.53 (assuming 75 predictions and 70 true positives). The headline F1 is therefore conditional on the matching filter and does not measure the complete extraction-plus-classification pipeline, which is the task claimed in the abstract and introduction.
[Section IV, first paragraph; Algorithm 1] The cosine similarity threshold τ=0.92 is described as 'empirically chosen' in Section IV. This threshold directly determines which predicted entities are included in the classification evaluation, because entities whose similarity to a gold mention falls below τ are not scored. The reported F1 is thus not a model performance measure independent of the evaluation pipeline; it is partly an artifact of a fitted filter. The authors provide no sensitivity analysis (e.g., τ=0.90 or 0.95), no held-out validation for the threshold, and no justification for choosing 0.92 beyond post hoc matching behavior. This circularity undermines the central claim that the ensemble 'achieved' an F1 of 0.95.
[Section IV, discussion of Table V] The comparison of the ensemble F1=0.95 with the i2b2/VA system's F1=0.924 is invalid. The i2b2/VA F1 is an end-to-end concept extraction and classification measure computed over all gold entities in the benchmark, whereas the reported 0.95 is computed only on the matched subset of 70 entities. These are different quantities, and the paper even concedes this tension in the sentence 'Although entity extraction performance is low, the classification F1-score surpasses the 0.924 achieved by the i2b2/VA concept extraction system.' A conditional classification score on a small filtered subset cannot be compared with an end-to-end benchmark score, and the abstract's 'surpassing' claim is therefore not supported.
[Section III-A, Table II] The entire evaluation is performed on a single test document containing 190 gold entities. This sample is far too small to support the strong comparative claims made in the paper, such as GPT-4o consistently outperforming DeepSeek-R1 and the rank ordering of prompting strategies. No confidence intervals, significance tests, or error bars are provided, and no analysis of variance across documents is offered. The single-document design compounds the metric-definition problem and makes the headline numbers unreliable even as descriptive statistics.

minor comments (5)

[Algorithm 1 and Table V] The handling of predictions labeled 'unknown' (those clusters with fewer than two votes) is not specified in the precision/recall computation. The paper should state clearly whether 'unknown' counts as an incorrect label, is excluded from the denominator, or is treated as a separate category, because this materially affects the reported metrics in Table V.
[Section III-B, Figure 2] The prompt template is described at a high level but the exact prompts used for each few-shot configuration and for the ensemble are not included. Providing the full prompts (or an appendix) would be necessary for reproducibility, especially since the paper positions prompt design as a core contribution.
[Section II-C, paragraph 2] The sentence 'Shen et al. evaluated ChatGPT...' cites reference [13], but reference [13] is the Huang et al. paper. Please verify the intended citation for the Shen et al. work.
[Throughout] The corpus name is typeset as 'i2b2/V A' with a stray space in multiple places (e.g., Section II-A, Section III-A, Table IV context); it should be 'i2b2/VA'.
[Section III-B, paragraph 1] The phrase 'top p value of 1' is ambiguous; specify whether this refers to nucleus sampling with p=1.0 or to another parameter, and clarify how this interacts with temperature=0.2 in the API configuration.

Circularity Check

1 steps flagged · score 6.0 of 10

Headline F1=0.95 is a conditional metric over 70 matched entities, not an end-to-end NER result.

fitted input called prediction [Abstract; Section IV (paragraph after Table IV); Algorithm 1; Tables IV-V]
"GPT-4o with prompt ensemble achieved the highest classification performance with an F1-score of 0.95 and recall of 0.98. ... We applied a similarity threshold of 0.92, which was empirically chosen to keep the matches accurate while still covering enough relevant entities."

The headline F1=0.95 and recall=0.98 are computed only for the 70 entities that survived the empirically chosen cosine-similarity threshold tau=0.92. Table IV reports GPT-4o Ensemble as Predict=75, Match=70, Accuracy=0.37, meaning 70 of 190 gold entities were matched. If recall and F1 were computed over all gold entities, recall would be 70/190=0.37 and F1 about 0.53, not 0.98/0.95. The only way to obtain Table V's numbers is to score classification on the 70 matched entities, excluding the 120 unmatched gold mentions. The threshold is not an independent evaluation protocol; it is a fitted input (Algorithm 1's tau) that defines the scored subset.

full rationale

This is not a derivation-style paper, so most of the manuscript is not circular in the sense of an equation reducing to its inputs. No load-bearing self-citation chain is present; the author's prior citations [11] and [20] are background references, not the basis of the central result. The circularity concern is concentrated in the evaluation: the ensemble's headline performance depends on the empirically chosen cosine threshold tau=0.92, and the same threshold determines which extracted entities are scored. Table IV's Match=70 out of 190 gold entities means that the F1=0.95 and recall=0.98 in Table V can only be interpreted over the matched subset. Under standard NER evaluation, the 120 unmatched gold mentions are false negatives, giving recall approximately 0.37 and F1 approximately 0.53. The paper even admits the low extraction accuracy while separately claiming the classification F1 surpasses i2b2/VA's 0.924. Therefore the central 'GPT-4o with prompt ensemble achieves F1=0.95' claim is an artifact of a fitted, self-selected evaluation subset rather than an independent measure of the full extraction-and-classification pipeline. This warrants a partial circularity score of 6.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The main free parameter is the embedding similarity threshold, which is tuned on the data and filters the entity set before classification metrics are computed. No new theoretical entities are introduced.

free parameters (1)

cosine similarity threshold = 0.92
Empirically chosen in Section IV to balance matching accuracy and coverage; directly controls which entities enter the F1 computation.

assumptions (3)

domain assumption ClinicalBERT embeddings capture semantic similarity sufficiently for entity matching at threshold 0.92
The reliability of the ensemble depends on embedding similarity to align equivalent entity mentions; this is assumed in Section III-B and Table III.
domain assumption The 2010 i2b2/VA gold annotations are correct and complete
The evaluation treats the 190 test annotations as ground truth.
domain assumption LLM API outputs are parseable and consistent across runs
The method assumes models follow the requested XML-style output format at temperature 0.2.

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Cite this review

Pith. "Pith review of LLM-based Prompt Ensemble for Reliable Medical Entity Recognition from EHRs." pith.science (2026). https://pith.science/paper/5RMGFCMT

@misc{pith2026250508704,
  author       = {Pith},
  title        = {Pith review of: LLM-based Prompt Ensemble for Reliable Medical Entity Recognition from EHRs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5RMGFCMT}},
  note         = {Machine review of arXiv:2505.08704}
}

read the original abstract

Electronic Health Records (EHRs) are digital records of patient information, often containing unstructured clinical text. Named Entity Recognition (NER) is essential in EHRs for extracting key medical entities like problems, tests, and treatments to support downstream clinical applications. This paper explores prompt-based medical entity recognition using large language models (LLMs), specifically GPT-4o and DeepSeek-R1, guided by various prompt engineering techniques, including zero-shot, few-shot, and an ensemble approach. Among all strategies, GPT-4o with prompt ensemble achieved the highest classification performance with an F1-score of 0.95 and recall of 0.98, outperforming DeepSeek-R1 on the task. The ensemble method improved reliability by aggregating outputs through embedding-based similarity and majority voting.

Figures

Figures reproduced from arXiv: 2505.08704 by the authors.

**Figure 1.** Example of entities in EHR to identify clinical concepts such as medications, symptoms and diagnoses. These systems demonstrated good precision in restricted domains, but were time-consuming to maintain and lacked adaptability to unseen data [6]. To overcome these limitations, statistical machine learning models like Conditional Random Fields (CRFs) and Support Vector Machines (SVMs) were introduced, treating NER a… view at source ↗

**Figure 4.** Prompt Execution Time types. These findings highlight the importance of prompt efficiency and model-specific tuning when designing few-shot configurations, particularly for large-scale or time-sensitive clinical applications. Ethical Consideration: In our study, we used the publicly available and anonymized dataset from the 2010 i2b2/VA NLP Challenge, which removes risks related to patient identification as the reco… view at source ↗

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Reference graph

Works this paper leans on

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Pith tools

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

[1] [1]

The evolving use of electronic health records (ehr) for research,

E. Kim, S. M. Rubinstein, K. T. Nead, A. P. Wojcieszynski, P. E. Gabriel, and J. L. Warner, “The evolving use of electronic health records (ehr) for research,” in Seminars in radiation oncology , vol. 29, no. 4. Elsevier, 2019, pp. 354–361

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[3] [3]

Clinical named entity recognition: Challenges and opportunities,

S. R. Kundeti, J. Vijayananda, S. Mujjiga, and M. Kalyan, “Clinical named entity recognition: Challenges and opportunities,” in 2016 IEEE International Conference on Big Data (Big Data) . IEEE, 2016, pp. 1937–1945

work page 2016

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Gpt-ner: Named entity recognition via large language models,

S. Wang, X. Sun, X. Li, R. Ouyang, F. Wu, T. Zhang, J. Li, and G. Wang, “Gpt-ner: Named entity recognition via large language models,” arXiv preprint arXiv:2304.10428, 2023

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Y . Hu, Q. Chen, J. Du, X. Peng, V . K. Keloth, X. Zuo, Y . Zhou, Z. Li, X. Jiang, Z. Lu et al. , “Improving large language models for clinical named entity recognition via prompt engineering,” Journal of the American Medical Informatics Association , vol. 31, no. 9, pp. 1812– 1820, 2024

work page 2024

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A survey on recent named entity recognition and relationship extraction techniques on clinical texts,

P. Bose, S. Srinivasan, W. C. Sleeman IV , J. Palta, R. Kapoor, and P. Ghosh, “A survey on recent named entity recognition and relationship extraction techniques on clinical texts,” Applied Sciences, vol. 11, no. 18, p. 8319, 2021

work page 2021

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Clinical concept extraction: a methodology review,

S. Fu, D. Chen, H. He, S. Liu, S. Moon, K. J. Peterson, F. Shen, L. Wang, Y . Wang, A. Wen et al. , “Clinical concept extraction: a methodology review,” Journal of biomedical informatics , vol. 109, p. 103526, 2020

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Biobert based named entity recognition in electronic medical record,

X. Yu, W. Hu, S. Lu, X. Sun, and Z. Yuan, “Biobert based named entity recognition in electronic medical record,” in 2019 10th interna- tional conference on information technology in medicine and education (ITME). IEEE, 2019, pp. 49–52

2019