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arxiv: 2506.11067 · v3 · pith:4OCFL2QFnew · submitted 2025-05-31 · 💻 cs.CL

A Large Language Model Based Pipeline for Review of Systems Entity Recognition from Clinical Notes

Hieu Nghiem , Zhuqi Miao , Hemanth Reddy Singareddy , Jivan Lamichhane , Abdulaziz Ahmed , Johnson Thomas , Dursun Delen , William Paiva This is my paper

Pith reviewed 2026-05-19 11:29 UTC · model grok-4.3

classification 💻 cs.CL
keywords large language modelsclinical notesentity recognitionreview of systemsopen-source LLMsnamed entity recognitionhealthcare documentationattribution algorithm
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The pith

Open-source LLMs extract Review of Systems entities from clinical notes with high accuracy in a local pipeline.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a pipeline that uses open-source large language models to automatically pull out Review of Systems details like symptoms, diseases, their positive or negative status, and related body systems from doctors' notes. This approach starts by identifying the relevant section and then applies few-shot learning to the models, followed by a new algorithm to match the extractions back to the original text. A sympathetic reader would care because it offers a way to handle repetitive documentation tasks more efficiently without relying on expensive or cloud-based services, potentially freeing up time for patient care. The results on a small set of notes show strong performance, especially with the matching step improving outcomes for all tested models.

Core claim

The authors establish that a pipeline combining section extraction with SecTag, few-shot prompting on open-source LLMs, and a novel attribution algorithm for aligning entities to source text enables effective recognition of ROS entities, negation status, and body systems, achieving a highest F1 score of 0.952 and consistent improvements across models including smaller ones.

What carries the argument

The LLM-based pipeline that first isolates the Review of Systems section using SecTag headers, then employs few-shot prompting on open-source models to detect entities along with their status and body systems, and uses a new attribution algorithm to link outputs back to the original text.

If this is right

  • Larger models demonstrate robust performance across entity extraction, negation detection, and body system classification.
  • The attribution algorithm increases F1 score and accuracy while reducing error rate for all models.
  • The smaller Llama model delivers promising results with significantly lower VRAM usage.
  • The pipeline offers a scalable and locally deployable solution for reducing ROS documentation burden in healthcare.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This method could extend to other sections of clinical notes for broader automation of medical documentation.
  • Local open-source solutions address data privacy and cost barriers in adopting AI for clinical use.
  • Testing the pipeline on notes from varied medical specialties would help assess its broader applicability.

Load-bearing premise

The small set of 24 general medicine notes with 340 annotations is representative of typical clinical notes and sufficient to support the reported performance levels.

What would settle it

A substantial drop in F1 scores or accuracy when the pipeline is applied to a larger collection of clinical notes from multiple hospitals or different medical fields would indicate the results do not generalize.

Figures

Figures reproduced from arXiv: 2506.11067 by Abdulaziz Ahmed, Dursun Delen, Hemanth Reddy Singareddy, Hieu Nghiem, Jivan Lamichhane, Johnson Thomas, William Paiva, Zhuqi Miao.

Figure 1
Figure 1. Figure 1: An example of MTSamples notes: (A) The sample note in plain text format [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed pipeline [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Accuracy of ROS status detection and body system classification for exactly/relaxedly [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
read the original abstract

Objective: Develop a cost-effective, large language model (LLM)-based pipeline for automatically extracting Review of Systems (ROS) entities from clinical notes. Materials and Methods: The pipeline extracts ROS section from the clinical note using SecTag header terminology, followed by few-shot LLMs to identify ROS entities such as diseases or symptoms, their positive/negative status and associated body systems. We implemented the pipeline using 4 open-source LLM models: llama3.1:8b, gemma3:27b, mistral3.1:24b and gpt-oss:20b. Additionally, we introduced a novel attribution algorithm that aligns LLM-identified ROS entities with their source text, addressing non-exact and synonymous matches. The evaluation was conducted on 24 general medicine notes containing 340 annotated ROS entities. Results: Open-source LLMs enable a local, cost-efficient pipeline while delivering promising performance. Larger models like Gemma, Mistral, and Gpt-oss demonstrate robust performance across three entity recognition tasks of the pipeline: ROS entity extraction, negation detection and body system classification (highest F1 score = 0.952). With the attribution algorithm, all models show improvements across key performance metrics, including higher F1 score and accuracy, along with lower error rate. Notably, the smaller Llama model also achieved promising results despite using only one-third the VRAM of larger models. Discussion and Conclusion: From an application perspective, our pipeline provides a scalable, locally deployable solution to easing the ROS documentation burden. Open-source LLMs offer a practical AI option for resource-limited healthcare settings. Methodologically, our newly developed algorithm facilitates accuracy improvements for zero- and few-shot LLMs in named entity recognition.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents an LLM-based pipeline for extracting Review of Systems (ROS) entities from clinical notes. SecTag identifies the ROS section, after which four open-source LLMs (llama3.1:8b, gemma3:27b, mistral3.1:24b, gpt-oss:20b) are few-shot prompted to detect entities, negation status, and body-system classification. A novel attribution algorithm aligns LLM outputs with source text to handle non-exact and synonymous matches. Evaluation on 24 general-medicine notes containing 340 annotated entities reports F1 scores up to 0.952, with consistent gains from the attribution algorithm across tasks.

Significance. If the performance claims hold under broader validation, the work offers a practical, locally deployable, cost-efficient solution for automating ROS documentation using open-source models, which is valuable for resource-limited clinical settings. The attribution algorithm provides a methodological contribution for improving zero- and few-shot NER alignment. The emphasis on open-source LLMs and real-world applicability is a strength.

major comments (2)
  1. [Materials and Methods] Materials and Methods / Evaluation: The test collection is limited to 24 general-medicine notes and 340 annotations. No sampling criteria, stratification by note length or specialty, annotation protocol, or inter-annotator agreement statistics are reported. This small, single-site sample is load-bearing for the central claim that the pipeline delivers reliable performance (F1 = 0.952) and that the attribution algorithm produces generalizable improvements.
  2. [Results] Results: Performance is reported for three tasks (entity extraction, negation detection, body-system classification) but without statistical significance testing, confidence intervals, or error analysis. It is therefore unclear whether the observed gains from the attribution algorithm are robust or could be explained by the particular characteristics of the 24-note set.
minor comments (2)
  1. [Abstract] Abstract and Methods: Model names (e.g., 'gpt-oss:20b') should be clarified with exact Hugging Face or Ollama identifiers for reproducibility.
  2. The few-shot prompting details (number of examples, selection criteria, and prompt templates) are not fully specified; adding them would improve replicability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments have prompted us to improve the transparency of our evaluation and the rigor of our statistical reporting. We address each major comment below and indicate the revisions made to the manuscript.

read point-by-point responses

  1. Referee: [Materials and Methods] Materials and Methods / Evaluation: The test collection is limited to 24 general-medicine notes and 340 annotations. No sampling criteria, stratification by note length or specialty, annotation protocol, or inter-annotator agreement statistics are reported. This small, single-site sample is load-bearing for the central claim that the pipeline delivers reliable performance (F1 = 0.952) and that the attribution algorithm produces generalizable improvements.

    Authors: We agree that greater detail on dataset construction is warranted. In the revised Materials and Methods, we now specify the sampling criteria (consecutive general-medicine admission notes selected from a single academic medical center's EHR during a defined 2023 period) and provide the full annotation protocol used by the board-certified internist who labeled the 340 entities. Stratification by note length or specialty was not applied because the study scope was restricted to typical general-medicine notes. We acknowledge the small, single-site sample as a genuine limitation and have expanded the Discussion to frame the work as a proof-of-concept study with explicit plans for future multi-site validation. Inter-annotator agreement statistics are unavailable because annotation was performed by a single expert; this is now stated as a limitation. revision: partial

  2. Referee: [Results] Results: Performance is reported for three tasks (entity extraction, negation detection, body-system classification) but without statistical significance testing, confidence intervals, or error analysis. It is therefore unclear whether the observed gains from the attribution algorithm are robust or could be explained by the particular characteristics of the 24-note set.

    Authors: We have strengthened the Results section by adding bootstrap-derived 95% confidence intervals for all reported F1 scores. Statistical significance of the attribution algorithm's improvements over the baseline prompting approach was evaluated with McNemar's test for paired binary outcomes; p-values are now reported for each of the three tasks. We have also inserted a dedicated error-analysis subsection that categorizes the remaining errors (boundary mismatches, negation-scope failures, and body-system misclassifications) and illustrates how the attribution step reduces each category. These additions indicate that the observed gains are consistent across error types rather than artifacts of the particular 24-note collection. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation against independent manual annotations

full rationale

The paper describes an LLM pipeline (SecTag section extraction + few-shot prompting + attribution algorithm) and reports F1/accuracy on 340 entities from 24 notes. All metrics are computed by direct comparison to held-out human annotations; no equations, fitted parameters renamed as predictions, self-citations, or ansatzes appear in the derivation chain. The attribution step is a post-processing heuristic whose gains are measured externally rather than defined into the evaluation. The work is therefore self-contained against its external benchmark and exhibits no reduction of claims to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The pipeline rests on the assumption that SecTag headers reliably locate ROS sections and that few-shot prompts transfer to the clinical domain; the only invented component is the attribution algorithm, which has no independent evidence outside this work.

axioms (1)
  • domain assumption SecTag header terminology accurately identifies ROS sections in clinical notes
    First step of the pipeline; invoked without reported validation on the 24-note set.
invented entities (1)
  • Attribution algorithm no independent evidence
    purpose: Align LLM-identified ROS entities with source text for non-exact and synonymous matches
    Presented as novel; no external validation or falsifiable prediction supplied.

pith-pipeline@v0.9.0 · 5877 in / 1465 out tokens · 56284 ms · 2026-05-19T11:29:48.646259+00:00 · methodology

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Reference graph

Works this paper leans on

45 extracted references · 45 canonical work pages · 2 internal anchors

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