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arxiv: 2606.08247 · v1 · pith:MATGLS3Fnew · submitted 2026-06-06 · 📡 eess.AS · cs.AI· cs.LG· eess.SP

AeroSpectra Sentinel: An Auditable LLM Prompt-Chaining Decision-Support Workflow for Acute Asthma Risk Assessment from Respiratory Sounds and Clinical Signals

Aueaphum Aueawatthanaphisut This is my paper

Pith reviewed 2026-06-27 19:09 UTC · model grok-4.3

classification 📡 eess.AS cs.AIcs.LGeess.SP
keywords asthmarespiratory soundsLLM prompt chainingmachine learningclinical decision supportaudio classificationguardrailsFHIR
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The pith

A workflow fuses respiratory sound ML screening at 91% accuracy with five-stage LLM prompt chaining and guardrails to support auditable acute asthma risk assessment.

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

The paper presents AeroSpectra Sentinel as a client-side prototype that processes respiratory sounds via short-time Fourier transform, applies machine learning for asthma screening, fuses clinical signals, and routes results through a five-stage LLM prompt-chaining process with guardrails and FHIR schema validation. On a stratified subset of 584 recordings from a public dataset, a random forest classifier reached 91.10% binary accuracy and 78.69% F1-score for asthma versus non-asthma, while a guardrail-plus-schema variant of the LLM workflow produced the strongest safety and documentation consistency across 40 simulated clinical vignettes. A sympathetic reader would care because conventional audio classifiers often lack transparent reasoning or escalation logic, and this staged approach aims to make LLM-assisted interpretation of multiple signals more reliable for rapid decisions in acute asthma. The system is explicitly positioned as research software, not a validated medical device.

Core claim

The central claim is that the AeroSpectra Sentinel workflow, which separates signal acquisition, STFT-based acoustic feature extraction, ML screening, clinical guardrails, and a five-stage LLM prompt-chaining process, enables both high-accuracy binary asthma screening (91.10% accuracy and 78.69% F1-score via random forest on 584 recordings) and stronger simulated safety plus documentation consistency via the guardrail-plus-schema variant on 40 vignettes, all while remaining auditable and client-side.

What carries the argument

The five-stage LLM prompt-chaining process with clinical guardrails and FHIR schema validation, which separates acoustic screening from auditable clinical reasoning and reporting.

If this is right

  • The random forest on acoustic features can distinguish asthma from non-asthma at 91.10% accuracy on the tested subset.
  • Adding guardrails and FHIR schema validation to prompt chaining produces stronger safety and documentation consistency than one-shot or plain chaining.
  • The staged workflow allows separation of signal preprocessing, ML screening, and LLM reasoning for auditability.
  • The system generates FHIR-ready reports suitable for clinical decision support.
  • Multiclass classification on the same data reaches 77.40% accuracy and 77.23% macro-F1.

Where Pith is reading between the lines

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

  • If the accuracy holds on broader data, the workflow could integrate into mobile apps for preliminary asthma screening before clinician review.
  • The client-side design implies reduced data transmission risks compared to fully cloud-based systems.
  • Retraining the ML component on condition-specific datasets could extend the approach to related respiratory assessments.
  • The emphasis on guardrails suggests a template for making other LLM medical workflows more controllable.

Load-bearing premise

That results from a public respiratory sound dataset subset and performance on simulated clinical vignettes will generalize to reliable, safe operation with real patients and live clinical signals.

What would settle it

A drop in random forest binary accuracy below 80% or failure of the guardrail-plus-schema variant to maintain safety and consistency when tested on a new collection of real patient recordings paired with live clinical signals and vignettes.

Figures

Figures reproduced from arXiv: 2606.08247 by Aueaphum Aueawatthanaphisut.

Figure 1
Figure 1. Figure 1: End-to-end system architecture of AeroSpectra Sentinel. Respiratory audio is transformed into spectral evidence, machine-learning outputs, clinical [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Detailed signal-processing pipeline for respiratory sound analysis. The raw acoustic waveform is conditioned by normalization, high-pass filtering, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Technical prompt-chain schematic. Each stage consumes a typed evidence bundle, applies a constrained prompt policy, and emits a schema-checked [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Class distribution of the uploaded respiratory sound dataset. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Accuracy and asthma-class F1-score for binary asthma screening. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Confusion matrix of the best binary asthma screening model. Rows [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Waveform and spectrogram evidence. The asthma example shows more concentrated time–frequency structure in the wheeze band, while the healthy [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ablation study on acoustic feature groups. Frequency-band ratios [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
read the original abstract

Acute asthma risk assessment requires rapid interpretation of respiratory sounds, oxygenation, airflow limitation, speech ability, work of breathing, mental status, and response to reliever therapy. Conventional audio-only classifiers can detect wheeze-like patterns but often lack transparent clinical reasoning and safe escalation logic. This paper presents AeroSpectra Sentinel, a client-side research prototype and decision-support workflow that combines short-time Fourier transform (STFT) respiratory sound analysis, lightweight machine-learning screening, clinical feature fusion, and a five-stage large language model (LLM) prompt-chaining process. The workflow separates signal acquisition, preprocessing, acoustic feature extraction, ML screening, clinical guardrails, and FHIR-ready reporting. We evaluated the audio screening component on a public respiratory sound dataset containing 1,211 WAV recordings from five labels. Using a stratified subset of 584 recordings, a random forest achieved 91.10% binary accuracy and 78.69% F1-score for asthma-vs-non-asthma screening, while a feature-based multilayer perceptron achieved 89.73% accuracy and 78.26% F1-score. A compact log-spectrogram CNN achieved 73.29% accuracy and 55.17% F1-score. Multiclass classification achieved 77.40% accuracy and 77.23% macro-F1. To evaluate the LLM workflow, we conducted a scenario-based audit on 40 simulated clinical vignettes comparing one-shot prompting, prompt chaining, prompt chaining with guardrails, and prompt chaining with guardrails plus FHIR schema validation. The guardrail-plus-schema variant achieved the strongest simulated safety and documentation consistency. AeroSpectra Sentinel is intended as a research prototype, not as a diagnostic medical device or clinically validated risk-assessment product.

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

3 major / 2 minor

Summary. The paper presents AeroSpectra Sentinel, a client-side research prototype workflow for acute asthma risk assessment that integrates STFT-based respiratory sound analysis, lightweight ML screening (random forest achieving 91.10% binary accuracy and 78.69% F1-score on a stratified 584-recording subset from a 1,211-recording public dataset), clinical feature fusion, and a five-stage LLM prompt-chaining process with guardrails and FHIR schema validation. The LLM component is evaluated via a scenario-based audit on 40 simulated clinical vignettes, where the guardrail-plus-schema variant is reported to achieve the strongest simulated safety and documentation consistency. The work explicitly positions itself as a research prototype rather than a clinically validated device.

Significance. If the proxy results hold under real conditions, the work contributes an auditable, modular client-side framework combining audio ML with LLM chaining for respiratory decision support, with credit due for reporting concrete metrics on a named public dataset and for the emphasis on guardrails and schema validation. The significance remains modest because the central safety and escalation claims rest entirely on simulated vignettes without real-patient data, limiting immediate implications for clinical use.

major comments (3)
  1. [Abstract and LLM workflow evaluation] Abstract and LLM workflow evaluation section: the claim that the guardrail-plus-schema variant achieved the strongest simulated safety and documentation consistency rests on 40 vignettes without reported quantitative metrics, error bars, vignette construction details, or comparison to expert judgments, which is load-bearing for the LLM component's contribution to safe escalation logic.
  2. [Audio screening evaluation] Audio screening evaluation section: the random forest results (91.10% accuracy, 78.69% F1 on 584 recordings) lack specification of patient-independent splits or the exact label distribution in the stratified subset drawn from the five-class public corpus, undermining assessment of generalization to live clinical signals.
  3. [Overall workflow description] Overall workflow description: no external clinical cohort, prospective deployment data, or real-patient validation is provided to support transfer from the public dataset subset and simulated vignettes to acute asthma presentations, which is load-bearing for the decision-support workflow claim.
minor comments (2)
  1. [Abstract] The abstract could more explicitly quantify the limitations of the simulated vignette evaluation to prevent overinterpretation of the safety results.
  2. [Methods] Notation for the five-stage prompt-chaining process and the distinction between guardrails and schema validation could be clarified with a diagram or pseudocode for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive review and for recognizing the prototype nature of the work. We address each major comment below with planned revisions to improve transparency and methodological detail while preserving the manuscript's scope as a research prototype.

read point-by-point responses

  1. Referee: [Abstract and LLM workflow evaluation] Abstract and LLM workflow evaluation section: the claim that the guardrail-plus-schema variant achieved the strongest simulated safety and documentation consistency rests on 40 vignettes without reported quantitative metrics, error bars, vignette construction details, or comparison to expert judgments, which is load-bearing for the LLM component's contribution to safe escalation logic.

    Authors: We agree that the current description of the LLM evaluation is insufficiently detailed. The evaluation consists of a scenario-based audit on 40 simulated vignettes, and the manuscript does not report quantitative metrics, error bars, or expert comparisons. In revision we will expand the LLM workflow evaluation section to describe vignette construction criteria, the specific safety and consistency scoring rubric applied, and any available per-variant counts or qualitative observations. We will also add an explicit statement that the audit is simulated and not a substitute for expert or real-world validation. revision: partial

  2. Referee: [Audio screening evaluation] Audio screening evaluation section: the random forest results (91.10% accuracy, 78.69% F1 on 584 recordings) lack specification of patient-independent splits or the exact label distribution in the stratified subset drawn from the five-class public corpus, undermining assessment of generalization to live clinical signals.

    Authors: We accept this criticism. The manuscript will be revised to state whether the 584-recording stratified subset used patient-independent partitioning and to report the exact per-class counts (asthma vs. non-asthma and the original five-class breakdown) in both the training and test portions. revision: yes

  3. Referee: [Overall workflow description] Overall workflow description: no external clinical cohort, prospective deployment data, or real-patient validation is provided to support transfer from the public dataset subset and simulated vignettes to acute asthma presentations, which is load-bearing for the decision-support workflow claim.

    Authors: We agree that the work contains no real-patient cohort or prospective data. The manuscript already positions AeroSpectra Sentinel as a research prototype rather than a clinically validated device. We will strengthen the limitations and future-work paragraphs to more explicitly discuss the gap between public-dataset/simulated results and live clinical deployment, and to outline the additional validation steps required before any clinical use. revision: partial

Circularity Check

0 steps flagged

No circularity; all claims are direct empirical measurements on external public data and simulations

full rationale

The manuscript contains no derivation chain, first-principles predictions, or fitted-parameter reductions. Central results consist of (a) random-forest, MLP, and CNN accuracy/F1 numbers obtained by training and testing on a stratified 584-recording subset of the publicly available 1,211-recording respiratory-sound corpus, and (b) comparative safety/consistency scores on 40 hand-crafted simulated vignettes for four LLM prompting variants. These are straightforward hold-out or scenario-based evaluations; no quantity is defined in terms of itself, no input is relabeled as a prediction, and no load-bearing premise rests on self-citation. The paper therefore satisfies the self-contained empirical criterion and receives the lowest circularity score.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so specific free parameters, axioms, or invented entities cannot be enumerated; the work implicitly relies on standard assumptions of ML generalization and LLM reliability from prior literature.

pith-pipeline@v0.9.1-grok · 5872 in / 1220 out tokens · 31456 ms · 2026-06-27T19:09:16.726132+00:00 · methodology

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