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

arXiv: https://arxiv.org/abs/2606.08247 · HTML: https://mlsanity.com/paper/2606.08247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine](https://mlsanity.com/paper/2308.06834/) (2023)
- [Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering](https://mlsanity.com/paper/2403.04890/) (2024)

## Abstract (arXiv)

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.