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

2026-06-06 · Source: Machine Learning · Field: Health & Wellbeing — Clinical Care & Medical Practice, Medical Devices & Health Technology, Health & Medical Research · Depth: Advanced, quick

Summary

AeroSpectra Sentinel is a client-side research prototype designed as a decision-support workflow for acute asthma risk assessment, integrating respiratory sounds and clinical signals. It combines short-time Fourier transform (STFT) analysis, lightweight machine-learning screening, clinical feature fusion, and a five-stage large language model (LLM) prompt-chaining process. The system separates signal acquisition, preprocessing, acoustic feature extraction, ML screening, clinical guardrails, and FHIR-ready reporting. Its audio screening component was evaluated on a public dataset of 584 recordings, where a random forest achieved 91.10% binary accuracy and 78.69% F1-score for asthma-vs-non-asthma. The LLM workflow, tested on 40 simulated clinical vignettes, demonstrated that a guardrail-plus-FHIR schema validation variant achieved the strongest simulated safety and documentation consistency compared to other prompting methods. This prototype is intended for research, not as a diagnostic medical device.

Key takeaway

For research scientists developing AI-driven clinical decision support for acute respiratory conditions, you should prioritize auditable, multi-modal workflows that integrate both machine learning and large language models. Implement prompt chaining with explicit clinical guardrails and FHIR schema validation to enhance simulated safety and documentation consistency. This approach helps ensure transparency and reliability, moving beyond opaque audio-only classifiers towards more robust and accountable systems in early-stage medical AI development.

Key insights

An auditable LLM prompt-chaining workflow can enhance acute asthma risk assessment by integrating respiratory sounds and clinical data.

Principles

• Integrating clinical guardrails and FHIR schema validation significantly improves LLM workflow safety and documentation consistency.
• Combining acoustic feature extraction with ML screening and LLM reasoning provides a multi-modal approach to complex medical assessment.

Method

The workflow involves signal acquisition, preprocessing, acoustic feature extraction, ML screening, clinical guardrails, and a five-stage LLM prompt-chaining process for FHIR-ready reporting.

In practice

• Consider random forest or multilayer perceptron for high-accuracy binary audio classification in medical screening.
• Apply prompt chaining with guardrails and FHIR schema validation to improve safety and consistency in LLM-driven clinical decision support.

Topics

• LLM Prompt Chaining
• Acute Asthma Risk Assessment
• Respiratory Sound Analysis
• Clinical Decision Support
• FHIR Schema Validation
• Machine Learning Screening

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.