Enterprise AI Analysis
SePA: A Search-enhanced Predictive Agent for Personalized Health Coaching
This paper introduces SePA (Search-enhanced Predictive AI Agent), a novel LLM health coaching system that integrates personalized machine learning and retrieval-augmented generation to deliver adaptive, evidence-based guidance, designed to overcome limitations in existing systems by incorporating transparency and evidence-based coaching principles.
Key Performance Indicators & Impact
SePA significantly advances personalized health coaching through superior predictive accuracy and robust, verifiable guidance.
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Stress Prediction Accuracy (PHM)
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Injury Risk Prediction Accuracy (PHM)
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RAG Performance Lift in LLMs
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Expert First-Place Votes for SePA-web
Deep Analysis & Enterprise Applications
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Predictive Modeling
Retrieval-Augmented Generation (RAG)
System Architecture & Privacy
Performance & Latency
R² > 0.50
Stress Prediction Accuracy with Personalized Models
SePA's Personalized Health Models (PHMs) achieve a robust coefficient of determination (R²) of over 0.50 for stress predictions, and over 0.40 for injury risk, significantly outperforming generalized baselines.
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| Aspect | SePA's RAG Pipeline | Prior RAG Systems (e.g., PHIA) |
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| Contextual Relevance |
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| Trustworthiness |
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| Transparency & Reproducibility |
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1.35 Mean Rank
Expert Preference for RAG-enhanced Advice
A pilot expert study showed SePA-web achieved a superior mean rank of 1.35 compared to 1.65 for the SePA-no-web system, with a meaningful practical effect (Cliff's δ=0.30).
SePA's Web Retrieval Pipeline Flow
Query Contextualization
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Cache Check
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Trusted Retrieval
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Scraping & Cleaning
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Document-Level Reranking
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Semantic Similarity Search
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Response Synthesis
Ensuring Trust and Confidentiality in Health AI
SePA's architecture prioritizes user privacy through three critical layers: (1) Raw user health data is ephemeral and deleted post-processing. (2) No personally identifiable information is ever sent to external APIs; only anonymized search queries are transmitted. (3) Users can opt for no-retention LLM endpoints that discard all data after inference. This multi-layered approach provides a transparent and reproducible blueprint for critical system components, fostering community scrutiny and improvement.
Key Takeaway: SePA provides a transparent and reproducible blueprint for critical system components, fostering community scrutiny and improvement.
19.69s
Median Response Time with Web Retrieval Enabled
Enabling SePA's web-retrieval pipeline increases the median response time from 4.41s to 19.69s, reflecting the inherent overhead of multi-step retrieval processes (document fetching, processing, reranking, and semantic search).
Optimizing for Real-time Health Coaching
The design of SePA reflects a crucial trade-off between response quality and system latency. Our model selection balances retrieval quality with real-time interactivity within typical web server hardware constraints. While larger models might boost accuracy, their latency would be prohibitive for conversational contexts. This highlights that future optimization is crucial, potentially through techniques like model distillation, to enhance user experience without sacrificing guidance quality.
Key Takeaway: Future optimization through techniques like model distillation can improve user experience without sacrificing guidance quality.
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Full-Scale Deployment & Integration
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Monitoring & Continuous Improvement
Ongoing performance monitoring, regular updates, iterative enhancements based on feedback and new data, and long-term support.
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