AI-Assistant For Personal Health Management

The Problem (What We Are Solving)

An individual's health data is fragmented across dozens of sources: wearable fitness trackers, diet logging apps, electronic medical records (EMRs), and pharmacy data. This creates a siloed, incomplete picture, making it impossible for a user to see the connections between their lifestyle and health outcomes. Users lack a single source of truth and the tools to receive proactive, personalized guidance.

The Solution (What We Are Building)

We will build an intelligent assistant, delivered through a mobile app, that unifies this fragmented data into a single, queryable "Unified Health Record" (UHR). Using AI and ML, the assistant will identify trends, predict potential risks, and provide actionable insights through a conversational interface, transforming reactive health monitoring into proactive health management.

Core Features

• Unified Health Dashboard: A single interface displaying key metrics from all connected sources.
• Data Ingestion Engine: Connectors for major wearables (Fitbit, Apple Health), EMRs (via PDF/image upload), and manual entry.
• Proactive Health Alerts: Intelligent notifications for anomalies or potential health issues (e.g., "Your resting heart rate has been 15% higher than average this week").
• Natural Language Querying: A chat interface to ask complex questions like, "How did my sleep quality change when I started my new medication last month?".
• Personalized Goal Setting: AI-driven recommendations for health goals based on the user's comprehensive data profile.

Architectural Components & Technologies

High-Level System Architecture

This diagram provides a simplified, conceptual view of the system's core AI logic. It illustrates how user data is processed into different forms of memory and how the AI uses that memory to generate intelligent, personalized responses, both reactively and proactively.

Building Guide: A Step-by-Step Explanation

This guide explains how to build each component of the system architecture, following the flow of data from input to insight.

1. User Data Inputs

This is the starting point of the entire system. The platform is designed to accept data from multiple sources to build a comprehensive health profile.

• EMR/PDF Documents: Users can upload medical records, lab results, or doctor's notes as PDF or image files. This unstructured data is crucial for capturing clinical context.
• Wearable Data: The system connects to APIs from popular wearables (Fitbit, Apple Health, etc.) to ingest structured, time-series data like heart rate, sleep cycles, and activity levels.
• Manual Notes & Logs: Users can directly input qualitative data, such as daily mood, symptoms, food logs, or medication adherence. This provides personal context that automated systems can't capture.

2. Data Processing & Embedding

Once data is ingested, it must be converted into a format the AI can understand.

• OCR & Text Extraction: For uploaded documents, an Optical Character Recognition (OCR) service like AWS Textract scans the files and extracts the raw text.
• Embedding Model: This is a critical AI step. All unstructured text (from OCR and manual notes) is fed through an embedding model (e.g., SentenceTransformer). This model converts the text into numerical vectors (embeddings) that capture its semantic meaning. This allows the AI to understand that "headache" and "migraine" are conceptually similar, for example.

3. AI Memory Stores

The processed data is stored in a dual-memory system, mimicking how human memory works.

• Long-Term Memory (Vector DB / Data Warehouse): This is the permanent knowledge base. The numerical embeddings of text are stored in a Vector Database (e.g., Pinecone) for fast semantic searching. Structured data from wearables is cleaned and stored in a Data Warehouse (e.g., Snowflake) for analytical queries.
• Short-Term Memory (Cache): A high-speed cache (e.g., Redis) is used to store the recent history of the user's conversation with the AI. This gives the assistant context, allowing it to understand follow-up questions without starting from scratch every time.

4. Conversational Interaction Loop (Reactive)

This is the primary way the user interacts with the assistant.

• User Asks a Question: The user types a question in natural language into the mobile app.
• RAG & Query Engine: The engine receives the question. It first retrieves relevant information from both Long-Term Memory (finding similar notes in the Vector DB or querying metrics from the Data Warehouse) and Short-Term Memory (recalling the last few messages).
• Large Language Model (LLM): The original question, plus all the retrieved context, is sent to a powerful LLM. The LLM's job is to synthesize this information into a coherent, human-readable answer.
• Personalized Insight: The final, context-rich answer is sent back to the user's app.

# rag_query_service.py - Conceptual flow
from llama_index.core import VectorStoreIndex, SQLDatabase
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.tools import QueryEngineTool

# 1. Create tools that know how to query different data sources
sql_tool = QueryEngineTool.from_defaults(
    query_engine=sql_query_engine, # Connects to the Data Warehouse
    description="Use for questions about structured metrics like heart rate over time."
)
vector_tool = QueryEngineTool.from_defaults(
    query_engine=vector_query_engine, # Connects to the Vector DB
    description="Use for questions about unstructured notes, symptoms, or doctor's advice."
)

# 2. The Router decides which tool(s) to use based on the user's question
query_engine = RouterQueryEngine.from_defaults(
    query_engine_tools=[sql_tool, vector_tool],
)

# 3. The RAG engine retrieves data and generates a final answer
# response = query_engine.query("How did my sleep quality change after I started taking melatonin?")

5. Alerting Loop (Proactive)

This loop runs in the background, making the assistant proactive.

• Analysis & Alerting Engine: This is a scheduled service that continuously monitors the Long-Term Memory. It runs queries to look for predefined patterns or statistical anomalies (e.g., "Is the 7-day average resting heart rate more than two standard deviations above the 90-day average?").
• Proactive Alert to User: If the engine finds such a pattern, it triggers an event. This event generates a helpful notification that is pushed to the user's mobile device, alerting them to a potential health trend without them needing to ask first.