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Helios

Overview

Helios is an advanced healthcare medical AI system designed to monitor patient vital signs and other health metrics, detect anomalies, and recommend medical actions such as assessing, diagnosing, and scheduling follow-ups. Inspired by the AI mechanics of Oden, Helios leverages state-of-the-art machine learning and deep learning techniques to provide real-time insights and recommendations to healthcare professionals.

Key Features

1. Real-time Monitoring: Continuously monitors patient vital signs and health metrics.
2. Anomaly Detection: Detects anomalies in patient data and raises alerts.
3. Medical Recommendations: Provides actionable medical recommendations based on patient data.
4. Prometheus Integration: Integrates with Prometheus for real-time monitoring and alerting.
5. Enhanced Logging: Utilizes an advanced logging system to track events and metrics.
6. Machine Learning Models: Employs transformer models, LSTM networks, and other deep learning techniques for predictive analytics.
7. Holographic Memory: Uses holographic memory for enhanced pattern recognition and decision-making.
8. Security and Compliance: Ensures high standards of security and compliance with healthcare regulations.

Components

Prometheus Monitoring Setup

Helios uses Prometheus to monitor hospital or ward metrics:

• Patient Count: Number of patients being monitored.
• Average Risk: Average risk score across patients.
• Alert Level: Overall alert level for the patient cohort.

Configuration Management

Helios is highly configurable, with settings for logging, risk thresholds, memory size, email notifications, and more, all defined in the HeliosConfig class.

Thought Templates

Helios communicates in human-like medical language using predefined thought templates for various scenarios such as risk detection, action selection, status updates, and more.

Machine Learning Models

Helios employs several advanced machine learning models, including:

• Transformer Encoder: For processing and understanding sequential data.
• LSTM Networks: For long-term dependency tracking in patient metrics.
• Clustering Models: For identifying patterns and anomalies in patient data.

Logging System

The logging system is designed to capture detailed information about the system's operations, including structured logs for easy parsing and analysis.

State Management

Helios manages its state using the SystemState class, which tracks active status, high-risk factors, effectiveness metrics, and more.

Medical Brain

The MedicalBrain class is the core of Helios, responsible for predicting patient risk scores, training models, and making decisions based on patient data.

Medical Policy

The MedicalPolicy class implements a Dueling DQN with Prioritized Experience Replay for decision-making, allowing Helios to learn and adapt over time.

Dynamics Model

The HeliosDynamics class simulates the effects of medical actions on patient metrics, helping to predict future states and outcomes.

Reward Estimation

Helios estimates the reward for medical actions based on their impact on patient metrics and risk scores.

Predictive Model

The PredictiveModel class uses bidirectional LSTM networks and attention mechanisms to predict future patient metrics.

Effectiveness Manager

The EffectivenessManager class tracks the effectiveness of medical actions, allowing Helios to learn from its experiences and improve over time.

Chain of Thought Module

The HeliosCoT class provides a chain of thought reasoning for medical decision-making, integrating risk assessment, action selection, response evaluation, and proactive planning.

How to Use Helios

Prerequisites

• Python 3.7 or higher
• Required Python packages (listed in requirements.txt)

Setup

1. Clone the repository:

   git clone https://github.com/SuperCaleb/Helios.git
   cd Helios

2. Install the required packages:

   pip install -r requirements.txt

3. Configure environment variables for email notifications and risk intel sources:

   export PH_API_KEY="your_public_health_api_key"
   export HELIOS_EMAIL_PASSWORD="your_email_password"
   export SLACK_WEBHOOK="your_slack_webhook_url"

4. Run Helios:

   python helios.py

Usage

Helios operates autonomously to monitor patient data, detect anomalies, and provide medical recommendations. It logs events and metrics, and integrates with Prometheus for real-time monitoring.

Commands

• status: Get the current status of patient data.
• explain_last_action: Get an explanation of the last action taken by Helios.

Monitoring and Alerts

Helios uses Prometheus for monitoring and can send alerts via email and Slack. Ensure that Prometheus is set up and configured to monitor the metrics exposed by Helios.

Value Proposition

Helios offers significant value to healthcare professionals and institutions by:

1. Enhancing Patient Care: Provides real-time insights and recommendations, enabling proactive and informed medical decisions.
2. Improving Efficiency: Automates the monitoring and analysis of patient data, freeing up valuable time for healthcare professionals.
3. Reducing Risks: Detects anomalies early and raises alerts, helping to prevent adverse events and improve patient outcomes.
4. Leveraging Advanced AI: Uses state-of-the-art machine learning and deep learning techniques to deliver accurate and reliable predictions.
5. Ensuring Compliance: Maintains high standards of security and compliance with healthcare regulations.

In summary, Helios is a powerful tool that enhances patient care, improves operational efficiency, reduces risks, and leverages advanced AI to support healthcare professionals in delivering the best possible outcomes for their patients.