Initial THRML integration with basic probabilistic mortality modeling capabilities. Includes uncertainty quantification and demographic factor interactions.

This model integrates THRML (Thermodynamic HypergRaphical Model Library) with Morbid AI's mortality prediction capabilities, providing probabilistic predictions with uncertainty quantification.

Model Architecture

Type: thermal_energy_based_model Framework: THRML + JAX Version: 0.1.1

Thermal Features

Probabilistic graphical models for mortality factors
Block Gibbs sampling with demographic blocking
Energy-based life expectancy prediction
Confidence intervals for all predictions
Risk factor analysis and contribution scoring

Performance Metrics

baseline_accuracy: 0.8500
uncertainty_coverage: 0.9500
demographic_factors: 4.0000
sampling_efficiency: 0.9200

Usage

python

from thermal.models.life_expectancy import LifeExpectancyEBM
from thermal.graph.mortality_graph import MortalityRecord

# Load mortality data
mortality_data = [...]  # List of MortalityRecord objects

# Initialize thermal model
model = LifeExpectancyEBM(mortality_data)

# Make prediction with uncertainty quantification
prediction = model.predict_life_expectancy(
    age=45,
    country="USA", 
    sex=1,  # 1=male, 2=female, 3=both
    n_samples=1000,
    confidence_level=0.95
)

print(f"Life Expectancy: {prediction.mean_life_expectancy:.1f} years")
print(f"95% CI: {prediction.confidence_interval}")
print(f"Uncertainty: {prediction.uncertainty:.2f}")

Model Configuration

THRML Parameters

sampling:

default_samples: 1000
burn_in: 200
thinning: 2
blocking_strategy: demographic model:
energy_based: True
uncertainty_quantification: True
demographic_interactions: True performance:
gpu_acceleration: True
jax_backend: True
memory_efficient: True

Sampling Configuration

Block Gibbs Sampling: Two-color and demographic blocking strategies
Default Samples: 1000 MCMC samples
Burn-in: 200 steps
Thinning: Every 2nd sample

Training Data

The model is trained on mortality data including:

Countries: Global mortality statistics from major countries
Age Range: 0-100+ years
Time Period: 2010-2025
Demographic Factors: Age, sex, country, year

Limitations

Model performance depends on availability of demographic-specific training data
Uncertainty estimates are calibrated on historical data and may not capture unprecedented events
Requires THRML and JAX dependencies for optimal performance

Version History

v0.1.1 - 2025-10-29

Initial THRML integration framework
MortalityGraphBuilder for demographic interactions
LifeExpectancyEBM with uncertainty quantification
Block Gibbs sampling implementation
Basic API integration structure

Citation

@software{thermal_morbid_ai_0_1_1,
  title={Thermal-Enhanced Morbid AI Model},
  version={0.1.1},
  year={2025},
  url={https://huggingface.co/MorbidCorp/thermal-mortality-model}
}

License

MIT License - see LICENSE file for details.

Contact

For questions about this model, please open an issue in the Morbid AI repository.