Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments

Area of Science:

• Psychiatry
• Machine Learning
• Precision Medicine

Background:

• Mental disorders involve unobserved latent states inferred from observable data.
• Patient heterogeneity necessitates individualized treatment strategies for precision medicine.

Purpose of the Study:

• To develop an integrated learning framework for simultaneous inference of latent mental states and personalized treatment recommendation.
• To address heterogeneity in mental disorder diagnosis and treatment effects.

Main Methods:

• The framework integrates measurement theory from psychiatry to model latent states from multi-domain data.
• A multi-layer neural network is employed to capture complex treatment effect heterogeneity.
• Incorporates multivariate outcomes and biological measures while preserving latent state structure.

Main Results:

• The proposed framework effectively learns patients' underlying mental states and recommends optimal treatments.
• Learned treatment policies outperform alternative methods in handling heterogeneous treatment effects.
• Demonstrated broad utility and improved patient outcomes on multiple domains in simulated and real-world data.

Conclusions:

• The integrated learning framework offers a promising approach for precision medicine in mental health.
• Simultaneous learning of latent states and treatment policies enhances individualized care.
• The method shows potential for optimizing treatment strategies and improving patient outcomes in psychiatry.