# Precision psychiatry: predicting predictability

arXiv: https://arxiv.org/abs/2306.12462 · HTML: https://mlsanity.com/paper/2306.12462/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 90 references that are in the map.
- First posted on arXiv 21 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [A Tutorial on Regularized Partial Correlation Networks](https://mlsanity.com/paper/1607.01367/) (2016)

## Abstract (arXiv)

Precision psychiatry is an ermerging field that aims to provide individualized approaches to mental health care. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.