# Modern Views of Machine Learning for Precision Psychiatry

arXiv: https://arxiv.org/abs/2204.01607 · HTML: https://mlsanity.com/paper/2204.01607/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 438 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 28 of 28)

- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017)
- [Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease](https://mlsanity.com/paper/1806.01738/) (2018)
- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [VIGAN: Missing View Imputation with Generative Adversarial Networks](https://mlsanity.com/paper/1708.06724/) (2017)
- [A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis](https://mlsanity.com/paper/2005.04573/) (2020)
- [Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media](https://mlsanity.com/paper/1710.05429/) (2017)
- [Deep Learning for Depression Recognition with Audiovisual Cues: A Review](https://mlsanity.com/paper/2106.00610/) (2021)
- [What can Data-Centric AI Learn from Data and ML Engineering?](https://mlsanity.com/paper/2112.06439/) (2021)
- [Understanding and Measuring Psychological Stress using Social Media](https://mlsanity.com/paper/1811.07430/) (2018)
- [Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data](https://mlsanity.com/paper/1508.07416/) (2015)
- [Automated Quality Assessment of Cognitive Behavioral Therapy Sessions Through Highly Contextualized Language Representations](https://mlsanity.com/paper/2102.11573/) (2021)
- [Expert-Augmented Machine Learning](https://mlsanity.com/paper/1903.09731/) (2019)
- [Deep Representation Learning For Multimodal Brain Networks](https://mlsanity.com/paper/2007.09777/) (2020)
- [Ensemble learning with 3D convolutional neural networks for connectome-based prediction](https://mlsanity.com/paper/1809.06219/) (2018)
- [Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI](https://mlsanity.com/paper/1910.06950/) (2019)
- [Multiplex Graph Networks for Multimodal Brain Network Analysis](https://mlsanity.com/paper/2108.00158/) (2021)
- [Artificial Intelligence and the Future of Psychiatry: Insights from a Global Physician Survey](https://mlsanity.com/paper/1907.12386/) (2019)
- [Generation of Synthetic Electronic Health Records Using a Federated GAN](https://mlsanity.com/paper/2109.02543/) (2021)
- [User Driven Model Adjustment via Boolean Rule Explanations](https://mlsanity.com/paper/2203.15071/) (2022)
- [Control of Dynamics in Brain Networks](https://mlsanity.com/paper/1701.01531/) (2017)
- [Models of communication and control for brain networks: distinctions, convergence, and future outlook](https://mlsanity.com/paper/2002.07029/) (2020)
- [Fusing multimodal neuroimaging data with a variational autoencoder](https://mlsanity.com/paper/2105.01128/) (2021)
- [Relations between large scale brain connectivity and effects of regional stimulation depend on collective dynamical state](https://mlsanity.com/paper/2002.00094/) (2020)

## Built on by (most cited first, 5 of 5)

- [A Comprehensive Evaluation of Large Language Models on Mental Illnesses](https://mlsanity.com/paper/2409.15687/) (2024)
- [Large-scale Foundation Models and Generative AI for BigData Neuroscience](https://mlsanity.com/paper/2310.18377/) (2023)
- [The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review](https://mlsanity.com/paper/2311.06633/) (2023)
- [Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review](https://mlsanity.com/paper/2409.07493/) (2024)
- [Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry](https://mlsanity.com/paper/2411.05856/) (2024)

## Abstract (arXiv)

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.