Using machine learning to increase treatment efficacy in mental health.

Something unique to every machine learning company is the precise nature of their hyperparameter optimization and goals of their model. We will optimize aifred with the help of a distributed network of domain experts in psychiatry -- a collaboration unique to aifred health. We are implementing attention networks responsible for removing the “black-box” nature of neural networks. As well, we are analyzing the quality of model predictions, allowing both for greater interpretability of model decisions and the generation of new basic research questions, which are going to be unique to the data-set and optimization techniques we develop in-house. By training aifred on reliable datasets, we are able to ensure quality input to our model. De-identified patient outcomes will feed back into our neural networks to continuously improve aifred’s predictive power. Feature engineering is an important part of determining which inputs go into a network and varies how it’s done for every team- once again, this will be undertaken with the support of diverse group of experts we are recruiting.

Fundamental

Our research team is conducting a series of systematic literature reviews to curate predictors of treatment response and side effect burden in depression. We are evaluating the state of precision psychiatry in domains including genetics, endocrinology, immunology, metabolic biochemistry, and neuroimaging, as well as examining the feasibility of including biomarker testing in routine clinical practice. The results of these reviews will serve to validate our model and inform the input feature space by integrating these multimodal biomarkers along with sociodemographic and clinical factors.

Clinical

Clinical research is focused on validating our model in controlled and real-world conditions. We are designing three kinds of research trials indicated below. Safety is critical, so our clinical team, which includes two physicians, will be making sure to review our model’s predictions and ensure hard-coded safety features so that model treatment recommendations are safe. We are blazing the trail when it comes to clinical validation of deep-learning based clinical decision aids, and as such are investing heavily in the development of ethical principles to guide development and testing. In fact, ethical development is so important to us that we have created our own ethical framework, known as Meticulous Transparency, to guide our work. We also never store personally identifiable patient information, to protect patient privacy.