Observable-Cue Mental Health Risk Prediction & Screening Toolbox
Overview
An AI toolbox for mental-health screening and risk prediction trained on observable cues collected from non-invasive sources, specifically daily diary/video recordings. It follows an end-to-end approach in which observable cues are extracted from recordings and then used by downstream classifiers to generate risk/screening outputs. The toolbox is multi-model and offers both binary and multi-class predictors. The toolbox uses a mix of classical and deep learning methods to extract and classify observable cues from non-invasive data sources such as diary/interview recordings (speech/audio with optional transcripts and video) and, where available, passive sensing streams (wearables/phones). For strong transparent baselines on engineered cue vectors, it includes gradient-boosted tree classifiers (e.g., GBDT/XGBoost/LightGBM). For temporal modeling of cue sequences, it provides GRU/LSTM-based classifiers that capture longitudinal dynamics in speech- and multimodal-derived features. For end-to-end multimodal learning, it includes multimodal transformer fusion models that combine audio, text, and/or video representations via attention-based fusion (late/intermediate fusion variants) to learn cross-modal correlations relevant to screening.
Outcome
A reusable set of trained screening/risk models (binary and multi-class) that take observable-cue inputs and output calibrated risk scores and class predictions suitable for early-warning, triage, and decision-support workflows, along with explainability outputs (e.g., key contributing behavioral markers) to support transparent integration into clinical decision-support systems and pilot validation.
Service Card
| Category | AI products |
| Domain | Digital Health, Mental Health Screening |
| Vertical | Health and Biotechnology |
| Target users | Healthcare Providers and Pilot Teams in Mental Health (Clinicians, Psychologists, Care Coordinators), Digital Health Solution Developers, Research Groups |
| Service type | Model (AI Toolbox with Multiple Models), Docker |
| Outputs | Trained screening/risk models with calibrated risk scores, class predictions, and explainability outputs. |
| Requirements | |
| Limitations | |
| Availability | Q4/2026 (initial), with extensions in Q2/2027 |
| Licensing | |
| Visual content | assets/service-visuals/observable-cue.png |
| Visual caption |