AI service — AI Medical · Mental health
Mental health text analysis
Detect and explain indicators of depression, stress and distress in text, with human review of every risk signal.
Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.
Text carries signal about mental state, and a research literature exists on extracting it. These models classify indicators across depression, stress and suicidal-ideation datasets, and the newer ones generate an explanation alongside the classification so a reviewer can judge it.
Text arrives from the source you nominate and comes back as scored indicators with explanations. Risk detection routes to a person — always. We will not build an unsupervised crisis or triage pathway on these models, because none has validated safety behaviour for suicidal ideation.
Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.
What we size for
We build the mental health text analysis pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.
Common use cases
- Research cohorts — Score a text corpus for mental health indicators in a study.
- Reviewer support — Surface passages for a clinician or safeguarding lead to assess.
- Service planning — Measure indicator prevalence across a population over time.
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