Multimodal Biomarkers and Digital Phenotyping in Behavioral Health

Who this is for

Research teams, digital health companies, and translational groups exploring multimodal biomarkers, passive or active digital measures, and clinically meaningful behavioral health phenotypes.

Problems Keystone helps solve

• Distinguishing useful clinical signal from noisy multimodal data collection.
• Defining phenotypes and endpoints before modeling begins.
• Aligning digital measures with symptoms, function, treatment response, or risk.
• Designing biomarker strategies that remain interpretable to clinical and research audiences.

Example questions clients bring

• Which data streams are likely to add meaningful information rather than complexity?
• How should wearable, app, imaging, survey, or clinical variables be combined?
• What endpoint should a biomarker predict, explain, stratify, or monitor?
• How should early-stage biomarker work be communicated without overclaiming?

Methods and capabilities

• Multimodal data inventory and feature framing.
• Phenotype, endpoint, and validation-plan review.
• Digital phenotyping concept design.
• Interpretability and clinical-translation critique.

Typical deliverables

• Biomarker strategy memo.
• Digital phenotyping feature map.
• Endpoint and validation plan.
• Multimodal data-readiness review.
• Research communication brief.

Relevant research foundation

This work builds on Keystone’s focus in multimodal behavioral health data, neuroimaging, clinical phenotyping, computational psychiatry, and translational biomarker questions.

What Keystone does not do

Keystone does not claim that exploratory signals are validated biomarkers, build consumer surveillance products, or support data collection strategies that lack appropriate consent and governance.