ClinicalTrials.gov
public full text
Public Domain
AI Platform for Fatigue and Depression Detection
This study evaluates the accuracy of the Okaya AI platform in detecting fatigue and depression in cardiology patients, comparing its assessments to PHQ-9 and Fatigue Assessment Scale scores.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Digital Health Intervention Based on Artificial Intelligence to Support the Personalized Recovery of Long COVID Patients Affected by Fatigue (AIDA)#1112098
- Disentangling Fatigue from Depression among Survivors of Severe COVID-19#223964
- COMMIT Depression Trial Nepal#1100673
- Effectiveness of Tuina Therapy for Patients With Chronic Fatigue Syndrome: Protocol for a Multicenter, Randomized, Open-Label, Assessor-Blinded, Parallel-Group Clinical Trial.#636165
- Effects of a Rehabilitation Programme Focused on Energy Management in People With Myalgic Encephalomyelitis / Chronic Fatigue Syndrome#1104334
Record · ID 1102383
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive