Data Sheet 1_Precision diagnosis model for treatment-resistant depression integrating seru

A single-center retrospective study of 93 major depressive disorder patients (53 treatment-resistant, 40 non-treatment-resistant). The dataset integrates serum untargeted metabolomics and clinical baseline data to build a dual-modal diagnostic model. It was authored by Weiquan Huang and last updated on 2026-05-21.

Use Cases

• Training diagnostic classifiers for treatment-resistant depression based on integrated serum metabolomics and clinical data.
• Identifying core metabolic biomarkers and clinical risk factors for depression subtypes.
• Validating machine learning algorithms (e.g., random forest) for multimodal medical data integration.
• Exploring metabolic pathway enrichment (e.g., lipid and amino acid metabolism) in psychiatric disorders.

Strengths

• Includes data from 93 patients with a clear diagnostic split (53 TRD, 40 non-TRD).
• Model achieved a high validation AUC of 0.911, indicating strong diagnostic performance.
• Identifies 3 core clinical risk factors and 8 core metabolic biomarkers.
• Uses five machine learning algorithms for model comparison and validation.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is from a single-center retrospective study, which may limit generalizability.

Provenance

Source

figshare, author Weiquan Huang.

Collection Method

Single-center retrospective study collecting serum untargeted metabolomics and clinical baseline data.

Freshness

Last updated 2026-05-21 05:42:25; freshness should be verified.