Table 2_Development of a machine learning-based mortality prediction model for patients wi

Yi Zhang's study presents a machine learning model for predicting 31-day mortality among hospitalized patients with mental disorders and COVID-19. The model was developed and validated using retrospective data from 439 patients across 10 hospitals in Henan Province, China. The dataset was last updated on 2026-05-26.

Use Cases

• Developing mortality risk stratification tools based on routine clinical and laboratory variables mentioned in the description
• Training and comparing machine learning algorithms for imbalanced medical datasets based on the described oversampling approach
• Interpreting model predictions for clinical features like concomitant hormone therapy and lymphocyte count using SHAP analysis
• Validating prognostic models for patients with comorbidities, specifically mental disorders and COVID-19

Strengths

• Model validation on an independent test cohort of 131 patients
• The best-performing neural network model achieved an AUC of 0.911 in the test cohort
• Data sourced from a multicenter cohort of 10 hospitals

Limitations

• Dataset is very small at 27.9 KB, indicating limited scope
• Row count and column-level documentation are unknown, which may limit suitability assessment
• Data reflects a specific regional (Henan Province, China) and temporal bias inherent to the source

Provenance

Source

figshare, author Yi Zhang

Collection Method

Retrospectively collected from hospital records

Freshness

Last updated 2026-05-26 06:03:48; freshness should be verified

Geography

Henan Province, China