Chronic Disease Cohorts
Cross-source consensus on Chronic Disease Cohorts from 1 sources and 5 claims.
1 sources · 5 claims
Risks & contraindications
Background
Where it comes from
Highlighted claims
- COPD, HF, and T2DM were chosen because they are prevalent, frequently use hospital resources, and often coexist. — Synthetic data-augmented machine learning for 30-day readmission prediction in patients with chronic conditions: a retrospective real-world study
- COPD is described as affecting more than 16 million Americans and having 30-day readmission rates of 17-20%. — Synthetic data-augmented machine learning for 30-day readmission prediction in patients with chronic conditions: a retrospective real-world study
- HF is described as affecting about 6.2 million US adults and having 30-day readmission rates of 21-25%. — Synthetic data-augmented machine learning for 30-day readmission prediction in patients with chronic conditions: a retrospective real-world study
- T2DM is described as affecting more than 37 million Americans and having readmission rates of 14-22%. — Synthetic data-augmented machine learning for 30-day readmission prediction in patients with chronic conditions: a retrospective real-world study
- Overlap among these chronic diseases increases disease burden, healthcare use, and vulnerability to poor post-discharge outcomes. — Synthetic data-augmented machine learning for 30-day readmission prediction in patients with chronic conditions: a retrospective real-world study