Machine learning and LACE index for predicting 30-day readmissions after heart failure hospitalization in elderly patients
Machine learning and LACE index for predicting 30-day readmissions after heart failure hospitalization in elderly patients (Springer).
Machine learning (ML) techniques may improve readmission prediction performance in heart failure (HF) patients. This study aimed to assess the ability of ML algorithms to predict unplanned all-cause 30-day readmissions in HF elderly patients, and to compare them with conventional LACE (Length of
hospitalization, Acuity, Comorbidities, Emergency department visits) index.
[...]
ML models performed better than the conventional LACE index
for predicting readmissions. ML models can be proposed as promising tools for
the identification of subjects at high risk of hospitalization in this clinical
setting,[...]
Quelle: Springer, 04.06.2022