Improved clinical data imputation via classical and quantum determinantal point processes

Understand

Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance.

• Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream classification.
• Here we propose novel imputation methods based on determinantal point processes that enhance popular techniques such as the Multivariate Imputation by Chained Equations (MICE) and MissForest.
• Their advantages are two-fold: improving the quality of the imputed data demonstrated by increased accuracy of the downstream classification; and providing deterministic and reliable imputations that remove the variance from the classification results.