18 research outputs found
Improved clinical data imputation via classical and quantum determinantal point processes
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. We experimentally
demonstrate the advantages of our methods by performing extensive imputations
on synthetic and real clinical data. We also develop quantum circuits for
implementing determinantal point processes, since such quantum algorithms
provide a computational advantage with respect to classical ones. We
demonstrate competitive results with up to ten qubits for small-scale
imputation tasks on a state-of-the-art IBM quantum processor. Our classical and
quantum methods improve the effectiveness and robustness of clinical data
prediction modeling by providing better and more reliable data imputations.
These improvements can add significant value in settings where where high
precision is critical, such as in pharmaceutical drug trials where our approach
can provide higher confidence in the predictions made