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 perform quantum hardware
experiments by applying the quantum circuits for DPP sampling, 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 demanding high
precision, such as in pharmaceutical drug trials where our approach can provide
higher confidence in the predictions made.
Skander Kazdaghli Iordanis Kerenidis Jens Kieckbusch Philip Teare