Multiple Imputation for Biomedical Data using Monte Carlo Dropout Autoencoders
This is an incremental improvement for biomedical researchers dealing with missing data.
The paper tackled missing values in biomedical data by proposing a Monte Carlo dropout autoencoder method, which improved imputation error and predictive similarity.
Due to complex experimental settings, missing values are common in biomedical data. To handle this issue, many methods have been proposed, from ignoring incomplete instances to various data imputation approaches. With the recent rise of deep neural networks, the field of missing data imputation has oriented towards modelling of the data distribution. This paper presents an approach based on Monte Carlo dropout within (Variational) Autoencoders which offers not only very good adaptation to the distribution of the data but also allows generation of new data, adapted to each specific instance. The evaluation shows that the imputation error and predictive similarity can be improved with the proposed approach.