Handling Missing Values in Healthcare Settings

[Link] [Slides]

In statistical modeling, it is common to encounter missing values, particularly in healthcare situations where vital sign measurements may be sporadic. This can lead to difficulties in accurately evaluating a patient’s health and making informed decisions about their treatment. One possible solution is to use imputation methods to fill in the missing values and enable counterfactual predictions in healthcare settings. To address this issue, we have conducted a study examining existing approaches to missing value imputation in healthcare settings, with a specific focus on time series data. We provide an overview of both classical (e.g. MICE, MissForest, Gaussian Processes), and deep learning-based (e.g. Recurrent Neural Networks, Generative Adversarial Networks, Autoencoders) imputation methods, assess their strengths and limitations within the context of time series data, and highlight potential areas for further investigation.

@article{septiandri2023missing,
  title   = {Handling Missing Values in Healthcare Settings},
  author  = {Septiandri, Ali Akbar and Jendoubi, Takoua and De la O, Alejandro Díaz},
  year    = 2023,
  month   = {September},
  url     = {https://aliakbars.id/posts/2023/09/missing-values}
}