Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study

Pablo Ferri Borreda, Nekane Romero-Garcia, Rafael Badenes, David Lora-Pablos, Teresa García Morales, Agustín Gómez De La Camara, Juan M. García-Gómez, Carlos Sáez. Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study. Computer Methods and Programs in Biomedicine, 242:107803, December 2023. [doi]

@article{BorredaRBLMCGS23,
  title = {Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study},
  author = {Pablo Ferri Borreda and Nekane Romero-Garcia and Rafael Badenes and David Lora-Pablos and Teresa García Morales and Agustín Gómez De La Camara and Juan M. García-Gómez and Carlos Sáez},
  year = {2023},
  month = {December},
  doi = {10.1016/j.cmpb.2023.107803},
  url = {https://doi.org/10.1016/j.cmpb.2023.107803},
  researchr = {https://researchr.org/publication/BorredaRBLMCGS23},
  cites = {0},
  citedby = {0},
  journal = {Computer Methods and Programs in Biomedicine},
  volume = {242},
  pages = {107803},
}