Danielle X Morales, Sara E Grineski, Timothy W Collins
Advancing understanding of discordant mentoring relationships in STEMM: A method and framework
Annals of the New York Academy of Sciences
electronic health records ehrs widely adopted recent years often include high proportion missing data create difficulties implementing machine learning tools personalized medicine completed datasets preferred number analysis methods successful imputation missing ehr data improve interpretation increase power predict health outcomes however use popular imputation methods mainly require scripting skills implemented using various packages syntax thus implementation full suite methods generally reach except experienced data scientists moreover imputation often considered separate exercise exploratory data analysis considered art data exploration process created new graphical tool imputehr based python base allows implementation range simple sophisticated eg gradientboosted treebased neural network data imputation approaches addition imputation tool enables data exploration informed decisionmaking well implementing machine learning prediction tools response data selected user although approach works missing data problem tool primarily motivated problems encountered ehr biomedical data illustrate tool using multiple real datasets providing performance measures imputation downstream predictive analysis