Prediction and Inference With Missing Data in Patient Alert Systems
| Autor: | Nicholas Chia, Santiago Romero-Brufau, Rickey E. Carter, John R. Bergquist, Curtis B. Storlie, Jeanne M. Huddleston, Terry M. Therneau |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Computer science business.industry 05 social sciences Inference Latent variable Missing data Machine learning computer.software_genre Statistics - Applications 01 natural sciences Dirichlet process 010104 statistics & probability Multivariate probit model 0502 economics and business Applications (stat.AP) In patient Artificial intelligence 0101 mathematics Statistics Probability and Uncertainty business computer 050205 econometrics |
| Zdroj: | Journal of the American Statistical Association. 115:32-46 |
| ISSN: | 1537-274X 0162-1459 |
| DOI: | 10.1080/01621459.2019.1604359 |
| Popis: | We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-ICU patients using ~200 variables (vitals, lab results, assessments, ...). There are several missing predictor values for most patients, which in the health sciences is the norm, rather than the exception. A Bayesian approach is presented that addresses many of the shortcomings to standard approaches to missing predictors: (i) treatment of the uncertainty due to imputation is straight-forward in the Bayesian paradigm, (ii) the predictor distribution is flexibly modeled as an infinite normal mixture with latent variables to explicitly account for discrete predictors (i.e., as in multivariate probit regression models), and (iii) certain missing not at random situations can be handled effectively by allowing the indicator of missingness into the predictor distribution only to inform the distribution of the missing variables. The proposed approach also has the benefit of providing a distribution for the prediction, including the uncertainty inherent in the imputation. Therefore, we can ask questions such as: is it possible this individual is at high risk but we are missing too much information to know for sure? How much would we reduce the uncertainty in our risk prediction by obtaining a particular missing value? This approach is applied to the BPR problem resulting in excellent predictive capability to identify deteriorating patients. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|