3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data

By Yuan Luo, Peter Szolovits, A. Dighe, J. Baron. 2017

97 citations recorded by the source corpus.

Objective A key challenge in clinical data mining is that most clinical datasets contain missing data. Since many commonly used machine learning algorithms require complete datasets (no missing data), clinical analytic approaches often entail an imputation procedure to "fill in" missing data. However, although most clinical datasets contain a temporal component, most commonly used imputation methods do not adequately accommodate longitudinal time-based data. We sought to develop a new imputation algorithm, 3-dimensional multiple imputation with chained equations (3D-MICE), that can perform accurate imputation of missing clinical time series data. Methods We extracted clinical laboratory test results for 13 commonly measured analytes (clinical laboratory tests). We imputed missing test results for the 13 analytes using 3 imputation methods: multiple imputation with chained equations (MICE), Gaussian process (GP), and 3D-MICE. 3D-MICE utilizes both MICE and GP imputation to integrate cross-sectional and longitudinal information. To evaluate imputation method performance, we randomly masked selected test results and imputed these masked results alongside results missing from our original data. We compared predicted results to measured results for masked data points. Results 3D-MICE performed significantly better than MICE and GP-based imputation in a composite of all 13 analytes, predicting missing results with a normalized root-mean-square error of 0.342, compared to 0.373 for MICE alone and 0.358 for GP alone. Conclusions 3D-MICE offers a novel and practical approach to imputing clinical laboratory time series data. 3D-MICE may provide an additional tool for use as a foundation in clinical predictive analytics and intelligent clinical decision support.

Subfields: Epidemiology, Physiology, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

1. Yuan Luo
2. Tristan Naumann

In Statistics and Probability

1. Zeeshan Ahmed
2. Yuan Luo
3. Khalid Mohamed
4. Xinqi Dong
5. Saman Zeeshan
6. Tristan Naumann

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “Copmared to logistic regression, the Naive Bayes classifier is capable of learning even in the presence of some missing values and relies less on missing data imputation [94, 95].”
• Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “In past decades, various systems have been developed in both commercial and academic sectors (11,24,25,26,27,28,29,30) for this purpose.”