2 research outputs found
The Missing Indicator Method: From Low to High Dimensions
Missing data is common in applied data science, particularly for tabular data
sets found in healthcare, social sciences, and natural sciences. Most
supervised learning methods only work on complete data, thus requiring
preprocessing such as missing value imputation to work on incomplete data sets.
However, imputation alone does not encode useful information about the missing
values themselves. For data sets with informative missing patterns, the Missing
Indicator Method (MIM), which adds indicator variables to indicate the missing
pattern, can be used in conjunction with imputation to improve model
performance. While commonly used in data science, MIM is surprisingly
understudied from an empirical and especially theoretical perspective. In this
paper, we show empirically and theoretically that MIM improves performance for
informative missing values, and we prove that MIM does not hurt linear models
asymptotically for uninformative missing values. Additionally, we find that for
high-dimensional data sets with many uninformative indicators, MIM can induce
model overfitting and thus test performance. To address this issue, we
introduce Selective MIM (SMIM), a novel MIM extension that adds missing
indicators only for features that have informative missing patterns. We show
empirically that SMIM performs at least as well as MIM in general, and improves
MIM for high-dimensional data. Lastly, to demonstrate the utility of MIM on
real-world data science tasks, we demonstrate the effectiveness of MIM and SMIM
on clinical tasks generated from the MIMIC-III database of electronic health
records
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Most pregnancies and births result in a good outcome, but complications are
not uncommon and when they do occur, they can be associated with serious
implications for mothers and babies. Predictive modeling has the potential to
improve outcomes through better understanding of risk factors, heightened
surveillance, and more timely and appropriate interventions, thereby helping
obstetricians deliver better care. For three types of complications we identify
and study the most important risk factors using Explainable Boosting Machine
(EBM), a glass box model, in order to gain intelligibility: (i) Severe Maternal
Morbidity (SMM), (ii) shoulder dystocia, and (iii) preterm preeclampsia. While
using the interpretability of EBM's to reveal surprising insights into the
features contributing to risk, our experiments show EBMs match the accuracy of
other black-box ML methods such as deep neural nets and random forests.Comment: DSHealth at SIGKDD 2022, 5 pages, 3 figure