Interpretable and robust hospital readmission predictions from Electronic Health Records. Calero-Diaz, H., Hamad, R., Atallah, C., Casement, J., Canoy, D., Reynolds, N., Barnes, M., & Missier, P. In Procs. IEEE BigData, Sorrento, Italy, December, 2023. IEEE.
Interpretable and robust hospital readmission predictions from Electronic Health Records [link]Paper  abstract   bibtex   1 download  
—Rates of Hospital Readmission (HR), defined as unplanned readmission within 30 days of discharge, have been increasing over the years, and impose an economic burden on healthcare services worldwide. Despite recent research into predicting HR, few models provide sufficient discriminative ability. Three main drawbacks can be identified in the published literature: (i) imbalance in the target classes (readmitted or not), (ii) not including demographic and lifestyle predictors, and (iii) lack of interpretability of the models. In this work, we address these three points by evaluating class balancing techniques, performing a feature selection process including demographic and lifestyle features, and adding interpretability through a combination of SHapley Additive exPlanations (SHAP) and Accumulated Local Effects (ALE) post hoc methods. Our best classifier for this binary outcome achieves a UAC of 0.849 using a selection of 1296 features, extracted from patients’ Electronic Health Records (EHRs) and from their sociodemographics profiles. Using SHAP and ALE, we have established the importance of age, the number of long-term conditions, and the duration of the first admission as top predictors. In addition, we show through an ablation study that demographic and lifestyle features provide even better predictive capabilities than other features, suggesting their relevance toward HR
@inproceedings{calero-diaz_interpretable_2023,
	address = {Sorrento, Italy},
	title = {Interpretable and robust hospital readmission predictions from {Electronic} {Health} {Records}},
	abstract = {—Rates of Hospital Readmission (HR), defined as
unplanned readmission within 30 days of discharge, have been
increasing over the years, and impose an economic burden on
healthcare services worldwide. Despite recent research into predicting HR, few models provide sufficient discriminative ability.
Three main drawbacks can be identified in the published literature: (i) imbalance in the target classes (readmitted or not), (ii)
not including demographic and lifestyle predictors, and (iii) lack
of interpretability of the models. In this work, we address these
three points by evaluating class balancing techniques, performing
a feature selection process including demographic and lifestyle
features, and adding interpretability through a combination of
SHapley Additive exPlanations (SHAP) and Accumulated Local
Effects (ALE) post hoc methods. Our best classifier for this
binary outcome achieves a UAC of 0.849 using a selection of
1296 features, extracted from patients’ Electronic Health Records
(EHRs) and from their sociodemographics profiles. Using SHAP
and ALE, we have established the importance of age, the
number of long-term conditions, and the duration of the first
admission as top predictors. In addition, we show through an
ablation study that demographic and lifestyle features provide
even better predictive capabilities than other features, suggesting
their relevance toward HR},
	booktitle = {Procs. {IEEE} {BigData}},
	publisher = {IEEE},
	author = {Calero-Diaz, Hugo and Hamad, Rebeen and Atallah, Christian and Casement, John and Canoy, Dexter and Reynolds, Nick and Barnes, Michael and Missier, Paolo},
	month = dec,
	year = {2023},
	url={https://ieeexplore.ieee.org/document/10386820}
}
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