Interpretable Topic Features for Post-ICU Mortality Prediction

Electronic health records provide valuable resources for understanding the correlation between various diseases and mortality. The analysis of post-discharge mortality is critical for healthcare professionals to follow up potential causes of death after a patient is discharged from the hospital and give prompt treatment. Moreover, it may reduce the cost derived from readmissions and improve the quality of healthcare.

Our work focused on post-discharge ICU mortality prediction. In addition to features derived from physiological measurements, we incorporated ICD-9-CM hierarchy into Bayesian topic model learning and extracted topic features from medical notes. We achieved highest AUCs of 0.835 and 0.829 for 30-day and 6-month post-discharge mortality prediction using baseline and topic proportions derived from Labeled-LDA. Moreover, our work emphasized the interpretability of topic features derived from topic model which may facilitates the understanding and investigation of the complexity between mortality and diseases.


Y. Luo A. Rumshisky Interpretable Topic Features for Post-ICU Mortality Prediction. AMIA 2016, American Medical Informatics Association Annual Symposium. 2016

@inproceedings{luo_interpretable_2016, author = "Luo, Yen-Fu and Rumshisky, Anna", title = "Interpretable {Topic} {Features} for {Post}-{ICU} {Mortality} {Prediction}", booktitle = "{AMIA} 2016, {American} {Medical} {Informatics} {Association} {Annual} {Symposium}, {Chicago}, {IL}, {USA}, {November} 12-16, 2016", year = "2016" }