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.
Publications

@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" }