title: Interpretable Topic Features for Post-ICU Mortality Prediction.
abstract: 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.
authors.affiliation: University of Massachusetts Lowell, Lowell, MA.
authors.forename: Yen-Fu
authors.initials: YF
authors.lastname: Luo
authors.affiliation: University of Massachusetts Lowell, Lowell, MA.
authors.forename: Anna
authors.initials: A
authors.lastname: Rumshisky
mesh_terms: D019540:Area Under Curve; D001499:Bayes Theorem; D002423:Cause of Death; D016208:Databases, Factual; D003661:Decision Support Techniques; D006801:Humans; D007362:Intensive Care Units; D038801:International Classification of Diseases; D009026:Mortality; D010351:Patient Discharge; D011379:Prognosis