Interpretable Topic Features for Post-ICU Mortality Prediction.
Year: 2016
Type: article
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.
Source: PubMed Central
Authors: Yen-Fu Luo, Anna Rumshisky
Institution: University of Massachusetts Lowell
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2621052381