ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Interpretable Topic Features for Post-ICU Mortality Prediction.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Analyzing Information Disparities across Modalities in Mortality Prediction.medRxiv : the preprint server for health sciences · 2025Article
- Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists.Critical care explorations · 2021Article
- Combining structured and unstructured data for predictive models: a deep learning approach.BMC medical informatics and decision making · 2020Article
- A Hybrid Normalization Method for Medical Concepts in Clinical Narrative using Semantic Matching.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Recurrent Neural Networks for Multivariate Time Series with Missing Values.Scientific reports · 2018Article
- What's in a Note? Unpacking Predictive Value in Clinical Note Representations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
28269879PMC5333300What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.