Summary of A Multi-cohort Study on Prediction Of Acute Brain Dysfunction States Using Selective State Space Models, by Brandon Silva et al.
A multi-cohort study on prediction of acute brain dysfunction states using selective state space models
by Brandon Silva, Miguel Contreras, Sabyasachi Bandyopadhyay, Yuanfang Ren, Ziyuan Guan, Jeremy Balch, Kia Khezeli, Tezcan Ozrazgat Baslanti, Ben Shickel, Azra Bihorac, Parisa Rashidi
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research aims to develop automated methods for predicting acute brain dysfunction (ABD) in intensive care unit (ICU) patients using Electronic Health Records (EHR) data. The current diagnostic methods rely on infrequent clinical observations, which can only determine a patient’s ABD status after onset. The proposed models predict delirium, coma, and mortality for 12-hour intervals throughout an ICU stay and validate the results on two public datasets. Additionally, the study introduces the concept of dynamically predicting critical transitions from non-ABD to ABD and between different ABD states in real-time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is trying to help doctors predict when patients are having problems with their brains while they’re in the hospital. Right now, doctors can only tell if a patient’s brain is having trouble after it happens. This study uses computer data from hospitals to try to figure out what’s going on with a patient’s brain before something bad happens. They want to make sure that doctors have better tools to help them take care of patients who are very sick. |
