Dynamic Delirium Prediction in the Intensive Care Unit using Machine Learning on Electronic Health Records

Author:

Contreras Miguel1,Silva Brandon1,Shickel Benjamin2,Bandyopadhyay Sabyasachi1,Guan Ziyuan2,Ren Yuanfang2,Ozrazgat-Baslanti Tezcan2,Khezeli Kia1,Bihorac Azra2,Rashidi Parisa1

Affiliation:

1. University of Florida,Department of Biomedical Engineering,Gainesville,FL,USA

2. University of Florida,Department of Medicine,Gainesville,FL,USA

Funder

University of Florida

Publisher

IEEE

Reference31 articles.

1. A deep learning model for real-time mortality prediction in critically ill children

2. An attention based deep learning model of clinical events in the intensive care unit

3. Development and Validation of an Electronic Health Record–Based Machine Learning Model to Estimate Delirium Risk in Newly Hospitalized Patients Without Known Cognitive Impairment

4. Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data;davoudi;Proc IEEE 7th Int Symp Bioinf Bioeng,2017

5. The eICU Collaborative Research Database, a freely available multi-center database for critical care research;pollard;Data Science Journal,2018

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