A deep learning model for real-time mortality prediction in critically ill children
Abstract Background The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of...
Main Authors: | Soo Yeon Kim, Saehoon Kim, Joongbum Cho, Young Suh Kim, In Suk Sol, Youngchul Sung, Inhyeok Cho, Minseop Park, Haerin Jang, Yoon Hee Kim, Kyung Won Kim, Myung Hyun Sohn |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2019-08-01
|
Series: | Critical Care |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s13054-019-2561-z |
Similar Items
-
Validation of Pediatric Index of Mortality 3 for Predicting Mortality among Patients Admitted to a Pediatric Intensive Care Unit
by: Jae Hwa Jung, et al.
Published: (2018-08-01) -
Serum Albumin as a Biomarker of Poor Prognosis in the Pediatric Patients in Intensive Care Unit
by: Young Suh Kim, et al.
Published: (2017-11-01) -
Oxygenation Index in the First 24 Hours after the Diagnosis of Acute Respiratory Distress Syndrome as a Surrogate Metric for Risk Stratification in Children
by: Soo Yeon Kim, et al.
Published: (2018-11-01) -
Predicting the Outcome of Critically Ill Children and Adolescents with Electroencephalography
by: Sangbo Lee, et al.
Published: (2019-03-01) -
Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit
by: Min Jung Kim, et al.
Published: (2017-05-01)