Real-time mortality prediction in the Intensive Care Unit
Real-time prediction of mortality for intensive care unit patients has the potential to provide physicians with a simple and easily interpretable synthesis of patient acuity. Here we extract data from a random time during each patient’s ICU stay. We believe this sampling scheme allows for the applic...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
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American Medical Informatics Association
2019
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| Online Access: | https://hdl.handle.net/1721.1/123113 |
| _version_ | 1811068219014774784 |
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| author | Johnson, Alistair Edward William Mark, Roger G |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Johnson, Alistair Edward William Mark, Roger G |
| author_sort | Johnson, Alistair Edward William |
| collection | MIT |
| description | Real-time prediction of mortality for intensive care unit patients has the potential to provide physicians with a simple and easily interpretable synthesis of patient acuity. Here we extract data from a random time during each patient’s ICU stay. We believe this sampling scheme allows for the application of the model(s) across a future patient’s entire ICU stay. The AUROC of a Gradient Boosting model was high (AUROC=0.920), even though no information about diagnosis or comorbid burden was utilized. We also compare models using data from the first 24 hours of a patient’s stay against published severity of illness scores, and find the Gradient Boosting model greatly outperformed the frequently used Simplified Acute Physiology Score II (AUROC = 0.927 vs. 0.809). We nuance this performance with comparison to the literature, provide our interpretation, and discuss potential avenues for improvement. |
| first_indexed | 2024-09-23T07:53:06Z |
| format | Article |
| id | mit-1721.1/123113 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T07:53:06Z |
| publishDate | 2019 |
| publisher | American Medical Informatics Association |
| record_format | dspace |
| spelling | mit-1721.1/1231132022-09-23T09:24:15Z Real-time mortality prediction in the Intensive Care Unit Johnson, Alistair Edward William Mark, Roger G Massachusetts Institute of Technology. Institute for Medical Engineering & Science Real-time prediction of mortality for intensive care unit patients has the potential to provide physicians with a simple and easily interpretable synthesis of patient acuity. Here we extract data from a random time during each patient’s ICU stay. We believe this sampling scheme allows for the application of the model(s) across a future patient’s entire ICU stay. The AUROC of a Gradient Boosting model was high (AUROC=0.920), even though no information about diagnosis or comorbid burden was utilized. We also compare models using data from the first 24 hours of a patient’s stay against published severity of illness scores, and find the Gradient Boosting model greatly outperformed the frequently used Simplified Acute Physiology Score II (AUROC = 0.927 vs. 0.809). We nuance this performance with comparison to the literature, provide our interpretation, and discuss potential avenues for improvement. 2019-12-04T22:41:19Z 2019-12-04T22:41:19Z 2018-04 Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/123113 Johnson, Alistair E. W. and Roger G. Mark. "Real-time mortality prediction in the Intensive Care Unit." AMIA Annual Symposium Proceedings (2017): 994-1003 © 2017 AMIA https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977709/ AMIA Annual Symposium Proceedings Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Medical Informatics Association Prof. Mark via Courtney Crummett |
| spellingShingle | Johnson, Alistair Edward William Mark, Roger G Real-time mortality prediction in the Intensive Care Unit |
| title | Real-time mortality prediction in the Intensive Care Unit |
| title_full | Real-time mortality prediction in the Intensive Care Unit |
| title_fullStr | Real-time mortality prediction in the Intensive Care Unit |
| title_full_unstemmed | Real-time mortality prediction in the Intensive Care Unit |
| title_short | Real-time mortality prediction in the Intensive Care Unit |
| title_sort | real time mortality prediction in the intensive care unit |
| url | https://hdl.handle.net/1721.1/123113 |
| work_keys_str_mv | AT johnsonalistairedwardwilliam realtimemortalitypredictionintheintensivecareunit AT markrogerg realtimemortalitypredictionintheintensivecareunit |