Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment
Abstract The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration. This paper presents a machine learning-d...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-15877-1 |
| _version_ | 1828541251498016768 |
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| author | Aida Brankovic Hamed Hassanzadeh Norm Good Kay Mann Sankalp Khanna Ahmad Abdel-Hafez David Cook |
| author_facet | Aida Brankovic Hamed Hassanzadeh Norm Good Kay Mann Sankalp Khanna Ahmad Abdel-Hafez David Cook |
| author_sort | Aida Brankovic |
| collection | DOAJ |
| description | Abstract The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration. This paper presents a machine learning-driven tool developed using real-time EMR data for identifying patients at high risk of reaching critical conditions that may demand immediate interventions. This tool provides a pre-emptive solution that can help busy clinicians to prioritize their efforts while evaluating the individual patient risk of deterioration. The tool also provides visualized explanation of the main contributing factors to its decisions, which can guide the choice of intervention. When applied to a test cohort of 18,648 patient records, the tool achieved 100% sensitivity for prediction windows 2–8 h in advance for patients that were identified at 95%, 85% and 70% risk of deterioration. |
| first_indexed | 2024-12-12T01:38:15Z |
| format | Article |
| id | doaj.art-ef75fa1e835c41cf9eed55bb1aa35b16 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-12T01:38:15Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ef75fa1e835c41cf9eed55bb1aa35b162022-12-22T00:42:48ZengNature PortfolioScientific Reports2045-23222022-07-0112111010.1038/s41598-022-15877-1Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatmentAida Brankovic0Hamed Hassanzadeh1Norm Good2Kay Mann3Sankalp Khanna4Ahmad Abdel-Hafez5David Cook6CSIRO Australian e-Health Research CentreCSIRO Australian e-Health Research CentreCSIRO Australian e-Health Research CentreCSIRO Australian e-Health Research CentreCSIRO Australian e-Health Research CentreMetro South HealthIntensive Care Unit, Princess Alexandra HospitalAbstract The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration. This paper presents a machine learning-driven tool developed using real-time EMR data for identifying patients at high risk of reaching critical conditions that may demand immediate interventions. This tool provides a pre-emptive solution that can help busy clinicians to prioritize their efforts while evaluating the individual patient risk of deterioration. The tool also provides visualized explanation of the main contributing factors to its decisions, which can guide the choice of intervention. When applied to a test cohort of 18,648 patient records, the tool achieved 100% sensitivity for prediction windows 2–8 h in advance for patients that were identified at 95%, 85% and 70% risk of deterioration.https://doi.org/10.1038/s41598-022-15877-1 |
| spellingShingle | Aida Brankovic Hamed Hassanzadeh Norm Good Kay Mann Sankalp Khanna Ahmad Abdel-Hafez David Cook Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment Scientific Reports |
| title | Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment |
| title_full | Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment |
| title_fullStr | Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment |
| title_full_unstemmed | Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment |
| title_short | Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment |
| title_sort | explainable machine learning for real time deterioration alert prediction to guide pre emptive treatment |
| url | https://doi.org/10.1038/s41598-022-15877-1 |
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