Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department
The MI3 algorithm was accurate in risk stratifying ED patients for myocardial infarction. The 99th percentile URL cut-off was the least accurate in ruling in and out myocardial infarction compared to the other strategies.
Authors
- Ziwei Lin (ORCID: https://orcid.org/0000-0002-1361-4031)
- Cheryl Shumin Kow (ORCID: https://orcid.org/0000-0002-0809-0771)
- Mark Richards (ORCID: https://orcid.org/0000-0002-2023-8177)
- Tar Choon Aw (ORCID: https://orcid.org/0000-0002-7814-8836)
- Laurel Jackson (ORCID: https://orcid.org/0000-0002-0491-9079)
- Siang Jin Terrance Chua (ORCID: https://orcid.org/0000-0003-1797-6790)
- Gillian Murtagh (ORCID: https://orcid.org/0009-0001-4900-3080)
- Swee Han Lim (ORCID: https://orcid.org/0000-0003-2563-749X)
Institutions
- Singapore General Hospital (SG)
- Changi General Hospital (SG)
- Abbott Fund (US)
- National Heart Centre Singapore (SG)
- Sengkang General Hospital (SG)
Publication Details
- Journal
- Annals of the Academy of Medicine Singapore
- Published
- 2025-04-17
- DOI
- https://doi.org/10.47102/annals-acadmedsg.2024232
- Citations
- 3
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 4.16