Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians.
Sazzli KasimPutri Nur Fatin Amir RudinSorayya MalekFirdaus AzizWan Azman Wan AhmadKhairul Shafiq IbrahimMuhammad Hanis Muhmad HamidiRaja Ezman Raja ShariffAlan Yean Yip FongCheen SongPublished in: PloS one (2024)
In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.
Keyphrases
- st elevation myocardial infarction
- percutaneous coronary intervention
- big data
- cardiovascular events
- machine learning
- coronary artery disease
- liver failure
- risk factors
- acute coronary syndrome
- artificial intelligence
- cardiovascular disease
- current status
- atrial fibrillation
- aortic dissection
- bioinformatics analysis