Spectrum bias in algorithms derived by artificial intelligence: a case study in detecting aortic stenosis using electrocardiograms.
Andrew S TsengMichal Shelly-CohenItzhak Z AttiaPeter A NoseworthyPaul A FriedmanJae K OhFrancisco Lopez-JimenezPublished in: European heart journal. Digital health (2021)
While the algorithm performed robustly in identifying severe AS, this study shows that limiting datasets to clearly positive or negative labels leads to overestimation of test performance when testing an AI algorithm in the setting of classifying severe AS using ECG data. While the effect of the bias may be modest in this example, clinicians should be aware of the existence of such a bias in AI-derived algorithms.
Keyphrases
- artificial intelligence
- machine learning
- deep learning
- big data
- aortic stenosis
- transcatheter aortic valve replacement
- ejection fraction
- aortic valve replacement
- aortic valve
- transcatheter aortic valve implantation
- left ventricular
- early onset
- palliative care
- electronic health record
- heart rate variability
- coronary artery disease
- heart failure
- rna seq
- single cell