Physicians’ minds, no matter how bright or experienced, are fallible—unable to adequately store, recall, and correctly analyze the millions of pieces of medical information needed to optimally care for patients. The promise of machine learning (ML) and predictive analytics is that clinicians’ decisions can be augmented by computers rather than relying solely on their brains. For example, automated ML algorithms can rapidly search through gigabytes of data and generate probabilistic estimates of patients’ likelihood for different outcomes, such as various disease complications or death. With these empirical estimates, patients and their physicians could make better informed care decisions.
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Peterson ED. Machine Learning, Predictive Analytics, and Clinical Practice: Can the Past Inform the Present? JAMA. 2019;322(23):2283–2284. doi:10.1001/jama.2019.17831
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