Reference Li J, Hao Y, Liu Y, et al. Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study.

Mount Sinai researchers used a clinical and biomedical text processing model at six hospitals to analyze triage data and ...

Artificial intelligence (AI) can help emergency department (ED) teams better anticipate which patients will need hospital ...

The healthcare industry is witnessing a technological revolution like never before. Artificial intelligence and machine ...

Machine learning—a term that describes the ability of algorithms to adjust and learn from data and to take or suggest actions—may be the answer they’re searching for.

That’s sort of the next frontier of this.” 2. Dr. Obermeyer said machine learning doesn’t show researchers and physicians how it reaches conclusions. Such algorithms only show predictions.

Machine learning holds promise for optimizing treatment strategies and potentially improving outcomes in respiratory failure ...

Of the three classifier algorithms, XGBoost trained with the HD features had the best 10-fold cross-validated accuracy with a mean of 0.88, sensitivity and specificity of 0.85 and 0.91 ...

They can check the instructions for the algorithm and audit the output data before it leaves the hospital. If they don’t like it, they can shut off their database to the federated network.