Electronic health records store valuable information about hospital patients, but they’re often sparse and unstructured, making them difficult for potentially labor- and time-saving AI systems to parse. Fortunately, researchers at New York University and Princeton have developed a framework that evaluates clinical notes (i.e., descriptions of symptoms, reasons for diagnoses, and radiology results) and autonomously assigns a risk score indicating whether patients will be readmitted within 30 days. They claim that the code and model parameters, which are publicly available on Github, handily outperform baselines.
“Accurately predicting readmission has clinical significance both in terms of efficiency and reducing the burden on intensive care unit doctors,” the paper’s authors wrote. “One estimate puts the financial burden of readmission at $17.9 billion dollars and the fraction of avoidable admissions at 76 percent.”
Source: AI predicts hospital readmission rates from clinical notes | VentureBeat
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