xtracted from a set of N electronic health records (EHRs), for a population of N patients. The features might involve patient attributes and current and past symptoms, diagnoses, est results, hospitalizations, procedures, and medications.) Briefly describe in words a ractical use for identifying the 10 nearest neighbors of a given EHR (as measured by heir associated feature vectors), among the other EHRS.

3-2-1 Code It
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Chapter11: Cpt Surgery I
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Neighboring electronic health records. Let x₁, . . . , x N be n-vectors that contain n features
extracted from a set of N electronic health records (EHRs), for a population of N patients.
(The features might involve patient attributes and current and past symptoms, diagnoses,
test results, hospitalizations, procedures, and medications.) Briefly describe in words a
practical use for identifying the 10 nearest neighbors of a given EHR (as measured by
their associated feature vectors), among the other EHRs.
Transcribed Image Text:Neighboring electronic health records. Let x₁, . . . , x N be n-vectors that contain n features extracted from a set of N electronic health records (EHRs), for a population of N patients. (The features might involve patient attributes and current and past symptoms, diagnoses, test results, hospitalizations, procedures, and medications.) Briefly describe in words a practical use for identifying the 10 nearest neighbors of a given EHR (as measured by their associated feature vectors), among the other EHRs.
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