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For all iterations of NLP model development, evaluation statistics (precision, recall, and F-measure) were calculated based on true positives, false positives, and false negatives for three defined matching tasks: (1) strict-boundary matching, (2) soft-boundary matching, and (3) note categorization. Strict-boundary matching considered only the perfect overlap of the human annotation and NLP target matching methods to be a match in performance metric calculation.
JMIR Med Inform 2025;13:e66466
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In particular, the method described in Ebrahimi et al [33] is used to approximate the differential entropy, implemented in the Python (Python Software Foundation) library Sci Py version 1.7.3 [34], as the closed-form expression for the attention distribution for a given row f (x) is not known analytically from the values of attention sampled. The differential entropy for a row i is given by
and the corresponding vector h ∈ Rn×1 corresponds to the entropy across every row.
JMIR Aging 2025;8:e65178
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