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Supervised Learning in the Presence of Noise: Application in ICD-10 Code Classification
We first identify ICD-10 codes that human coders tend to misuse or confuse, based on the codes' locations in the ICD-10 hierarchy, the types of the codes, and baseline classifier's prediction behaviors; we then develop a novel training strategy that accounts for such noise.
From Extreme Multi-label to Multi-class: A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention
With our proposed approach, interpretability is achieved not through implicitly learned attention scores but by attributing each prediction to a particular sentence and words selected by human coders.
An Ensemble Approach for Automatic Structuring of Radiology Reports
We present an ensemble method that consolidates the predictions of three models, capturing various attributes of textual information for automatic labeling of sentences with section labels.
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