Exemplar Auditing for Multi-Label Biomedical Text Classification

• URL: http://arxiv.org/abs/2004.03093v1
• Date: Tue, 7 Apr 2020 02:54:20 GMT
• Title: Exemplar Auditing for Multi-Label Biomedical Text Classification
• Authors: Allen Schmaltz and Andrew Beam
• Abstract summary: We generalize a recently proposed zero-shot sequence labeling method, "supervised labeling via a convolutional decomposition" The approach yields classification with "introspection", relating the fine-grained features of an inference-time prediction to their nearest neighbors. Our proposed approach yields both a competitively effective classification model and an interrogation mechanism to aid healthcare workers in understanding the salient features that drive the model's predictions.
• Score: 0.4873362301533824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many practical applications of AI in medicine consist of semi-supervised discovery: The investigator aims to identify features of interest at a resolution more fine-grained than that of the available human labels. This is often the scenario faced in healthcare applications as coarse, high-level labels (e.g., billing codes) are often the only sources that are readily available. These challenges are compounded for modalities such as text, where the feature space is very high-dimensional, and often contains considerable amounts of noise. In this work, we generalize a recently proposed zero-shot sequence labeling method, "binary labeling via a convolutional decomposition", to the case where the available document-level human labels are themselves relatively high-dimensional. The approach yields classification with "introspection", relating the fine-grained features of an inference-time prediction to their nearest neighbors from the training set, under the model. The approach is effective, yet parsimonious, as demonstrated on a well-studied MIMIC-III multi-label classification task of electronic health record data, and is useful as a tool for organizing the analysis of neural model predictions and high-dimensional datasets. Our proposed approach yields both a competitively effective classification model and an interrogation mechanism to aid healthcare workers in understanding the salient features that drive the model's predictions.

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