Multi-Label Classification of Patient Notes: Case Study on ICD Code Assignment

Tal Baumel, Jumana Nassour-Kassis, Raphael Cohen, Michael Elhadad, Noemie Elhadad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The automatic coding of clinical documentation according to diagnosis codes is a useful task in the Electronic Health Record, but a challenging one due to the large number of codes and the length of patient notes. We investigate four models for assigning multiple ICD codes to discharge summaries, and experiment with data from the MIMIC II and III clinical datasets. We present Hierarchical Attention-bidirectional Gated Recurrent Unit (HA-GRU), a hierarchical approach to tag a document by identifying the sentences relevant for each label. HA-GRU achieves state-of-the art results. Furthermore, the learned sentence-level attention layer highlights the model decision process, allows for easier error analysis, and suggests future directions for improvement.
Original languageEnglish
Title of host publication2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018)
Place of PublicationNew Orleans
Pages409-416
Number of pages8
StatePublished - 2018

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