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Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignment

arXiv 2112.13756 ↗First posted 27 Dec 2021cs.CLcited by 0

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Abstract (arXiv)

Clinical information systems have become large repositories for semi-structured and partly annotated electronic health record data, which have reached a critical mass that makes them interesting for supervised data-driven neural network approaches. We explored automated coding of 50 character long clinical problem list entries using the International Classification of Diseases (ICD-10) and evaluated three different types of network architectures on the top 100 ICD-10 three-digit codes. A fastText baseline reached a macro-averaged F1-score of 0.83, followed by a character-level LSTM with a macro-averaged F1-score of 0.84. The top performing approach used a downstreamed RoBERTa model with a custom language model, yielding a macro-averaged F1-score of 0.88. A neural network activation analysis together with an investigation of the false positives and false negatives unveiled inconsistent manual coding as a main limiting factor.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 27 Dec 2021.

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25 of 45 references.

References · 25

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13 more
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