mlsanityData to 7 Oct 2026

Effective Medical Code Prediction via Label Internal Alignment

arXiv 2305.05162 ↗First posted 9 May 2023cs.LG cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

The clinical notes are usually typed into the system by physicians. They are typically required to be marked by standard medical codes, and each code represents a diagnosis or medical treatment procedure. Annotating these notes is time consuming and prone to error. In this paper, we proposed a multi-view attention based Neural network to predict medical codes from clinical texts. Our method incorporates three aspects of information, the semantic context of the clinical text, the relationship among the label (medical codes) space, and the alignment between each pair of a clinical text and medical code. Our method is verified to be effective on the open source dataset. The experimental result shows that our method achieves better performance against the prior state-of-art on multiple metrics.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

18 of 39 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  5. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  6. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  7. 2018Explainable Prediction of Medical Codes from Clinical Textcited by 231
  8. 2015Doctor AI: Predicting Clinical Events via Recurrent Neural Networkscited by 205
  9. 2017Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysiscited by 145
  10. 2019An Attentive Survey of Attention Modelscited by 123
  11. 2017DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understandingcited by 71
  12. 2018Joint Embedding of Words and Labels for Text Classificationcited by 69
6 more
  1. 2018Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanismscited by 53
  2. 2018Latent Alignment and Variational Attentioncited by 36
  3. 2017Towards Automated ICD Coding Using Deep Learningcited by 35
  4. 2016Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Recordscited by 20
  5. 2016Condensed Memory Networks for Clinical Diagnostic Inferencingcited by 14
  6. 2015Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networkscited by 2

Built on by

Not cited yet.