Effective Convolutional Attention Network for Multi-label Clinical Document Classification (2021.emnlp-main)
Copied to clipboard
| Challenge: | a large number of medical encounters need to be coded everyday due to long document sets and large label set. |
| Approach: | They propose a convolutional attention network for multi-label document classification problem . they use convolution-based encoders and convolution networks to aggregate information across documents . |
| Outcome: | The proposed model outperforms prior best model and multilingual Transformer model on a widely used dataset in the medical domain. |
Similar Papers
SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance. |
| Approach: | They propose a multi-label document classification paradigm that utilizes large language models to expand the label content and generate pseudo-samples for the tail categories. |
| Outcome: | The proposed method significantly outperforms state-of-the-art models. |
Label-Specific Document Representation for Multi-Label Text Classification (D19-1)
Copied to clipboard
| Challenge: | Existing methods to classify documents using labels only assign one label to document . multi-label text classification is a challenging task because of the huge amount of documents, words and labels. |
| Approach: | They propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four datasets . it can predict low-frequency labels, and it can be used in sentimental analysis . |
Explainable Prediction of Medical Codes from Clinical Text (N18-1)
Copied to clipboard
| Challenge: | Clinical notes are text documents that are created by clinicians for each patient encounter. |
| Approach: | They propose a method that aggregates information across the document using a convolutional neural network and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. |
| Outcome: | The proposed method is accurate and better than the current state of the art. |
Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for ICD indexing use machine learning to assign subset of codes to medical records . experimental results show proposed method achieves state-of-the-art performance on a number of measures. |
| Approach: | They propose a method that uses a deep dilated residual convolution encoder to learn document representations across different lengths of the texts. |
| Outcome: | The proposed method achieves state-of-the-art performance on a number of measures. |
DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction (2024.emnlp-main)
Copied to clipboard
| Challenge: | Prior work focused on hierarchical label structures but neglected to incorporate external knowledge from medical guidelines. |
| Approach: | They propose to incorporate external knowledge from medical guidelines into domain knowledge enhanced classification for diagnosis prediction. |
| Outcome: | The proposed system outperforms state-of-the-art label-wise attention networks and transformer models on a real-world emergency medical services dataset and a public electronic health record dataset. |
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification (D18-1)
Copied to clipboard
| Challenge: | a novel model for multi-label text classification is proposed for the task of assigning multiple labels for a given text. |
| Approach: | They propose a novel model for multi-label text classification based on sequence-to-sequence learning and a hybrid attention mechanism that extracts both the word-level and the semantic unit. |
| Outcome: | The proposed model is competitive to the baseline models and more robust to classifying low-frequency labels. |
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent work in XMC addresses this problem using deep encoders that project text descriptions to an embedding space suitable for recovering the closest labels. |
| Approach: | They propose a method that uses a shallow transformer encoder to combine text-based embeddings, label centroids and learnable free vectors to improve XMC efficiency. |
| Outcome: | The proposed method achieves state-of-the-art in several public benchmarks of different sizes and domains while keeping the model efficient. |
Leveraging Hierarchical Category Knowledge for Data-Imbalanced Multi-Label Diagnostic Text Understanding (D19-62)
Copied to clipboard
| Challenge: | Existing work on automatic diagnosis prediction has considered the output labels independently, so that the codes with few samples are difficult to learn. |
| Approach: | They propose to leverage domain knowledge to predict diagnostic codes given the descriptive present illness in electronic health records by leveraging hierarchical category knowledge. |
| Outcome: | The proposed model can efficiently utilize category knowledge and provide informative cues to improve the top-ranked diagnostic codes which is better than the prior state-of-the-art. |
EMR Coding with Semi-Parametric Multi-Head Matching Networks (N18-1)
Copied to clipboard
| Challenge: | Electronic medical record (EMR) coding is the process of extracting diagnosis and procedure codes from the digital record (the EMR) pertaining to a patient's visit. |
| Approach: | They propose a neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-the-art models. |
| Outcome: | The proposed model outperforms existing models on a well known de-identified EMR dataset with multi-label performance measures. |
An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels (2020.emnlp-main)
Copied to clipboard
Ilias Chalkidis, Manos Fergadiotis, Sotiris Kotitsas, Prodromos Malakasiotis, Nikolaos Aletras, Ion Androutsopoulos
| Challenge: | Large-scale Multi-label Text Classification (LMTC) is a type of classification that assigns labels to a large set of labels. |
| Approach: | They propose to use probabilistic label trees to improve frequent, few and zero-shot learning . they propose to combine a new state-of-the-art method with pre-trained Transformers . |
| Outcome: | The proposed models outperform existing models on frequent, few and zero-shot learning on three datasets from different domains. |