Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for ICD coding are limited due to the high-dimensional space of multi-label assignment and the long-tail challenge. |
| Approach: | They propose a prompt-based fine-tuning technique with label semantics to solve this challenge. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a benchmark dataset of code assignment in 14.5% of cases. |
Similar Papers
A General Knowledge Injection Framework for ICD Coding (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to improve ICD coding focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other. |
| Approach: | They propose a general knowledge injection framework that integrates three key types of knowledge without specialized design of additional modules. |
| Outcome: | The proposed framework outperforms baseline models and is comparable to models relying on extra human annotations. |
Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches to assigning ICD codes to clinical text are time-consuming, labor intensive, and error-prone. |
| Approach: | They propose to adapt a Transformer-based model to a longformer model and use it to encode clinical narratives. |
| Outcome: | The proposed approach outperforms current state-of-the-art models in ICD coding with the label embeddings contributing to the good performance. |
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. |
PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing studies on prompt-based few-shot tuning focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs. |
| Approach: | They propose a framework that leverages label semantics for prompt-based tuning. |
| Outcome: | The proposed framework improves on few-shot text classification tasks by leveraging label semantics and data augmentation. |
MKeCL: Medical Knowledge-Enhanced Contrastive Learning for Few-shot Disease Diagnosis (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to disease classification are limited in real-world clinics due to insufficient data and inflexibility. |
| Approach: | They propose a medical knowledge-Enhanced Contrastive Learning approach to disease diagnosis . they incorporate medical knowledge graphs and medical licensing exams in modeling . |
| Outcome: | The proposed model outperforms existing models on real clinical EMRs on a single patient. |
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. |
Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for ICD indexing have a heavy label distribution and a manual process . Xie and Xing (2017) propose a new approach to ICD re-ranking . |
| Approach: | They propose a "retrieve and re-rank" framework to allocate subsets of ICD codes to medical records . they leverage auxiliary knowledge of the electronic health records (EHR) and a discrete retrieval method . |
| Outcome: | The proposed method achieves state-of-the-art performance on the MIMIC-III benchmark. |
A Two-Stage Decoder for Efficient ICD Coding (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent automated ICD coding efforts improve performance by encoding medical notes and codes with additional data and knowledge bases. |
| Approach: | They propose a two-stage decoding mechanism to predict ICD codes using hierarchical properties of the codes to split the prediction into two steps: at first, predict the parent code and then predict the child code based on the previous prediction. |
| Outcome: | Experiments on the public MIMIC-III data show that the proposed model performs well in single-model settings without external data or knowledge. |
SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have used prompt-based fine-tuning methods for text classification tasks . however, the difficulty and costs of manually selecting domain label terms for the verbalizer remain unexplored . |
| Approach: | They propose a framework to automatically retrieve scientific topic-related terms for low-resource text classification tasks. |
| Outcome: | The proposed method outperforms state-of-the-art methods on scientific text classification tasks under few and zero-shot settings. |
Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning (2022.naacl-industry)
Copied to clipboard
| Challenge: | Medical coding (MC) is an essential pre-requisite for reliable data retrieval and reporting. |
| Approach: | They propose a method to classify medical terms into standardized alphanumerical terms and codes . they use a combination of traditional BERT-based classification and a zero/few-shot learning approach . |
| Outcome: | The proposed approach outperforms baselines in the few-shot regime. |