Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
Awesome-medical-coding-NLP
A collection of papers on automated medical coding from free-texts
https://github.com/acadTags/Awesome-medical-coding-NLP
Last synced: 5 days ago
JSON representation
-
2023
- MDACE: MIMIC Documents Annotated with Code Evidence - in ACL 2023 - [official repository](https://github.com/3mcloud/MDACE/) `resource`, `explainability`
- Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study - in SIGIR 2023 - [official implementation](https://github.com/joakimedin/medical-coding-reproducibility) `resource`
- Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt - in AAAI 2023 `PLM`
- Surpassing GPT-4 Medical Coding with a Two-Stage Approach - `LLM`, `analysis`
- Automated clinical coding using off-the-shelf large language models - NeurIPS 2023 Deep Generative Models for Health workshop `LLM`, `PLM`, `few/zero-shot`, `knowledge`
- Towards Automatic ICD Coding via Knowledge Enhanced Multi-Task Learning - in CIKM 2023 `knowledge`, `NER+L`, `GNN`
-
2022
- AnEMIC: A Framework for Benchmarking ICD Coding Models - in EMNLP 2022 demo
- Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes? - in LOUHI@EMNLP 2022 `explainability`
- This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text - in AACL-IJCNLP 2022 `explainability`
- Automatic ICD Coding Exploiting Discourse Structure and Reconciled Code Embeddings - in COLING 2022
- TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding - in COLING 2022 `multimodal`
- Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding - in Findings of EMNLP 2022 `knowledge` `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Entity Anchored ICD Coding - in AMIA 2022 `knowledge` `NER+L`
- An exploratory data analysis: the performance differences of a medical code prediction system on different demographic groups - in ClinicalNLP@NAACL 2022 `analysis`
- PLM-ICD: Automatic ICD Coding with Pretrained Language Models - in ClinicalNLP@NAACL 2022 - [official implementation](https://github.com/miulab/plm-icd) `PLM`
- Horses to Zebras: Ontology-Guided Data Augmentation and Synthesis for ICD-9 Coding - in BioNLP@ACL 2022 `knowledge` `NER+L`
- Model Distillation for Faithful Explanations of Medical Code Predictions - in BioNLP@ACL 2022 `explainability`
- ICDBigBird: A Contextual Embedding Model for ICD Code Classification - in BioNLP@ACL 2022 `PLM`
- Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding - in ACL 2022 - [official implementation](https://github.com/GanjinZero/ICD-MSMN) `knowledge`
- A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge - in Findings of the ACL 2022 `knowledge`, `explainability`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
- Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels - in ICANN 2022 `PLM` `few/zero-shot`
-
2021
- Active learning for medical code assignment - ACM CHIL 2021 workshop `human-in-the-loop`
- Multitask Recalibrated Aggregation Network for Medical Code Prediction - multi-task learning - in ECML-PKDD 2021 `knowledge`
- Effective Convolutional Attention Network for Multi-label Clinical Document Classification - in EMNLP 2021 `CNN`
- Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification - Meta-learning for few- or zero-shot multi-label classification - in EMNLP 2021
- CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification - label classification, applied to MIMIC-III ICD coding - in EMNLP 2021 `knowledge`
- Description-based Label Attention Classifier for Explainable ICD-9 Classification - with Longformer and label descriptions - in W-NUT@EMNLP 2021 `knowledge`
- Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines - Attention-based model, human-level coding results - in MLHC 2021 - [leaderboard on paper with code](https://paperswithcode.com/sota/medical-code-prediction-on-mimic-iii) - [video](https://www.youtube.com/watch?v=Pm5DZhCOJJ0) `knowledge`
- Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study - III ICD coding. in Computers in Biology and Medicine, 2021 `PLM`
- Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network - in NAACL 2021 `explainability`
- Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism
- Analyzing Code Embeddings for Coding Clinical Narratives - in Findings of the ACL 2021 `knowledge`
- JLAN: medical code prediction via joint learning attention networks and denoising mechanism
- Explainable Automated Coding of Clinical Notes using Hierarchical Label-Wise Attention Networks and Label Embedding Initialisation
-
2020
- Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs - shot`
- An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels - (i) Improvement on zero-shot learning and (ii) the idea of Graph-aware Annotation Proximity (GAP), an graph-based look into the coding process, and (iii) BERTs' underpreformance on MIMIC-III. In EMNLP 2020 `few/zero-shot`
- BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining - in ClinicalNLP workshop at EMNLP 2020 `PLM`
- A Label Attention Model for ICD Coding from Clinical Text - In IJCAI 2020.
- Generalized Zero-Shot Text Classification for ICD Coding - Generalised Zero-shot learning with Generative adversial training, the ICD hierarchy with descriptions, and Graph Recurrent Neural Networks. In IJCAI 2020. `few/zero-shot`
- Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs
- HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding - Hyperbolic embedding + Graph Convolutional Networks. In ACL 2020. `knowledge`
- Clinical-Coder: Assigning Interpretable ICD-10 Codes to Chinese Clinical Notes
- Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset
- Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention - [official code](https://github.com/justinlovelace/Dynamic-Problem-Lists). `explainability`
-
2019
- ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network - in AAAI 2019 `CNN`
- Multimodal Machine Learning for Automated ICD Coding - structured text and structured tabular data for ICD coding. (Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao, Charlotte Band, Piyush Mathur, Frank Papay, Ashish K. Khanna, Jacek B. Cywinski, Kamal Maheshwari, Pengtao Xie, Eric P. Xing ; Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:197-215, 2019.) `multimodal`
- Ontological attention ensembles for capturing semantic concepts in ICD code prediction from clinical text - Multi-view convolution + multi-task learning. In LOUHI 2019 at EMNLP. `knowledge`
- Clinical Concept Extraction for Document-Level Coding - In BioNLP@ACL 2019. `knowledge` `NER+L`
- EHR Coding with Multi-scale Feature Attention and Structured Knowledge Graph Propagation - in CIKM 2019 `knowledge`
-
2018
- Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces - Few-shot and zero-shot learning with Graph Convolutional Neural Networks and the ICD hierarchy with descriptions. In EMNLP 2018. `few/zero-shot`
- Explainable Prediction of Medical Codes from Clinical Text - CNN with labelwise attention and the benchmark MIMIC preprocessed datasets. In NAACL-HLT 2018. `explainability`, `resource`
- Towards automated clinical coding - International Journal of Medical Informatics, 2018
-
2017
- Towards Automated ICD Coding Using Deep Learning
- Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods
- MIMIC-IV - IV-Note](https://www.physionet.org/content/mimic-iv-note)
- MIMIC-III
- CodieEsp
- MIMIC-IV - IV-Note](https://www.physionet.org/content/mimic-iv-note)
-
2024
- Continuous Predictive Modeling of Clinical Notes and ICD Codes in Patient Health Records - in BioNLP@ACL 2024
- Low-resource ICD Coding of Hospital Discharge Summaries - in BioNLP@ACL 2024 - `few/zero-shot`, `knowledge`
- Can GPT-3.5 Generate and Code Discharge Summaries? - in JAMIA - `LLM`, `analysis`, `few/zero-shot`
- ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes - `LLM`, `few/zero-shot`
- Exploring LLM Multi-Agents for ICD Coding - `LLM`, `few/zero-shot`, `explainability`
- An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records - `explainability`
- Data Drift in Clinical Outcome Prediction from Admission Notes - in LREC-COLING 2024 - `terminology-changes`
- Can GPT-3.5 Generate and Code Discharge Summaries? - in JAMIA - `LLM`, `analysis`, `few/zero-shot`
- Can GPT-3.5 Generate and Code Discharge Summaries? - in JAMIA - `LLM`, `analysis`, `few/zero-shot`