Papers with Code 아카이브

Medical Code Prediction

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하위 태스크: 0
평가표: 7
보존된 평가 기록: 50

Context: Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort by human coders today. A new milestone will mark a meaningful step toward fully Autonomous Medical Coding in machines reaching parity with human coders' performance in medical code prediction.

Question: What exactly is the medical code prediction problem?

Answer: Clinical notes contain much information about what precisely happened during the patient's entire stay. And those clinical notes (e.g., discharge summary) is typically long, loosely structured, consists of medical domain language, and sometimes riddled with spelling errors. So, it's a highly multi-label classification problem, and the forthcoming ICD-11 standard will add more complexity to the problem! The medical code prediction problem is to annotate this clinical note with multiple codes subset from nearly 70K total codes (in the current ICD-10 system, for example).

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Medical Code Prediction

논문

A General Knowledge Injection Framework for ICD Coding

Uncertainty-aware abstention in medical diagnosis based on medical texts

An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records

Effective Medical Code Prediction via Label Internal Alignment

Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification

Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study

Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?

Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding

HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding

Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding

Multitask Balanced and Recalibrated Network for Medical Code Prediction

Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines

Parameter Selection: Why We Should Pay More Attention to It

Modeling Diagnostic Label Correlation for Automatic ICD Coding

Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention

Multitask Recalibrated Aggregation Network for Medical Code Prediction

Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study

An Explainable CNN Approach for Medical Codes Prediction from Clinical Text

Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation

A Label Attention Model for ICD Coding from Clinical Text

ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

Medical code prediction with multi-view convolution and description-regularized label-dependent attention

Explainable Prediction of Medical Codes from Clinical Text