Medical Code Prediction

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).

Benchmarks

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Most implemented papers

Explainable Prediction of Medical Codes from Clinical Text

Content

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

MIMIC-III, a freely accessible critical care database

A Label Attention Model for ICD Coding from Clinical Text

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

An explainable CNN approach for medical codes prediction from clinical text

Multitask Balanced and Recalibrated Network for Medical Code Prediction

Multitask Recalibrated Aggregation Network for Medical Code Prediction

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

Modeling Diagnostic Label Correlation for Automatic ICD Coding