Multi-label natural language processing to identify diagnosis and procedure codes from MIMIC-III inpatient notes

Understand

In the United States, 25% or greater than 200 billion dollars of hospital spending accounts for administrative costs that involve services for medical coding and billing.

• With the increasing number of patient records, manual assignment of the codes performed is overwhelming, time-consuming and error-prone, causing billing errors.
• Natural language processing can automate the extraction of codes/labels from unstructured clinical notes, which can aid human coders to save time, increase productivity, and verify medical coding errors.
• Our objective is to identify appropriate diagnosis and procedure codes from clinical notes by performing multi-label classification.