Intelligent EHRs: Predicting Procedure Codes From Diagnosis Codes
Abstract
In order to submit a claim to insurance companies, a doctor needs to code a patient encounter with both the diagnosis (ICDs) and procedures performed (CPTs) in an Electronic Health Record (EHR). Identifying and applying relevant procedures code is a cumbersome and time-consuming task as a doctor has to choose from around 13,000 procedure codes with no predefined one-to-one mapping. In this paper, we propose a state-of-the-art deep learning method for automatic and intelligent coding of procedures (CPTs) from the diagnosis codes (ICDs) entered by the doctor. Precisely, we cast the learning problem as a multi-label classification problem and use distributed representation to learn the input mapping of high-dimensional sparse ICDs codes. Our final model trained on 2.3 million claims is able to outperform existing rule-based probabilistic and association-rule mining based methods and has a recall of 90@3.
Keywords
Cite
@article{arxiv.1712.00481,
title = {Intelligent EHRs: Predicting Procedure Codes From Diagnosis Codes},
author = {Hasham Ul Haq and Rameel Ahmad and Sibt Ul Hussain},
journal= {arXiv preprint arXiv:1712.00481},
year = {2021}
}
Comments
Accepted poster at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/)