CBOWRA: A Representation Learning Approach for Medication Anomaly Detection

TL;DR

This paper introduces CBOWRA, a semantic representation method using continuous bag of words to detect medication anomalies in electronic health records by identifying semantic inconsistencies between diagnoses and prescriptions.

Contribution

It proposes a novel detection approach that leverages semantic context and ranking strategies, reducing reliance on manual feature engineering and improving detection accuracy.

Findings

TopN accuracy improved by up to 10.91% on one dataset

Method is highly competitive compared to traditional machine learning approaches

Effective in identifying semantic inconsistencies in real hospital records

Abstract

Electronic health record is an important source for clinical researches and applications, and errors inevitably occur in the data, which could lead to severe damages to both patients and hospital services. One of such error is the mismatches between diagnoses and prescriptions, which we address as 'medication anomaly' in the paper, and clinicians used to manually identify and correct them. With the development of machine learning techniques, researchers are able to train specific model for the task, but the process still requires expert knowledge to construct proper features, and few semantic relations are considered. In this paper, we propose a simple, yet effective detection method that tackles the problem by detecting the semantic inconsistency between diagnoses and prescriptions. Unlike traditional outlier or anomaly detection, the scheme uses continuous bag of words to construct the semantic connection between specific central words and their surrounding context. The detection of medication anomaly is transformed into identifying the least possible central word based on given context. To help distinguish the anomaly from normal context, we also incorporate a ranking accumulation strategy. The experiments were conducted on two real hospital electronic medical records, and the topN accuracy of the proposed method increased by 3.91 to 10.91% and 0.68 to 2.13% on the datasets, respectively, which is highly competitive to other traditional machine learning-based approaches.