CBOWRA: A Representation Learning Approach for Medication Anomaly Detection

Abstract

Electronic health record is an important source for clinical researches and applications, and errors inevitably occur in the data, which lead to severe damages to both patients and hospital services. One of such errors is the mismatch between diagnose and prescription, 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.

A novel data-driven algorithm to predict anomalous prescription based on patient's feature set

“…There is an increasing trend to study how machine learning (ML) tools can be used to augment medical professionals' decisions concerning diagnosis, treatment safety, and quality of patient care [2,3]. Several pharmaceutical studies [4][5][6][7] have applied ML to find anomalous prescriptions but not tailored to RT. In RT, several studies [8][9][10][11] have used ML to look at the treatment parameters to detect errors in treatment plans, but not focus on prescription error detection.…”