The Comparative Experimental Study of Multilabel Classification for Diagnosis Assistant Based on Chinese Obstetric EMRs.

Kunli Zhang,Hongying Zan,Hongchao Ma,Yueshu Zhao,Lei Zhuang

doi:10.1155/2018/7273451

Abstract

Obstetric electronic medical records (EMRs) contain massive amounts of medical data and health information. The information extraction and diagnosis assistants of obstetric EMRs are of great significance in improving the fertility level of the population. The admitting diagnosis in the first course record of the EMR is reasoned from various sources, such as chief complaints, auxiliary examinations, and physical examinations. This paper treats the diagnosis assistant as a multilabel classification task based on the analyses of obstetric EMRs. The latent Dirichlet allocation (LDA) topic and the word vector are used as features and the four multilabel classification methods, BP-MLL (backpropagation multilabel learning), RAkEL (RAndom k labELsets), MLkNN (multilabel k-nearest neighbor), and CC (chain classifier), are utilized to build the diagnosis assistant models. Experimental results conducted on real cases show that the BP-MLL achieves the best performance with an average precision up to 0.7413 ± 0.0100 when the number of label sets and the word dimensions are 71 and 100, respectively. The result of the diagnosis assistant can be introduced as a supplementary learning method for medical students. Additionally, the method can be used not only for obstetric EMRs but also for other medical records.

Highlights

Since family planning was issued as one of the fundamental state policies in China, late marriage and late childbirth have benefited the country
MLkNN is better than BP-multilabel learning (MLL) on both sides of the polyline, but in the middle part, BP-MLL is better than MLkNN
On the basis of the analysis of obstetric electronic medical records (EMRs), the diagnosis assistant is regarded as a multilabel classification task

Summary

Introduction

Since family planning was issued as one of the fundamental state policies in China, late marriage and late childbirth have benefited the country. The problem is exacerbated with the implementation of the Universal Two-child Policy in 2016. Since the National Health and Family Planning Medical Affairs Commission issued the Basic Norms of Electronic Medical Records (Trial) [2] in 2010, medical institutions have accumulated many obstetric EMRs (electronic medical records). EMR data are big data in the medical field. They contain medical data and a large amount of patients’ health information. One urgent task is how to achieve clinical information decision support with these resources in order to improve clinical treatments

Methods

Results

Conclusion