Next Article in Journal
Blockchain for Integrated Nuclear Power Plants Management System
Previous Article in Journal
Emotion-Semantic-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism for Microblog Sentiment Analysis
Previous Article in Special Issue
Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital
Article

Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary

School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Information 2020, 11(5), 281; https://doi.org/10.3390/info11050281
Received: 8 March 2020 / Revised: 13 May 2020 / Accepted: 20 May 2020 / Published: 23 May 2020
(This article belongs to the Special Issue Natural Language Processing in Healthcare and Medical Informatics)
Patients’ discharge summaries (documents) are health sensors that are used for measuring the quality of treatment in medical centers. However, extracting information automatically from discharge summaries with unstructured natural language is considered challenging. These kinds of documents include various aspects of patient information that could be used to test the treatment quality for improving medical-related decisions. One of the significant techniques in literature for discharge summaries classification is feature extraction techniques from the domain of natural language processing on text data. We propose a novel sentiment analysis method for discharge summaries classification that relies on vector space models, statistical methods, association rule, and extreme learning machine autoencoder (ELM-AE). Our novel hybrid model is based on statistical methods that build the lexicon in a domain related to health and medical records. Meanwhile, our method examines treatment quality based on an idea inspired by sentiment analysis. Experiments prove that our proposed method obtains a higher F1 value of 0.89 with good TPR (True Positive Rate) and FPR (False Positive Rate) values compared with various well-known state-of-the-art methods with different size of training and testing datasets. The results also prove that our method provides a flexible and effective technique to examine treatment quality based on positive, negative, and neutral terms for sentence-level in each discharge summary. View Full-Text
Keywords: discharge summaries; text clustering; extreme learning machine; sentiment analysis; health surveillance; quality evaluation discharge summaries; text clustering; extreme learning machine; sentiment analysis; health surveillance; quality evaluation
Show Figures

Figure 1

MDPI and ACS Style

Waheeb, S.A.; Ahmed Khan, N.; Chen, B.; Shang, X. Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary. Information 2020, 11, 281. https://doi.org/10.3390/info11050281

AMA Style

Waheeb SA, Ahmed Khan N, Chen B, Shang X. Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary. Information. 2020; 11(5):281. https://doi.org/10.3390/info11050281

Chicago/Turabian Style

Waheeb, Samer A., Naseer Ahmed Khan, Bolin Chen, and Xuequn Shang. 2020. "Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary" Information 11, no. 5: 281. https://doi.org/10.3390/info11050281

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop