Author:
Khaleghparast Shiva,Maleki Majid,Hajianfar Ghasem,Soumari Esmaeil,Oveisi Mehrdad,Golandouz Hassan Maleki,Noohi Feridoun,dehaki Maziar Gholampour,Golpira Reza,Mazloomzadeh Saeideh,Arabian Maedeh,Kalayinia Samira
Abstract
Abstract
Background
Patients’ rights are integral to medical ethics. This study aimed to perform sentiment analysis and opinion mining on patients’ messages by a combination of lexicon-based and machine learning methods to identify positive or negative comments and to determine the different ward and staff names mentioned in patients’ messages.
Methods
The level of satisfaction and observance of the rights of 250 service recipients of the hospital was evaluated through the related checklists by the evaluator. In total, 822 Persian messages, composed of 540 negative and 282 positive comments, were collected and labeled by the evaluator. Pre-processing was performed on the messages and followed by 2 feature vectors which were extracted from the messages, including the term frequency–inverse document frequency (TFIDF) vector and a combination of the multifeature (MF) (a lexicon-based method) and TFIDF (MF + TFIDF) vectors. Six feature selectors and 5 classifiers were used in this study. For the evaluations, 5-fold cross-validation with different metrics including area under the receiver operating characteristic curve (AUC), accuracy (ACC), F1 score, sensitivity (SEN), specificity (SPE) and Precision-Recall Curves (PRC) were reported. Message tag detection, which featured different hospital wards and identified staff names mentioned in the study patients’ messages, was implemented by the lexicon-based method.
Results
The best classifier was Multinomial Naïve Bayes in combination with MF + TFIDF feature vector and SelectFromModel (SFM) feature selection (ACC = 0.89 ± 0.03, AUC = 0.87 ± 0.03, F1 = 0.92 ± 0.03, SEN = 0.93 ± 0.04, and SPE = 0.82 ± 0.02, PRC-AUC = 0.97). Two methods of assessment by the evaluator and artificial intelligence as well as survey systems were compared.
Conclusion
Our results demonstrated that the lexicon-based method, in combination with machine learning classifiers, could extract sentiments in patients’ comments and classify them into positive and negative categories. We also developed an online survey system to analyze patients’ satisfaction in different wards and to remove conventional assessments by the evaluator.
Publisher
Springer Science and Business Media LLC
Reference31 articles.
1. Otani K, Kurz RS, Barney SM. The impact of nursing care and other healthcare attributes on hospitalized patient satisfaction and behavioral intentions. J Healthc Manag. 2004;49(3):181.
2. Özdemir MH, Can İÖ, Ergönen AT, Hilal A, Önder M, Meral D. Midwives and nurses awareness of patients’ rights. Midwifery. 2009;25(6):756–65.
3. Siegal G, Siegal N, Weisman Y. Physicians’ attitudes towards patients’ rights legislation. Med & L. 2001;20:63.
4. Parsapoor A, Bagheri A, Larijani B. Patient’s rights charter in Iran. Acta Medica Iranica 2014:24–28.
5. Kuzu N, Ergin A, Zencir M. Patients’ awareness of their rights in a developing country. Public Health. 2006;120(4):290–6.
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