Mining Patient Health Care Service Opinions for Hospital Recommendations

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-9
Year of Publication : 2021
Authors : G.Sabarmathi, Dr.R.Chinnaiyan
  10.14445/22315381/IJETT-V69I9P220

MLA 

MLA Style: G.Sabarmathi, Dr.R.Chinnaiyan "Mining Patient Health Care Service Opinions for Hospital Recommendations" International Journal of Engineering Trends and Technology 69.9(2021):161-167. 

APA Style: G.Sabarmathi, Dr.R.Chinnaiyan. Mining Patient Health Care Service Opinions for Hospital Recommendations International Journal of Engineering Trends and Technology, 69(9),161-167.

Abstract
Patient’s experience within the hospital environment is of paramount importance for the health care sector. Online reviews are recognized as the significant yardstick to scale the hospital's performance. This research proposes a novel machine learning-based ensemble classifier model to interpret the reviews in terms of patient’s experience and for the hospital recommendation system. The outcomes were compared with various machine learning classifications using a cross-validation approach to predict the most accurate model. The predicted result brings out an interesting viewpoint that avails the healthcare sector an opportunity to look into their service offerings in improving patient’s experience and hospital recommendation systems.

Reference
[1] S.N. Manke, N. Shivale, A review on sentiment analysis mining and sentiment analysis based on natural language processing. Int. J. Comput. Appl. 109(4) (2015)
[2] H. Mulki, et al., Tunisian dialect sentiment analysis: a natural language processing-based approach. Computación y Sistemas 22(4) (2018).
[3] Abel F, Gao Q, Houben G-J, Tao K., Twitter-based user modeling for news recommendations. In: Rossi F (ed) IJCAI 2013, proceedings of the 23rd international joint conference on artificial intelligence, Beijing, China, (2013) 3–9., IJCAI/AAAI.
[4] Huber M, Knottnerus JA, Green L, van der Horst H, Jadad AR, Kromhout D, et al. How should we define health? Br Med J 2011 Jul 26;343:d4163. [CrossRef] [Medline]
[5] Berg O. Health and quality of life. Acta Sociologica., 18(1) (1975) 3-22. [CrossRef]
[6] A. Shoukry, A. Rafea, Sentence-level Arabic sentiment analysis, in 2012 International Conference on Collaboration Technologies and Systems (CTS), IEEE (2012) 546–550
[7] Zomaya AY, Sakr S. Handbook of big data technologies. Berlin: Springer; (2017). https://doi.org/10.1007/978-3-319-49340-4
[8] H. Iyer, M. Gandhi, S. Nair, Sentiment analysis for visuals using natural language processing.Int. J. Comput. Appl. 128(6) (2015) 31–35.
[9] M.T. Khan, S. Khalid, Sentiment analysis for health care, in Big Data: Concepts, Methodolo-gies, Tools, and Applications, IGI Global (2016) 676–689
[10] M.M. Mostafa, N.R. Nebot, Sentiment analysis of Spanish words of Arabic origin related to Islam: a social network analysis. J. Lang. Teach. Res. 8(6) (2017) 1041–1049.
[11] Denecke K, Deng Y. Sentiment analysis in medical settings: new opportunities and challenges. Artif Intell Med. 64(1) (2015) 17–27. doi: 10.1016/j.artmed.2015.03.006. [PubMed] [CrossRef] [Google Scholar].
[12] Gohil S, Vuik S, Darzi A. Sentiment analysis of health care tweets: a review of the methods used. JMIR Public HealthSurveill. 23 4(2) (2018) e43. doi: 10.196/publichealth.5789. https://publichealth.jmir.org/2018/2/e43/ [PMC free article] [PubMed] [CrossRef] [Google Scholar].
[13] L. Igual, S. Seguí, Statistical natural language processing for sentiment analysis, in Introduction to Data Science Springer, Cham, (2017) 181–197.
[14] Nikfarjam A, Emadzadeh E, Gonzalez G. A hybrid system for emotion extraction from suicide notes. Biomed Inform Insights 5(Suppl 1) (2012) 165-174 [FREE Full text] [CrossRef] [Medline]
[15] Wang W, Chen L, Tan M, Wang S, Sheth AP. Discovering fine-grained sentiment in suicide notes. Biomed Inform Insights 5(Suppl 1) (2012) 137-145 [FREE Full text] [CrossRef] [Medline]
[16] Liakata M, Kim J, Saha S, Hastings J, Rebholz-Schuhmann D. Three hybrid classifiers for the detection of emotions in suicide notes. Biomed Inform Insights;5(Suppl 1) (2012) 175-184 [FREE Full text] [CrossRef] [Medline].
[17] Pedersen T. Rule-based and lightly supervised methods to predict emotions in suicide notes. Biomed Inform Insights 5(Suppl 1) (2012) 185-193 [FREE Full text] [CrossRef] [Medline].
[18] Sohn S, Torii M, Li D, Wagholikar K, Wu S, Liu H. A hybrid approach to sentiment sentence classification in suicide notes. Biomed Inform Insights;5(Suppl 1) (2012) 43-50 [FREE Full text] [CrossRef] [Medline]
[19] Wang W, Chen L, Tan M, Wang S, Sheth AP. Discovering fine-grained sentiment in suicide notes. Biomed Inform Insights;5(Suppl 1) (2012) 137-145 [FREE Full text] [CrossRef] [Medline]
[20] Yu N, Kübler S, Herring J, Hsu Y, Israel R, Smiley C. LASSA: emotion detection via information fusion. Biomed Inform Insights;5(Suppl. 1) (2012) 71-76 [FREE Full text] [CrossRef] [Medline]
[21] Chen L, Gong T, Kosinski M, Stillwell D, Davidson RL. Building a profile of subjective well-being for social media users. PLoS One;12(11) (2017) e0187278 [FREE Full text] [CrossRef] [Medline]
[22] Gohil S, Vuik S, Darzi A. Sentiment analysis of health care tweets: a review of the methods used. JMIR Public Health Surveill Apr 23; 4(2) (2018) e43 [FREE Full text] [CrossRef] [Medline]
[23] Kitchenham B. Procedures for performing systematic reviews. Keele University, Keele;33(2004) 1-26 [FREE Full text]
[24] Cochrane Library: Cochrane Reviews. URL: https://www.cochranelibrary.com/ [accessed 2019-11-12]
[25] Paltoglou G, Thelwall M. A study of information retrieval weighting schemes for sentiment analysis. In: Proceedings of the 48th annual meeting of the association for computational linguistics (ACL ‟10). (2010) 1386–1395.
[26] Yessenalina YY, Cardie C. Multi-level structured models for document-level sentiment classification. In: Proceedings of the 2010 conference on empirical methods in natural language processing (EMNLP ‟10). (2010) 1046–56.
[27] Wilson T, Wiebe J, Hofmann P. Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing (HLT 05), Association for Computational Linguistics. Morristown, NJ, USA. (2005) 347–54.
[28] Serrano-Guerrero J, Olivas JA, Romero FP, Herrera-Viedma E. Sentiment analysis: a review and comparative analysis of web services. Inf Sci. (2015) 311:18–38.
[29] Rushdi-Saleh M, Martín-Valdivia M, Montejo-Ráez A, Ureña López L. Experiments with SVM to classify opinions in diferent domains. Expert Syst Appl. 38(12) (2011) 14799–804.
[30] Ye Q, Zhang Z, Law R. Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst Appl. 36(3) (2009) 6527–35.
[31] He Y, Zhou D. Self-training from labeled features for sentiment analysis. Inf Process Manag., 47(4) (2011)606–16.
[32] Xianghua F, Guo L, Yanyan G, Zhiqiang W. Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowl Based Syst. 37(2013) 186–95.
[33] Kim K, Lee J. Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction. Pattern Recogn., 47(2) (2014) 758–68.
[34] König AC, Brill E. Reducing the human overhead in text categorization. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining—KDD., 06. New York: ACM Press; (2006) 598–03.
[35] Abualigah, Laith & Alfar, Hamza & Shehab, Mohammad & Hussein, Alhareth., Sentiment Analysis in Healthcare: A Brief Review. 10.1007/978-3-030-34614-0_7., (2020).
[36] Zunic A, Corcoran P, Spasic I Sentiment Analysis in Health and Well-Being: Systematic Review JMIR Med Inform., 8(1) (2020) e16023 doi: 10.2196/16023PMID: 32012057PMCID: 7013658
[37] Balachandar S., Chinnaiyan R., Reliable Digital Twin for Connected Footballer. In: Smys S., Bestak R., Chen JZ., Kotuliak I. (eds) International Conference on Computer Networks and Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, 15(2019) Springer, Singapore
[38] Balachandar S., Chinnaiyan R., Centralized Reliability and Security Management of Data in the Internet of Things (IoT) with Rule Builder. In: Smys S., Bestak R., Chen JZ., Kotuliak I. (eds) International Conference on Computer Networks and Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, 15(2019) Springer, Singapore
[39] G. Sabarmathi and R. Chinnaiyan., Reliable Machine Learning Approach to Predict Patient Satisfaction for Optimal Decision Making and Quality Health Care, International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, (2019) 1489-1493
[40] G. Sabarmathi and R. Chinnaiyan., Investigations on big data features research challenges and applications, International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, (2017) 782-786
[41] G. Sabarmathi and R. Chinnaiyan.,, Big Data Analytics Framework for Opinion Mining of Patient Health Care Experience, Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, (2020) 352-357
[42] Sabarmathi G., Chinnaiyan R., Envisagation and Analysis of Mosquito-Borne Fevers: A Health Monitoring System by Envisagative Computing Using Big Data Analytics. In: Pandian A., Senjyu T., Islam S., Wang H. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018). ICCBI 2018. Lecture Notes on Data Engineering and Communications Technologies, 31 (2020) . Springer, Cham.
[43] R Chinnaiyan, S Somasundaram., Monte Carlo Simulation For Reliability Assessment of Component-Based Software Systems, i-Manager's Journal on Software Engineering, (2010).
[44] R Chinnaiyan, S Somasundaram., RELIABILITY ESTIMATION OF COMPONENT BASED SOFTWARE SYSTEMS THROUGH MARKOV PROCESS, International Journal of Mathematics, Computer Sciences and Information Technology.
[45] R.Chinnaiyan, S.Somasundaram., Evaluating the Reliability of Component-Based Software Systems, International Journal of Quality and Reliability Management., 27(1) (2010) 78-88.

Keywords
Patient Reviews, Machine Learning, Classification, ensemble, recommendation