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Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts (2403.09762v1)

Published 14 Mar 2024 in cs.CL, cs.AI, cs.HC, cs.LG, and cs.NE

Abstract: This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare, with a particular focus on the incorporation of Natural Language Processing and deep learning technologies. We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes based on textual information derived from clinical narratives, patient feedback on medications, and online health discussions. The review demonstrates noteworthy progress in the precision of algorithms used for sentiment classification, the prognostic capabilities of AI models for neurodegenerative diseases, and the creation of AI-powered systems that offer support in clinical decision-making. Remarkably, the utilization of AI applications has exhibited an enhancement in personalized therapy plans by integrating patient sentiment and contributing to the early identification of mental health disorders. There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures. Nevertheless, the potential of AI to revolutionize healthcare practices is unmistakable, offering a future where healthcare is not only more knowledgeable and efficient but also more empathetic and centered around the needs of patients. This investigation underscores the transformative influence of AI on healthcare, delivering a comprehensive comprehension of its role in examining emotional content in healthcare texts and highlighting the trajectory towards a more compassionate approach to patient care. The findings advocate for a harmonious synergy between AI's analytical capabilities and the human aspects of healthcare.

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References (29)
  1. N. Sanglerdsinlapachai, A. Plangprasopchok, T. B. Ho, and E. Nantajeewarawat, “Improving sentiment analysis on clinical narratives by exploiting UMLS semantic types,” Artificial Intelligence in Medicine, vol. 113, p. 102033, Mar. 2021.
  2. Y. Shao, Y. Cheng, S. J. Nelson, P. Kokkinos, E. Y. Zamrini, A. Ahmed, and Q. Zeng-Treitler, “Hybrid Value-Aware Transformer Architecture for Joint Learning from Longitudinal and Non-Longitudinal Clinical Data,” Journal of Personalized Medicine, vol. 13, no. 7, p. 1070, Jun. 2023.
  3. B. N. Hiremath and M. M. Patil, “Enhancing optimized personalized therapy in clinical decision support system using natural language processing,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, pp. 2840–2848, 06 2022. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S1319157820303268
  4. J. J. Zhu, Y.-C. Chang, C.-H. Ku, S. Y. Li, and C.-J. Chen, “Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning,” Journal of Business Research, vol. 129, pp. 860–877, 05 2021. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0148296320307505
  5. A. Vij and J. Pruthi, “An automated Psychometric Analyzer based on Sentiment Analysis and Emotion Recognition for healthcare,” Procedia Computer Science, vol. 132, pp. 1184–1191, 2018.
  6. S. M. Joseph, S. Citraro, V. Morini, G. Rossetti, and M. Stella, “Cognitive network neighborhoods quantify feelings expressed in suicide notes and reddit mental health communities,” Physica A: Statistical Mechanics and its Applications, vol. 610, p. 128336, 01 2023. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0378437122008949
  7. M. A. Al-Garadi, S. Kim, Y. Guo, E. Warren, Y.-C. Yang, S. Lakamana, and A. Sarker, “Natural language model for automatic identification of intimate partner violence reports from twitter,” Array, vol. 15, p. 100217, 09 2022. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S2590005622000625
  8. X. Yang, R. McEwen, L. R. Ong, and M. Zihayat, “A big data analytics framework for detecting user-level depression from social networks,” International Journal of Information Management, vol. 54, p. 102141, 10 2020. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0268401219313325
  9. S. Khan and S. Alqahtani, “Hybrid machine learning models to detect signs of depression,” Multimedia Tools and Applications, Oct. 2023.
  10. D. Peng, M. Zhou, C. Liu, and J. Ai, “Human–Machine dialogue modelling with the fusion of word- and sentence-level emotions,” Knowledge-Based Systems, vol. 192, p. 105319, Mar. 2020.
  11. P. Kaur, A. K. Malhi, and H. S. Pannu, “Sentiment analysis of linguistic cues to assist medical image classification,” Multimedia Tools and Applications, 09 2023. [Online]. Available: https://link.springer.com/10.1007/s11042-023-16538-9
  12. S. Liang, X. Chen, J. Ma, W. Du, and H. Ma, “An improved double channel long short-term memory model for medical text classification,” Journal of Healthcare Engineering, vol. 2021, pp. 1–8, 02 2021. [Online]. Available: https://www.hindawi.com/journals/jhe/2021/6664893/
  13. S. Jiménez-Zafra, M. T. Martín-Valdivia, M. D. Molina-González, and L. A. Ureña-López, “How do we talk about doctors and drugs? sentiment analysis in forums expressing opinions for medical domain,” Artificial Intelligence in Medicine, vol. 93, pp. 50–57, 01 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0933365717305407
  14. A. Mondal, E. Cambria, D. Das, A. Hussain, and S. Bandyopadhyay, “Relation extraction of medical concepts using categorization and sentiment analysis,” Cognitive Computation, vol. 10, no. 4, pp. 670–685, 08 2018. [Online]. Available: http://link.springer.com/10.1007/s12559-018-9567-8
  15. J. Carrillo-de Albornoz, J. Rodríguez Vidal, and L. Plaza, “Feature engineering for sentiment analysis in e-health forums,” PLOS ONE, vol. 13, no. 11, p. e0207996, 11 2018. [Online]. Available: https://dx.plos.org/10.1371/journal.pone.0207996
  16. S. Bromuri, D. Zufferey, J. Hennebert, and M. Schumacher, “Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms,” Journal of Biomedical Informatics, vol. 51, pp. 165–175, 10 2014, pMID: 24879897.
  17. I. Straw and C. Callison-Burch, “Artificial intelligence in mental health and the biases of language based models,” PloS One, vol. 15, no. 12, p. e0240376, 2020.
  18. T. D. Hadley, R. W. Pettit, T. Malik, A. A. Khoei, and H. M. Salihu, “Artificial intelligence in global health -a framework and strategy for adoption and sustainability,” International journal of MCH and AIDS, vol. 9, no. 1, pp. 121–127, 2020.
  19. F. Benrouba and R. Boudour, “Emotional sentiment analysis of social media content for mental health safety,” Social Network Analysis and Mining, vol. 13, no. 1, 2023.
  20. A. Brezulianu, A. Burlacu, I. V. Popa, M. Arif, and O. Geman, ““Not by our feeling, but by other’s seeing”: Sentiment analysis technique in Cardiology—An exploratory review,” Frontiers in Public Health, vol. 10, 2022.
  21. D. Gan, J. Shen, M. Xu, and Z. Stamenkovic, “Adaptive learning emotion identification method of short texts for online medical knowledge sharing community,” Computational Intelligence and Neuroscience, vol. 2019, 2019.
  22. B. Inkster, S. Sarda, and V. Subramanian, “An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study,” JMIR mHealth and uHealth, vol. 6, no. 11, 2018.
  23. R. Liu, Q. Liu, H. Zhu, and H. Cao, “Multistage deep transfer learning for EmIoT-Enabled Human–Computer interaction,” IEEE Internet of Things Journal, vol. 9, no. 16, pp. 15 128–15 137, Aug. 2022.
  24. A. R. Rahmanti, H.-C. Yang, B. S. Bintoro, A. A. Nursetyo, M. S. Muhtar, S. Syed-Abdul, and Y.-C. J. Li, “SlimMe, a chatbot with artificial empathy for personal weight management: System design and finding,” Frontiers in Nutrition, vol. 9, 2022.
  25. K. J. Rechowicz and C. A. Elzie, “The use of artificial intelligence to detect students’ sentiments and emotions in gross anatomy reflections,” Anatomical Sciences Education, 2023.
  26. Y. Shan, M. Ji, W. Xie, K.-Y. Lam, and C.-Y. Chow, “Public trust in artificial intelligence applications in mental health care: Topic modeling analysis,” JMIR Human Factors, vol. 9, no. 4, 2022.
  27. A. Yadollahi, A. G. Shahraki, and O. R. Zaiane, “Current state of text sentiment analysis from opinion to emotion mining,” ACM Computing Surveys, vol. 50, no. 2, 2017.
  28. V. P. R. and P. K. Nag, “Text-based emotion recognition using contextual phrase embedding model,” Multimedia Tools and Applications, Mar. 2023.
  29. P. K. Nag and V. Priya R, “Contextual BI-Directional Attention Flow With Embeddings From Language Models: A Generative Approach to Emotion Detection,” in Advances in Robotics - 5th International Conference of The Robotics Society, ser. AIR2021.   New York, NY, USA: Association for Computing Machinery, Jun. 2021, pp. 1–6.
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Authors (4)
  1. Prashant Kumar Nag (1 paper)
  2. Amit Bhagat (1 paper)
  3. R. Vishnu Priya (1 paper)
  4. Deepak kumar Khare (1 paper)