Authors: Prashant Kumar Nag, Amit Bhagat, R. Vishnu Priya, Deepak kumar Khare

Published on: March 14, 2024

Impact Score: 8.2

Arxiv code: Arxiv:2403.09762

Summary

• What is new: This research documents significant advancements in using AI for emotion assessment in healthcare texts, showcasing improvements in sentiment analysis accuracy, predicting outcomes for neurodegenerative diseases, and supporting clinical decisions through AI.
• Why this is important: The need to enhance the precision of sentiment analysis in healthcare texts and to leverage these insights for improving patient care and outcomes.
• What the research proposes: Employing Natural Language Processing and deep learning technologies to analyze emotions in healthcare-related texts, aiming to augment clinical decision-making and personalized therapy plans.
• Results: Increased accuracy in sentiment classification, improved predictive power for patient outcomes, and better support for personalized treatment plans and early detection of mental health issues.

Technical Details

Technological frameworks used: Natural Language Processing, deep learning

Models used: Sentiment analysis models, predictive models for neurodegenerative diseases

Data used: Clinical narratives, patient feedback on medications, online health discussions

Potential Impact

Healthcare providers, mental health services, pharmaceutical companies, medical insurance companies

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