| Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01040 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/e3sconf/202343001040 | |
| Published online | 06 October 2023 | |
Helpi – An Automated Healthcare Chatbot
1 Department of CSE (AIML), GRIET, Hyderabad, Telangana State, India
2 Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
3 KG Reddy College of Engineering & Technology, Hyderabad, India
* Corresponding author: karuna.griet@gmail.com
Due to technological advancements, the healthcare industry has witnessed the emergence of innovative solutions, and one such solution is the healthcare Chatbot. The primary objective of this paper is to create a healthcare Chatbot capable of offering medical assistance to patients. The healthcare Chatbot serves as an AI-based conversational program designed to assist both patients and healthcare providers. The proposed Chatbot, named “HELPI,” functions as a round-the-clock healthcare provider. It utilizes Natural Language Processing (NLP) and Machine Learning (ML) algorithms such as decision trees to analyse user-provided symptoms and accurately detect specific illnesses or diseases. Subsequently, it offers appropriate healthcare recommendations and suggests relevant medications. This broadens HELPI’s capability to address various healthcare-related concerns. In essence, HELPI aims to alleviate the burden on healthcare providers by providing an alternative platform for basic medical advice and support. The success of the HELPI Chatbot lays the foundation for future enhancements. Additional features, such as appointment scheduling, guidance on lifestyle modifications, and medication reminders, could be incorporated to further enhance the Chatbot’s functionality.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.