AI BASED SMART HEALTHCARE ASSISTANT WITH PREDICTIVE OUTBREAK MONITORING

Abstract

Abstract: Health-Doc is an Al-driven healthcare assistant and predictive outbreak monitoring system designed to assist users with health-related queries and provide early warnings of potential disease outbreaks. The system integrates advanced natural language processing (NLP) for realtime chatbot interaction, along with an ensemble of machine learning models, including ARIMA, LSTM, SIR, and PROPHET, to predict disease dynamics. By leveraging these technologies, Health-Doc not only offers users accurate health advice based on image and text inputs but also helps authorities and individuals stay informed about emerging health risks. The solution is scalable through API integration, allowing healthcare platforms to incorporate predictive analytics and intelligent response features directly into their services.

Specification

FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
PROVISIONAL/COMPLETE SPECIFICATION
(See section 10 and rule 13)

1. TITLE OF THE INVENTION -
Al based Smart Healthcare Assistant with predictive Outbreak Monitoring
2. APPLICANT(S)
(a) NAME: SRI SAI RAM ENGINEERING COLLEGE
(b) NATIONALITY: INDIAN
(c) ADDRESS: Sri Sai Ram Engineering College,
Sai Leo Nagar, West Tambaram,
Chennai - 600044
(a) NAME: Mr. K. JAYACHANDIRAN
(b) NATIONALITY: INDIAN
(c) ADDRESS: Department of Computer Science Engineering (Artificial
Intelligence and Machine Learning),
Sri Sai Ram Engineering College,
Sai Leo Nagar, West Tambaram,
Chennai-600044,
a) NAME: Mr. E. DINESH KUMAR
b) NATIONALITY: INDIAN
c) ADDRESS:
Department of Computer Science Engineering (Artificial
Intelligence and Machine Learning),
Sri Sai Ram Engineering College,
Sai Leo Nagar, West Tambaram,
Chennai-600044.
a) NAME: Mr. R. SHAMPRAKASH
b) NATIONALITY: INDIAN
c) ADDRESS: Department of Computer Science Engineering (Artificial
Intelligence and Machine Learning),
Sri Sai Ram Engineering College,
Sai Leo Nagar, West Tambaram.
Chennai-600044.

a) NAME: Dr. E. PRIYA
b) NATIONALITY: INDIAN
c) ADDRESS: Department of Computer Science Engineering (Artificial
Intelligence and Machine Learning), Sri Sai Ram Engineering College, Sai Leo Nagar, West Tambaram, Chennai-600044.

3. PREAMBLE TO THE DESCRIPTION
PROVISIONAL
The-following specification describer invention?
COMPLETE
The following specification particularly describes the invention and the manner in which it is to be performed
4. DESCRIPTION (Description shall start from next page) Annexed along with this form.
5. CLAIMS (not applicable for provisional specification. Claims should start with the preamble - "I/We claim" on separate page) Annexed along with this form.
6. DATE AND SIGNATURE (to be given on the last page of specification) Given at the end of specifications.
7. ABSTRACT OF THE INVENTION (to be given along with complete specification on the separate page)
Given at the end of specifications.

Introduction :
The Health-Doc project is designed as a super app that integrates Al-powered healthcare assistance and predictive outbreak monitoring. The core functionality of the system revolves around a chatbot that helps users with real-time medical advice by analyzing images of wounds or infections and responding to general health-related queries. Additionally, it forecasts potential disease outbreaks, such as pandemics or epidemics, by processing health data and generating alerts for both users and public health authorities. Through seamless integration with messaging platforms like WhatsApp and Telegram, Health-Doc provides accessible, instant healthcare services and alerts to users, bridging the gap between early diagnostics and professional healthcare intervention.

Field of Invention :
• Al-based healthcare diagnostics, focusing on image analysis for visible health conditions (e.g., wounds and infections).
• Real-time disease outbreak prediction using machine learning models and public health data.
• Integration of health advisory and predictive systems with popular communication platforms for user-friendly access.
• Development of a health super app that can assist with both personal healthcare and public health management.

Background of Invention : Revolutionizing Healthcare Assistance through Al-Driven Diagnostics and Predictive Analytics
• Problem Statement:
Millions of people, especially in developing countries, die due to lack of timely and quality healthcare. A significant gap exists in healthcare accessibility, especially in rural and underserved areas, where professional medical care is often delayed or unavailable.
• Existing Solutions:
While there are many chatbot systems providing basic health information, they largely rely on text input and do not incorporate Al-driven image diagnostics. Most current models also fail to predict health trends or disease outbreaks in real-time.
• Invention's Purpose:
The Health-Doc project aims to address these gaps by offering Al-based image diagnostics and predictive outbreak monitoring through a chatbot that is integrated into everyday messaging platforms. By analyzing health conditions and generating outbreak alerts, it allows users tp take early action and access nearby healthcare resources.

• Need for Innovation:
Current healthcare chatbots lack the ability to accurately diagnose health conditions based- on images, and few systems combine personalized health advice with large-scale outbreak predictions. This invention bridges that gap, providing both individual health assistance and public health forecasting, making healthcare more accessible and proactive.

Summary:
• The Health-Doc project is an Al-powered healthcare assistant that diagnoses visible health issues such as wounds and infections by analyzing images uploaded by users.
• It integrates predictive outbreak monitoring to forecast potential pandemics, epidemics, or endemics based on health data and user inputs.
• The chatbot is accessible through popular messaging platforms like WhatsApp and Telegram, providing instant health advice and proactive disease alerts to users.
• The system combines personal health diagnostics with public health management, allowing
users to take early action based on Al-driven recommendations.
• Future development includes expansion into a comprehensive mobile app with additional features like telemedicine, medicine delivery, and health monitoring.

Objectives :
1. Provide a comprehensive Al-based healthcare assistant that offers diagnostic support through image analysis of visible health conditions such as wounds or infections.
2. Enable real-time disease outbreak forecasting using advanced machine learning models, ensuring timely alerts for users and authorities.
3. Seamlessly integrate with popular messaging platformsto provide users with immediate access to healthcare advice and proactive health alerts.
4. Bridge the gap between early diagnosis and professional medical intervention by connecting users to nearby healthcare services when necessary.
5. Expand the system's functionality to include telemedicine, medicine delivery, and personalized health monitoring, making it a one-stop healthcare solution.
Brief Description of the Drawings :
Fig 1 - Disease Dynamics Engine:

The Disease Dynamics Engine leverages advanced predictive models, including ARIMA, SIR, PROPHET, and LSTM, to analyze historical health data and detect trends in disease spread. These models work in an ensemble approach, combining their strengths to improve prediction accuracy and provide timely forecasts for potential disease outbreaks, aiding in proactive health management.

Fig 2 - Query Processing Engine:
The Query Processing Engine uses Natural Language Processing (NLP) to interpret and respond to user queries effectively. By parsing questions, It determines the intent and retrieves answers from a health information database. This engine includes a similarity search mechanism, ensuring the retrieval of the most relevant responses, thus enhancing the accuracy and relevance of health assistance provided to users.

Fig 3 - Overall Design of the Proposed Solution :
This diagram provides an overview of how user interaction is handled. A user inputs a query into the mobile app, which is then processed by the Disease Dynamics Engine. The query is sent to the Query Processing Engine, which uses NLP to process the query, search for similar questions, and retrieve the most appropriate response. Finally, the system returns an Al-based response to the user.

Fig 4 - Business Model Canvas:
This diagram represents the business model of the Health-Doc application. It showcases how the platform extends beyond the web app by offering API access to developers, enabling them to integrate advanced disease analysis capabilities into their platforms. The flow illustrates the integration of the API, processing through J SON, and the wide compatibility with various platforms such as mobile devices, web applications, and wearables.

Detailed Explanation of the Development with Relevance to the Architecture Diagram:
The architecture of the Health-Doc solution revolves around two key components that work together to provide predictive disease analysis and health assistance through Al chatbot integration.

1. Disease Dynamics Engine:
• This core component handles the disease prediction functionalities of the system. Various statistical and machine learning models like ARIMA, LSTM, SIR, and PROPHET are employed to analyze historical and real-time health data. These models work together in an ensemble setup, enhancing the prediction accuracy for disease outbreaks.
• The engine takes inputs such as patient symptoms, disease prevalence data, and geographical information to provide dynamic forecasts on potential health risks. The ensemble model aggregates results from the individual models to provide the best possible prediction.

2. Query Processing Engine:
• This engine interacts with users directly, enabling real-time health assistance. When a user submits a query through the mobile application, the engine uses advanced NLP algorithms to interpret the question. The system incorporates vectorization methods to search for similar previously answered questions in its database,
• The engine is designed to understand context and provide Al-driven responses that address both health-related inquiries and prediction-related questions. It effectively bridges the gap between user queries and medical insights, ensuring that accurate and context-aware responses are provided.

3. Mobile Application Integration:
• The mobile app acts as the user interface, allowing users to submit health-related questions and receive disease predictions. The app communicates directly with the Disease Dynamics Engine and the Query Processing Engine. This ensures a smooth experience for users, with rapid Al responses and real-time health insights.
• The app also includes features for outbreak monitoring and notifications, alerting users about potential disease risks in their vicinity based on the data processed by the Disease Dynamics Engine.

4. Al and API Connectivity:
• The solution supports external integration through APIs, allowing developers to harness the disease analysis engine's capabilities within their platforms. This creates flexibility, enabling the Health-Doc solution to be incorporated into various healthcare systems and applications, extending its impact beyond standalone usage.

CLAIMS : t

We Claim:
1. Al-based system that processes user health-related queries using advanced natural language processing (NLP) and provides contextually relevant responses based on both text and image inputs.

2. Predictive disease dynamics engine using ah ensemble of machine learning models (ARIMA, LSTM, SIR, and PROPHET) to forecast potential disease outbreaks.

3. Query processing engine that ensures accurate responses by utilizing similarity search and
vectorization techniques to match user queries with a health information database.

4. API integration, enabling third-party developers to incorporate predictive health analysis and real-time response capabilities into their healthcare platforms.

5. Mobile and web application interface for users to interact with the system, receiving predictions and real-time notifications regarding health risks and potential outbreaks.

6. Real-time outbreak monitoring and alert system that processes health data.and notifies users and authorities when a disease outbreak is predicted.

DATE:
SIGNATURE:
NAME: Dr.J.RAJA,
Principal,
Sri Sairam Engineering College.

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