Artificial intelligence-based system and method for predicting acute exacerbation of asthma, chronic obstructive pulmonary disease

Abstract

The present disclosure introduces an AI-based system (100) and method for predicting the risk of acute exacerbation of chronic obstructive pulmonary disease (COPD) over three months. The system interfaces with consumer devices through REST APIs, ensuring secure communication via HTTPS. Client modules collect and pre-process raw data for integrity and compatibility. This data is then sent to the server (104) which is then transmitted to Input module, the input module (112) authenticates and validates it. The module processes personal details, respiratory history, allergies, vaccination and medication history, and demographic data for air quality analysis. The prediction module (116) uses a CART Model with 90% accuracy to evaluate COPD risk. Based on predefined thresholds, the clinical pathway module (118) and risk prediction response module (120) offer personalized treatment protocols and clinical decision support. The system complies with ISO 13485 standards for safety and reliability.

Description

4. DESCRIPTION:Field of the invention:

[0001] The present disclosure generally relates to the technical field of healthcare and, in specific, relates to an artificial intelligence (Al)-based system and method to predict the risk of acute exacerbation of Chronic Obstructive Pulmonary Disease (COPD) and related events in the next three months.Background of the invention:

[0002] Asthma is a heterogenous disease, usually characterized by chronic airway inflammation. It is defined the history of respiratory symptoms such as wheeze, shortness of breath, chest tightness and cough that vary over time and in intensity, together with variable expiratory airflow limitation [Ref GINA 2015]. COPD is a common preventable and treatable disease characterized by persistent airflow limitation that is usually progressive and associated with enhanced chronic inflammatory responses in the airways and the lungs to noxious particles and gases.

[0003] Exacerbations and comorbidities contribute to the overall severity in individual patients [Ref GOLD 2015]. The management of chronic respiratory diseases, particularly Chronic Obstructive Pulmonary Disease (COPD) and asthma, presents a significant healthcare challenge due to the frequent occurrence of acute exacerbations. These exacerbations, often triggered by environmental factors such as air pollution, pose a substantial risk to patients, leading to increased morbidity, hospital admissions, and healthcare costs.

[0004] Traditional methods for predicting acute exacerbations in COPD and asthma rely on static risk factors and historical data, often failing to account for dynamic changes in a patient's health status and environmental conditions. Furthermore, existing prediction models generally do not integrate dynamic clinical features and medication usage patterns, limiting their effectiveness in providing timely interventions. Previous tools and models for predicting acute exacerbations of COPD and asthma, such as the BODE Index, COPD Assessment Test (CAT), Clinical COPD Questionnaire (CCQ), St. George's Respiratory2Questionnaire (SGRQ), GOLD Classification System, and various Exacerbation Prediction questionnaire and assessments, have several limitations.

[0005] These tools primarily depend on fixed demographic factors and baseline clinical parameters without adequately considering fluctuating environmental factors like air quality and the patient's medication History. They often rely on static assessments of patient symptoms, lung function, and quality of life, using patient-reported data and historical trends. Furthermore, most of these models have been developed using data from populations outside of India, potentially limiting their applicability and accuracy for the Indian population due to genetic, environmental, and healthcare system differences.

[0006] As a result, these models provide broad categorizations and generic treatment guidelines, lacking the capability to offer highly personalized risk assessments and address specific patient needs. Additionally, they do not feature a comprehensive feedback loop to continuously update and refine risk predictions based on patient outcomes, further limiting their accuracy and utility. The Al COPD Risk Score, developed by Apollo Hospitals, provides a sophisticated method for disease prediction by utilizing machine learning algorithms. This model is trained using clinical features, medication history, and air quality data, enabling it to recognize complex patterns and interactions that may be missed by traditional scoring systems.

[0007] The model takes into account various factors such as Age, Gender, Smoking status, Tobacco Consumption, Medical history, Breathlessness, Productive cough, Respiratory Medication, respiratory rate, Pulse rate Change, Spo2, wheeze on chest Auscultation, Air Quality Index and Additional data on Allergies. With a comprehensive dataset of more than 800,000 medication purchase records and ethically acquired deanonymized data specific to the Indian population, the Al COPD tool considerably boosts predictive precision and relevance in the Indian context.

[0008] Moreover, the Al COPD tool incorporates a comprehensive risk assessment framework that considers various clinical and environmental factors, providing a holistic view of the patient's risk profile by predicting risk of acute exacerbation of Asthma, COPDrelated Events in next 3 months. It also includes an integrated Clinical Decision Support System (CDSS) that guides clinicians on the next best actions, such as lab investigations, imaging, and treatment recommendations, tailored to the individual patient's needs. By offering a dynamic, personalized approach to risk assessment and management, the Al COPD Risk Score represents a significant advancement in the prevention and management of acute exacerbations in COPD and asthma.Objectives of the invention:

[0009] The primary objective of the invention is to provide an artificial intelligence-based system to assess individual risk of acute exacerbations of Asthma, COPD and related events within the next 3 months.

[0010] Another objective of the invention is to employ advanced machine learning algorithms integrating clinical features, medication history, and environmental factors like Air quality data to enhance predictive accuracy.

[0011] The other objective of the invention is to support clinical decision-making processes by providing an Al predicted score and Risk Status, assisting physicians in determining the most appropriate patient care approach.

[0012] The other objective of the invention is to provide a risk assessment tool that incorporates machine learning models and incorporating wide array of data such as clinical features, Vaccination & medication history, and air quality data to ensure high accuracy.

[0013] The other objective of the invention is to facilitate early detection, prevention, and management of Acute Exacerbation of Asthma and COPD related events by employing the risk assessment tool across various healthcare settings such as Preventive Pulmonology Screening programs, Outpatient Clinics, Emergency Rooms, and Health Check Clinics.

[0014] Yet another objective of the invention is to develop a comprehensive model built on extensive Indian patient data, ensuring a more tailored and accurate risk assessment for Acute Exacerbation of COPD and Asthma-related events in the Indian population.

[0015] Further objective of the invention is to ensure continuous validation of the tool at different National & International Institutions to uphold its effectiveness and accuracy.Summary of the invention:

[0016] The present disclosure proposes an artificial intelligence (Al)-based system and method for predicting acute exacerbation of asthma and chronic obstructive pulmonary disease (COPD). The following presents a simplified summary in order to provide a basic understanding of some aspects of the claimed subject matter. This summary is not an extensive overview. It is not intended to identify key / critical elements or to delineate the scope of the claimed subject matter. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0017] In order to overcome the above deficiencies of the prior art, the present disclosure is to solve the technical problem to provide an Al-based system for predicting acute exacerbation of asthma and chronic obstructive pulmonary disease (COPD) related events.

[0018] According to one aspect, the invention provides an Artificial Intelligence (Al)-based system for predicting risk of acute exacerbation of asthma and chronic obstructive pulmonary disease (COPD) in next 3 months. In one embodiment herein, the server having a processor and a memory for storing one or more instructions executable by the processor. Additionally, the processor is configured to execute plurality of modules for performing multiple operations. The server is in communication with a user device through a network. The server is in communication with a database for storing and retrieving the patient's data and communicates with various modules.

[0019] The plurality of modules is configured to predict risk of acute exacerbation of asthma, chronic obstructive pulmonary disease (COPD) related events of at least one patient. The plurality of modules comprises client modules, an input module, processing module, prediction module, clinical pathway module, and risk prediction response module. In one embodiment herein, the user device includes at least one of, but not limited to, a computer, a smartphone, a laptop, and a tablet.

[0020] In one embodiment herein, the client modules is configured to perform multiple functions identifying and recommending probable organisms and their sensitive antibiotics at the point of care. The client modules comprise a data collection module, a standardization and pre-processing module and an API module. In one embodiment herein, the data collection module is configured to collect input data from various sources such as electronic medical records (EMRs), Data repositories and Databases.

[0021] In one embodiment herein, the standardization and pre-processing module is configured to standardize and pre-process the collected data to ensure its integrity and compatibility with the system's processing requirements. In one embodiment herein, the API module is configured to transmit the pre-processed data from the data collection module and the standardization and pre-processing module to the server via a network.

[0022] In one embodiment, the input module initiates authentication and validation of input data, ensuring compliance with required formats. It allows users to facilitate personal parameters, medical history, vaccination and medication history, demographic details for calculating air quality index, clinical parameters and lifestyle attributes and clinical examination parameters through the Application programming interface (API) or through web user interface on a user device.

[0023] In one embodiment, the input module is designed to facilitate a wide array of data. It includes the patient's medical history, focusing on history of respiratory health issues such as asthma, chronic obstructive pulmonary disease (COPD), asthma-COPD overlap, recurrent respiratory tract infections, other obstructive lung disease-related conditions, and known allergies like pollen, dust mites, animal dander, food, insect bites and stings, medicines, latex, and mould. The patient's vaccination history, specifically the type of COVID-19 vaccine received and the most recent dose, as well as the last influenza vaccine dose, if applicable, is also recorded. The module further collects the demographic details of the patient to calculate Air Quality Index.

[0024] Additionally, it captures clinical parameters and lifestyle attributes, including the patient's smoking and Tobacco Consumption history, any past diagnoses of Tuberculosis and heart disease, instances of progressive breathlessness during regular activities, exercise, and sleep, and occurrences of a productive cough. The data collected by input module is transmitted via the network to the server. The server receives the input data at data collection module and then it is sent to standardization and pre-processing modules and prepares it for processing.

[0025] In one embodiment, the API module is configured to receive the input data by utilizing APIs from other platforms and send results, thereby enhancing interoperability across healthcare environments. In one embodiment, the processing module transforms and standardizes data of at least one patient from the input module to meet the necessary input criteria for the subsequent analysis. In one embodiment, the prediction module is structured to categorize an individual's risk levels of having the acute exacerbation of COPD of at least one patient. The score is predicted using a machine learning model, notably the CART model, with an accuracy of 90% and above. In one embodiment, the clinical pathway module is designed to provide a recommended protocol of subsequent actions for at least one patient, based on the determined risk thresholds.

[0026] In one embodiment, the risk prediction response module presents the patient's risk score along with a clinical algorithm for next best actions based on the stratified risk of acute exacerbation of COPD through user interface of the user device. The user interface could be, but not limited to, a touchscreen, a display, and a screen. The stratification is into multiple risk levels: low risk (< 0.26), medium risk (0.26 - 0.31), and high risk (> 0.31). The risk prediction response module provides an overview including the patient's risk status (for example high, medium, low), Al score (ranging from 1 to 10), and a clinical algorithm with recommendations for diagnostic imaging, laboratory test, referrals, treatment goals, test follow up frequency, education, and revisit guidelines.

[0027] According to another aspect, the invention provides a method for predicting the risk of acute exacerbation of asthma and chronic obstructive pulmonary disease (COPD) related events. At one step, the data collection module collects the input data from various sourcessuch as electronic medical records (EMRs), Data repositories and Databases. At one step, the standardization and pre-processing module standardizes and pre-processes the collected data to ensure its integrity and compatibility with the system's processing requirements.

[0028] At one step, the API module transmits the pre-processed data from the data collection module and the standardization and pre-processing module to the server via the network. At one step, the input module receives the pre-processed data from the server to authenticate and validate, enabling users to facilitate a wide array of data such as personal parameters, medical history, vaccination, and medication history, demographic details for calculating air quality index, clinical parameters and lifestyle attributes and clinical examination parameters. Data collected by the input module is transmitted to processing module.

[0029] At one step, the processing module transforms and standardizes this input data to meet the necessary criteria for the subsequent analysis. At one step, the prediction module uses the CART model to categorize an individual's risk levels of acute exacerbation of asthma and chronic obstructive pulmonary disease (COPD) related events in the next 3 Months.

[0030] At one step, the clinical pathway module generates a recommended protocol of next based actions for at least one patient, based on the determined risk thresholds from the prediction module. Finally, the risk prediction response module presents the patient's risk status and Al-predicted score along with a clinical algorithm for the next best actions based on the stratified risk of acute exacerbation of asthma and chronic obstructive pulmonary disease (COPD) related Events. Throughout these steps, the system can be accessed on the user device, which is networked to a server, and it is integrated into an API interface to enable smooth communication, using HTTPS for secure communication.

[0031] Further, objects and advantages of the present invention will be apparent from a study of the following portion of the specification, the claims, and the attached drawings.Detailed description of drawings:

[0032] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate an embodiment of the invention, and, together with the description, explain the principles of the invention.

[0033] FIG. 1 illustrates a block diagram of an Al-based system for predicting chronic obstructive pulmonary disease (COPD), in accordance to an exemplary embodiment of the invention.

[0034] FIG. 2 illustrates a flowchart of a method of preparing Al-based system for predicting risk of Acute Exacerbation chronic obstructive pulmonary disease (COPD) related events, in accordance to an exemplary embodiment of the invention.

[0035] FIG. 3 illustrates the system architecture (300) for a method devised to develop and deploy the Al-based chronic obstructive pulmonary disease (COPD) risk assessment system, in accordance to an exemplary embodiment of the invention.Detailed invention disclosure:

[0036] Various embodiments of the present invention will be described in reference to the accompanying drawings. Wherever possible, same or similar reference numerals are used in the drawings and the description to refer to the same or like parts or steps.

[0037] The present disclosure has been made with a view towards solving the problem with the prior art described above, and it is an object of the present invention to provide an Al- based system that determines a patient's risk of acute exacerbation asthma, COPD related events, thereby providing suggestions using a clinical decision support system (CDSS).

[0038] According to one exemplary embodiment of the invention, FIG. 1 refers to a block diagram of an Al-based system 100 for predicting chronic obstructive pulmonary disease (COPD). In one embodiment herein, the Al-based system 100 for predicting the risk of acute exacerbation of asthma, chronic obstructive pulmonary disease (COPD), and related events.9The system supports integration with the user device through various approaches, one of the methods is the use of REST APIs. This integration method ensures seamless operation and generally employs HTTPS for secure communication.

[0039] The Al-based chronic obstructive pulmonary disease (COPD) risk assessment system 100 can be accessed on a user device that is connected to a server 104 via a network 102. The server 104 having a processor 106 and a memory 108 for storing one or more instructions executable by the processor 106. The server 104 is linked to a database 110 for efficient data management. The network 102 includes, but is not limited to, Wi-Fi, Bluetooth, a wireless local area network (WLAN) and internet connection, and radio communication. In some embodiments, the user device on which the Al-based system 100 can be accessed could be a touchscreen and a non-touchscreen and adopted to run on any type of OS, such as iOS, Windows, Android, Unix, Linux and others.

[0040] The processor 106 is configured to execute plurality of modules for performing multiple operations. The server 104 is in communication with a user device through a network 102. The server 104 is in communication with a database 110 for storing and retrieving the patient's data. The plurality of modules is configured to predict risk of acute exacerbation of asthma, chronic obstructive pulmonary disease (COPD) related events of at least one patient.

[0041] The server 104 could be operated as a single computer, which can be a hardware and software server, a workstation, a desktop, a laptop, a tablet, a mobile phone, a mainframe, a supercomputer, a server farm, and so forth. Within the system 100, a plurality of modules is executed by the processor 106. These modules include client modules, an input module 112, a processing module 114, a prediction module 116, a clinical pathway module 118, and a risk prediction response module 120. In one embodiment herein, the client modules is configured to perform multiple functions identifying and recommending probable organisms and their sensitive antibiotics at the point of care. The client modules comprise a data collection module 122, a standardization and pre-processing module 124 and an API module 126.

[0042] In one embodiment herein, the data collection module 122 is configured collect the input data from various sources such as electronic medical records (EMRs), Data repositories and Databases through a data collection module 122. In one embodiment herein, the standardization and pre-processing module 124 is configured to standardize and pre-processes the collected data to ensure its integrity and compatibility with the system's processing requirements. In one embodiment herein, the API module 126 is configured to transmit the pre-processed data from the data collection module 122 and the standardization and pre-processing module 124 to the server 104 via the network.

[0043] In one embodiment, the input module 112 receives the pre-processed data from the server 104 to authenticate and validate, ensuring compliance with required formats. It allows users to facilitate variety of information, including their personal details, medication list, vaccination history, Life style History and clinical examination attributes. Personal variables such as age, gender as well as respiratory health history such as asthma, COPD, and other lung diseases, along with known allergies. It records the patient's COVID-19 and influenza vaccination history and current location for air quality analysis.

[0044] Additionally, it captures lifestyle and clinical examination parameters like smoking habits, past diagnosis of Tuberculosis and heart disease, incidents of breathlessness, and instances of a productive cough. The medication list can be divided into at least four categories. The first category may include medications such as cough suppressants, mucolytic agents, anti-histamines, and anti-allergens like ketotitfen, ebastine, montelukast azelastine, and certrizine. The second category may comprise inhalers like Budesonide, Formoterol, Ipratropium, (levo) Salbutamol, Salmeterol, Tiotropium, and Rotahalers. The third category may encompass Bronchodilators, Lung Surfactants, Nebulizers like Acetylcysteine, Aminophyline, and Phospholipid fraction. The fourth category may involve medications that impact the respiratory system, such as antibiotics. The validated data is transmitted to processing module.

[0045] In one embodiment, the processing module 114 transforms and standardizes data from the input module 112 to meet the necessary input criteria for the subsequent analysis.

[0046] In one embodiment, the prediction module 116 is designed to categorize the risk of acute exacerbation of COPD and related incidents. The score is predicted using a machine learning model, notably the cart model, which has shown an accuracy rate of 90% and above. In one embodiment, the clinical pathway module 118 is designed to provide a personalized protocol of subsequent actions for at least one patient, based on the determined risk thresholds.

[0047] In one embodiment, the risk prediction response module 120 presents the patient's risk score along with a clinical algorithm for next best actions based on the stratified risk of acute exacerbation of COPD. The stratification is into multiple risk levels: low risk (< 0.26), medium risk (0.26 - 0.31), and high risk (> 0.31). The risk prediction response module 120 provides an overview including the patient's risk status (e.g., high, medium, low), Al score (e.g., 1 to 10), and a clinical algorithm with recommendations for diagnostic tests, referrals, treatment goals, educational materials, and revisit guidelines. The medical professional utilizes the generated data with the clinical judgment for deciding the best course of action for the patient.

[0001] According to another embodiment of the invention, FIG. 2 refers to a flowchart 200 of a method for predicting the risk of acute exacerbation of chronic obstructive pulmonary disease (COPD) and related Events in the patient using Al-based system 100. At step 202, the data collection module 122 collects the input data from various sources such as electronic medical records (EMRs), Data repositories and Databases. At step 204, the standardization and pre-processing module 124 standardizes and pre-processes the collected data to ensure its integrity and compatibility with the system's processing requirements.

[0048] At step 206, the API module 126 transmit the pre-processed data from the data collection module 122 and the standardization and pre-processing module 124 to the server 104 via the network 102. At step 208, the input module 112 receive the pre-processed data from the server 104 to authenticate and validate of input data, ensuring compliance with required formats.

[0049] It allows users to facilitate data related to personal parameters, medical history, Vaccination and medication history, demographic data for calculating Air quality Index, Clinical parameters and lifestyle attributes and Clinical examination parameters of at least one patient, thereby collecting the data of the patient through a Application programming interface (API) or through web user interface on a user device.

[0050] At step 210, the processing module 114 standardizes the collected data of the patient to fit the necessary input criteria for subsequent analysis. At step 212, the prediction module 116 applies the CART machine learning model to categorize the patient's risk of acute exacerbation of chronic obstructive pulmonary disease (COPD) and related events of the patient through a machine learning model based on the patient's data analysed through the processing module 114. At step 214, the clinical pathway module 118 creates the personalized protocol of subsequent actions based on determined risk levels. At step 216, the risk prediction response module displays the patient's risk status i.e. high risk, medium risk, low risk, Al predicted score and provides recommended protocol based on the risk threshold and risk status.

[0051] Numerous advantages of the present disclosure may be apparent from the discussion above. In accordance with the present disclosure, an artificial intelligence (Al)- based system for predicting chronic obstructive pulmonary disease (COPD) is disclosed. The proposed Al-based system 100 determines a patient's risk score based on the patient's data, thereby providing suggestions using a clinical decision support system (CDSS). The proposed Al-based system 100 generates personalized risk assessments based on clinical characteristics and environmental parameters like air quality index.

[0052] The proposed invention provides the Al-based system to assess the risk of acute exacerbation of Asthma, chronic obstructive pulmonary disease (COPD) and related Events 100 provides comprehensive and holistic risk assessment. The Al-based system 100 assists in providing recommendations based on stratified risk for Acute Exacerbation of COPD through an integrated clinical decision support tool.

[0053] The proposed Al-based system 100 for predicting risk of acute exacerbation of Asthma, chronic obstructive pulmonary disease (COPD) and related Events is expected to undergo additional development. This involves enriching data with larger datasets for greater accuracy, increasing features to cover a broader range of risk variables, and improving integration with clinical decision support systems (CDSS). The proposed Al-based system 100 will also be continuously validated across several universities to assure its dependability, and user feedback will be used to make required modifications and enhancements.

[0054] In accordance with an exemplary embodiment of the present invention, FIG. 3 provides a schematic depiction of the system architecture 300 for a method devised to develop and deploy the Al-based chronic obstructive pulmonary disease (COPD) Risk Assessment Tool. The diagram highlights a sequence of operations that are integral to the system's development and deployment.

[0055] As depicted in step 302, we start with the data sourcing phase where information is collected from various sources such as but not limited to patient health records, Medication purchase data and Respiratory Allergies from 314 Electronic Medical Records (EMR) & servers. Additional sources include clinical knowledge bases 316 like Standard Operating Protocols (SOPs), literature from research papers, journals, and consultations with clinicians 318.

[0056] In step 304, we move to the data ingestion phase. Here, data is collected and put into a centralized database 320. This data then traverses through a data pipeline 322 and is stored in a Data Repository 324 in structured formats. This phase is crucial for organizing the data and preparing it for further pre-processing stages. In step 306, we include the data preprocessing phase, which encompasses metadata management 326, ETL processes 328, and data transformation and harmonization 330. These processes improve data quality and consistency, setting the stage for subsequent analysis and model training.

[0057] In step 308, when it comes to data analysis for COPD risk identification, we utilize a variety of statistical tools 308. Propensity matching 332 unveils hidden patterns, whiledescriptive statistics 334 provide a summarized overview of the data. Correlation coefficients 336 establish relationships between various risk factors, and odds and hazard ratios 338 compare the possibility of outcomes. KM plots and survival charts 340 visualize time-to-event data, and visualization tools 342 simplify complex data representation. These tools, in concert, contribute to feature selection and model building of the COPD risk assessment system.

[0058] In step 310, we move towards model development, employing a Classification and Regression Tree (CART) model to predict the likelihood of Acute Exacerbation of COPD- related events in next 3 months. The CART model is chosen for its ability to handle complex interactions and non-linear relationships among the clinical features, such as age, gender, smoking status, tobacco consumption, medical history, breathlessness, productive cough, respiratory medication, respiratory rate, pulse rate change, SpO2, wheeze on chest Auscultation, Air Quality Index, and Additional data on Allergies.

[0059] By training the model with domain insights, data insights, and observing various iterations of the model performance, we achieve an optimal predictive performance. In step 312, we involve the deployment of the model for integration and usage through REST API 352 protocols. This architecture involves using an API Management Service 344 and an application service resource 350 for model inference. The model inference code is developed in Python programming language 354.

[0060] All the resources are hosted in a Virtual Private Network 348 securely. The API service 346 acts as the interface for the REST API, facilitating communication between the API Management Service and the web application. The API Management Service 344 includes storage capabilities 358 for securely managing and storing model artifacts, data inputs, and results. In one such application of the integration, this model inference API is integrated into a web application code 356 to be deployed and used as a Web Application 360.

[0061] This web application serves as a user-friendly platform for users and clinicians, enabling them to input data and obtain COPD risk scores. Therefore, this phase makes themodel operational and accessible, facilitating the prediction of COPD-related risk. Furthermore, the proposed Al-based system 100 will be continually updated to guarantee compliance with changing legislation and recommendations. As a result, while the proposed Al-based system 100 is quite successful, ongoing development intends to refine and improve its capabilities even more.

[0062] It will readily be apparent that numerous modifications and alterations can be made to the processes described in the foregoing examples without departing from the principles underlying the invention, and all such modifications and alterations are intended to be embraced by this application.16

Claims

5. CLAIMS: l / We Claim:

1. An artificial intelligence (Al) based system (100) for predicting the risk of acute exacerbation of asthma, and chronic obstructive pulmonary disease (COPD)-related events, comprising: a server (104) having a processor (106) and a memory (108) for storing one or more instructions executable by the processor (106), wherein the processor (106) is configured to execute plurality of modules for performing multiple operations, wherein the server (104) is in communication with a user device through a network (102), wherein the server (104) is in communication with a database (110) for storing and retrieving the patient's data, wherein the plurality of modules is configured to predict risk of acute exacerbation of asthma, chronic obstructive pulmonary disease (COPD) related events of at least one patient, wherein the plurality of modules comprises: client modules are configured to perform multiple functions for predicting risk of acute exacerbation of asthma, chronic obstructive pulmonary disease (COPD) related events of at least one patient, wherein the client modules comprises: a data collection module (122) configured to collect input data from various sources such as electronic medical records (EMRs), Data repositories and Databases ; a standardization and pre-processing module (124) configured to standardize and pre-process the collected data to ensure its integrity and compatibility with the system's processing requirements; an API module (126) configured to transmit the pre-processed data from the data collection module (122) and the standardization and pre-processing module (124) to the server (104) via a network (102);an input module (112) configured to receive the pre-processed data from the server (104) to authenticate and validate input data, ensuring compliance with required formats from various sources, and facilitating the collection of data related to personal parameters, medical history, lifestyle attributes, and clinical examination parameters through an Application programming interface (API) or through web user interface on a User device, with data transmitted via the network (102) to the server (104); a processing module (114) configured to transform and standardize the input data to meet the necessary input criteria for subsequent analysis; a prediction module (116) configured to categorize an individual's risk levels of having an acute exacerbation of COPD and related events using a machine learning model, notably the CART model, with an accuracy rate of 90% and above; a clinical pathway module (118) configured to provide a personalized protocol of subsequent actions for at least one patient based on the determined risk thresholds; and a risk prediction response module (120) configured to provide a patient's risk score and a clinical algorithm for next steps based on their risk level of acute exacerbation of COPD and related events, thereby offering a comprehensive view of the patient's risk status, Al Score, and presenting recommendations and responses, including clinical algorithms that suggest diagnostics imaging, referrals, treatment goals, lab investigation, next steps, educational topics, and guidelines for future visits and tests follow-up, with the capability to generate a PDF report, whereby the system (100) functions as a software as a medical device (SaMD), certified by ISO 13485, and integrates algorithms for assessing the risk of acute exacerbation of asthma, COPD, and related events, thereby enhancing patient outcomes and supporting clinicians.

2. The artificial intelligence (Al) based system (100) as claimed in claim 1, wherein the artificial intelligence (Al) based system (100) acts as a complementary tool for clinicians, thereby enhancing informed decision-making without replacing clinicians and diagnostictests, wherein the user device includes at least one of a computer, a smartphone, a laptop, a tablet, and a personal digital assistant (PDA).

3. The artificial intelligence (Al) based system (100) as claimed in claim 1, wherein the personal parameters include age, gender, and demographic data, wherein the medical history includes respiratory health history, such as conditions like asthma, COPD, and other lung diseases.

4. The artificial intelligence (Al) based system (100) as claimed in claim 1, wherein the medical history includes known allergies, including sensitivities to pollen, dust mites, animal dander, food, insect bites and stings, medicines, latex, and mold.

5. The artificial intelligence (Al) based system (100) as claimed in claim 1, wherein the medical history includes vaccination history, specifically the type of COVID-19 vaccine received, the date of the last dose, and if applicable, the last dose of the influenza vaccine.

6. The artificial intelligence (Al) based system (100) as claimed in claim 1, wherein the lifestyle attributes include smoking and tobacco consumption history.

7. The artificial intelligence (Al) based system (100) as claimed in claim 1, wherein the demographic data includes the patient's current location, area, and pin code are used to calculate the Air Quality Index.

8. The artificial intelligence (Al) based system (100) as claimed in claim 1, wherein the medical history includes a medication history, with the medication list divided into categories such as cough suppressants, mucolytic agents, antihistamines, and inhalers, as well as bronchodilators and antibiotics.

9. The artificial intelligence (Al) based system (100) as claimed in claim 1, wherein the clinical examination parameters include the patient's history of asthma, tuberculosis, and heart disease, the presence of progressive breathlessness, the occurrence of productive cough, the usage of certain medications, respiration rate, any recent increase in pulse rate, oxygen saturation levels, and the presence of wheezes upon chest auscultation.

10. A method for operating an Al-based system (100) for predicting the risk of acute exacerbation of asthma, and chronic obstructive pulmonary disease (COPD)-related events, comprising: collecting, by a data collection module (122), input data from various sources such as electronic medical records (EMRs), Data repositories and Databases; standardizing and pre-processing, by a standardization and pre-processing module (124), the collected data to ensure its integrity and compatibility with the system's processing requirements; transmitting, by an API module (126), the pre-processed data from the data collection module (122) and the standardization and pre-processing module (124) to the server (104) via a network (102); receiving, by an input module (112), the pre-processed data from the server (104) to authenticate and validate, and collecting patient-related parameters and medical history; standardizing, by a processing module (114), the pre-processed data to fit the necessary input criteria for subsequent analysis; applying, by a prediction module (116), a CART machine learning model to categorize the patient's risk of acute exacerbation of chronic obstructive pulmonary disease (COPD); creating, by a clinical pathway module (118), a personalized protocol of subsequent actions based on the determined risk; and displaying, by a risk prediction response module (120), the patient's risk status, Al score, and a clinical algorithm suggesting next steps based on the stratified risk of acute exacerbation of asthma and COPD.

6. DATE AND SIGNATURE:Dated this 28thday of August, 2024

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