Description:Field of Invention
Under the field of digital healthcare and medical informatics, the present invention is specifically related to methods and systems for delivering personalized drug recommendations and medical counsel. More accurately, this innovation takes advantage of a huge medical database, machine learning algorithms, natural language processing, and advanced data handling methods to provide users with personalized, precise, and helpful health data. It employs an advanced clustering technique for labeling diseases according to user inputs, a uniform extraction and interpretation system for medical terms, an easy-to-use interface for input of health information, and a smart filtering mechanism that identifies user profiles with correct treatment plans. The system is designed to scale and integrate with global healthcare systems, and it operates in real-time to give immediate feedback for routine and emergency health questions. This technology provides individuals the power to make informed choices regarding their health by filling the gap between complex medical information and usual healthcare needs.
Objectives of the invention
The objective of this invention is to the development of healthcare technology by delivering an easy-to-use platform that harnesses the power of machine learning and data processing to produce accurate and timely pharmaceutical suggestions in the form of a systematic system of filtering, classification, and terminology extraction.
Background of the Invention
The origins of the MediLearn idea can be traced to the expanding demand in an increasingly digital society for individualized and easily accessible healthcare solutions. Novel strategies for distributing medical advice and information are desperately needed, as healthcare systems struggle to keep up with the demands of a worldwide population.US20230162844A1 reveals the traditional methods of seeking medical guidance often involve long physician visits or depending on potentially inaccurate internet sources. Massive medical data analysis and processing now have new possibilities due to machine learning and artificial intelligence. Python, with its robust libraries and frameworks, is now a powerful tool for developing sophisticated healthcare applications. Utilizing these technologies, the research works towards creating a medicine recommendation system that can be personalized as per user inputs and large medical datasets revealed in US20190043606A1.MediLearn leverages Python and AI to create a medicine recommendation system based on individual needs. Such a system not only uses large medical datasets but also learns as per user-specific inputs such as symptoms, past health conditions, and dietary habits. In doing so, it makes sure that suggestions are evidence-based, relevant, and actionable for the users.
The invention bridges the knowledge gap between the amount of medical information available and human ability to meaningfully absorb and understand it. Through individualized recommendations and the simplification of complex medical data, MediLearn aims to equip users with information and enable informed healthcare decision-making.US20220059228A1 discloses The introduction of artificial intelligence (AI) and machine learning (ML) has dramatically changed how we manage large-scale data. These technologies are brilliant at interpreting huge data sets, identifying patterns, and predicting, paving the way for building smarter, adaptive healthcare solutions. AI-driven systems can interpret medical histories, user habits, and larger data sets to provide personalized healthcare suggestions effectively.As healthcare demands intensify, the gap between the vast amounts of medical information available and the average person's ability to digest, comprehend, and utilize that information effectively has widened disclosed in US20220122731A1. This disconnect underscores the need for tools that can bridge this divide by simplifying access to reliable medical knowledge while empowering users to make informed decisions.
The Ultimate goal of MediLearn is to transform how individuals interact with medical information and advice. By offering a platform that combines accessibility, personalization, and cutting-edge AI technology, it aims to reduce the dependency on traditional, often inaccessible, healthcare systems. Instead, MediLearn envisions a world where healthcare advice is not only reliable and evidence-based but also seamlessly integrated into users' daily lives.
Summary of the Invention
By providing an accessible platform for individualized pharmaceutical prescription, MediLearn is a major breakthrough in healthcare technology. The technology matches user inputs against a vast repository of medical information using machine learning algorithms and advanced data processing methods. MediLearn offers an efficient and scalable means of providing timely and accurate health care.The three primary components of the innovation include a brilliant filtering system that identifies the appropriate pharmaceuticals, a clustering algorithm that categorizes disorders, and a consistent extraction mechanism that handles medical terminology. On top of this platform, MediLearn adds patient feedback loops and real-time analytics to its core offerings to enhance healthcare decision-making. Based on the medical history, symptoms, and demographic data of a patient, the intelligent filtering mechanism ensures that it only provides the most relevant drug options. Meanwhile, the system is able to accurately categorize similar diseases owing to the clustering algorithm, which improves diagnostic support and speeds up the process of proposing treatments.
The uniform extraction system is very effective, as it standardizes and interprets multiform medical terminologies from multiple sources and represents data consistently. This reduces the risk of misinterpretation and enhances data interoperability across healthcare systems. Ultimately, MediLearn enhances treatment compliance and outcomes by empowering patients with individualized, data-driven insights and aiding clinical decision-making. It is a classic example of how medicine and AI can be combined to create smart, adaptive healthcare solutions.
Detailed Description of the Invention
The MediLearn system is a revolutionary solution to filling the gap between huge medical knowledge and people's capacity to access and understand it efficiently. Developed as an artificial intelligence-driven healthcare platform, it makes hard-to-understand medical information simple and offers personalized suggestions to users, facilitating well-informed healthcare decision-making. The system has a responsive and intuitive web and mobile interface where users can engage with different modules like a Symptom Checker, Medicine Recommendation Engine, and Disease Predictor.
Symptom Checker enables users to enter symptoms, severity, and individual health information, while AI-powered engine, learned from large medical datasets, processes the information to propose related health conditions and suitable medicines. Medicine Recommendation Engine not only suggests pre-filtered medications but also offers complete usage guidelines, substitutes, and alerts based on user profiles, such as prior conditions or allergies. There is an Interactive Chabot integrated into the platform to enable real-time support, teaching users how to use the application and responding to health-related questions.
In addition to suggestions, the site includes educational material, including tips on health and condition-specific guidance, with the intention of promoting awareness and active management of health. The underlying engine is fueled by sophisticated machine learning algorithms, written in Python, using tools such as TensorFlow and Scikit-learn, to provide accuracy and versatility. The Medicine Recommendation Engine not only provides suggested medications but also offers information on usage, substitutes, and precautions.
The design of MediLearn is user-friendly and accessible, with a clean, intuitive, and responsive interface that ensures compatibility with devices like smart phones, tablets, and desktops. To meet the increasing need for handy health care solutions, this system is meant to provide customers worldwide with doctor-friendly and hospital-free medicine recommendations. The main component of our approach is the use of a single extraction strategy in order to find notable medical sentences. Disorders are then grouped with the assistance of the K-means cluster model, and the best drugs are found using a filtering approach. Our strategy improves the overall experience for users when receiving medical advice by making it easier to choose the correct drug. This helps to improve healthcare availability worldwide.
A healthcare technology breakthrough, the MediLearn system aims to speed up and tailor drug recommendations worldwide. An universal method of extraction of the most important medical terms, the K-means clustering algorithm in determining conditions, and an advanced filtering mechanism for choosing appropriate medicines are just a few of the means in which MediLearn provides a doctor-friendly, hospital-free experience. The user-focused design of the system, its world-wide accessibility, and its ongoing evolution through user feedback make it a beacon of what the future holds for accessible and personalized healthcare. It enables medical recommendation-seeking by individuals worldwide.
It is shown in Figure 1 that the AI-integrated healthcare system ensures the continuous and efficient usage of the recommendation engine without performance degradation due to computational load. This is achieved through optimized algorithms that balance processing power and resource allocation during operation.A data cleanup mechanism (1) is included in the system to remove redundant or erroneous user inputs instantly. This feature ensures that irrelevant or inaccurate data does not interfere with the decision-making algorithms. The cleansing of input data reduces "friction" in the recommendation process, enabling smoother operations and more accurate outputs.
This guidance system enhances the diagnostic potential of the platform, ensuring that input data is analyzed comprehensively to improve the accuracy of the system's predictions and recommendations the cleansing of input data reduces "friction" in the recommendation process, enabling smoother operations and more accurate outputs.Dependent on user input, the invention's recommendation system based on intelligent health data assists users in recognizing potential medical conditions and suggests suitable treatments. Utilizing a simple-to-operate interface, users enter their health data, which is then processed and filtered to derive important medical terms. For labeling illnesses and proposing suitable therapies, such terms are matched against a secure medical database. The system incorporates a feedback process to enhance accuracy and personalization, and it shows results via an output interface that is user-friendly. The innovation offers reliable and readily accessible healthcare assistance by combining medical knowledge with cognitive processing.
By matching the user's medical profile with its vast medical database, containing information on diseases, therapies, and prescription medications, MediLearn uses a clever filtering mechanism to provide accurate suggestions. The approach ensures credibility and reliability by suggesting evidence-based treatments and medication protocols. A unique feature of MediLearn is its real-time capability, which offers quick feedback and recommendations on routine as well as urgent medical queries. This ability comes in particularly handy when time is limited and fast guidance can make a significant impact on health outcomes. MediLearn is an integrated and scalable platform that can be employed both by individuals and organizations. Its design supports seamless interoperability with global healthcare systems. It can adapt to various demographics and healthcare systems, ensuring accessibility and inclusivity across national and cultural boundaries. MediLearn enables consumers to confidently make educated health decisions by bridging the gap between complicated medical science and routine healthcare needs. With its intelligent, user-centric, and responsive approach to medical suggestions, this cutting-edge system has the potential to completely transform the field of digital health solutions.
Brief Description of Drawings:
The invention will be described in detail with reference to the exemplary embodiments shown in the figures wherein:
Figure 1: Pictorial representation of the MediLearn UI layout, showcasing the home screen with a search bar, medicine recommendation panel, navigation bar, and interactive chatbot.
Detailed description of drawing
The above diagram illustrates the entire process of a health data-driven medical recommendation system, which is intended to help users comprehend possible medical issues and provide them with drug recommendations depending on their input. The user is at the center of the system from the very beginning. Through a specific health data input interface, the user starts the engagement by entering their health-related data. Any digital system that allows users to enter information like symptoms, past medical history, lifestyle choices, or test results can serve as this interface.Some of these include web forms, Smartphone apps, and others. Because it sets the stage for all subsequent processing and recommendations, this user interaction is the first and most critical phase.
After submission of the health data, it is sent to the data preprocessor module. The primary function of the data preprocessor is to normalize and clean the input data. It guarantees that the data is error-free, consistent, and irrelevant. This can include treating missing values, fixing formatting errors, and normalizing vocabulary. The result from this module is organized, high-quality data that is in a state to be processed further. Proper preprocessing is important because the performance of the entire system relies greatly on the quality of the initial processing of the data.
The preprocessed data is then forwarded to the medical term extractor. This module examines the data and identifies medically pertinent terms employing methods such as natural language processing (NLP). It extracts key words and phrases such as symptoms, conditions, anatomical references, and other health indicators. These medical terms thus extracted are important for mapping user input to recognized medical knowledge. Meanwhile, the system retrieves a medical database that has verified reference data such as disease profiles, symptom associations, and sanctioned drugs.
The illness categorizer module interjects with the reference data and extracted phrases from the medical database. The module relates previously known illnesses or conditions to the symptoms and health indicators identified. To determine potential health issues, it examines patterns and cross-compares user data with medically recorded instances. This categorization is important in order to narrow down the scope of illnesses that can be affected by the user. For added accuracy and to ensure reliable classification, the disease categorizer uses reference information from the medical database.
After determining the potential conditions, the system proceeds to the medication recommender. This element utilizes the disease information categorized with the database of references to provide recommendations on medication. The suggestions are customized based on the input from the user and the most suitable and effective treatments. It makes sure that the suggested medication is safe, relative, and compliant with medical standards. The aim is to present the user with possible remedies or measures they can take depending on their health profile.
All the results from the processes described above are presented to the user by way of a friendly output interface. This interface is crafted to display results in a well-structured, easy-to-understand, and visually appealing form. It converts the intricate backend processing into plain, actionable data for the user. In addition to this, a feedback mechanism is integrated, enabling users to post their experiences, recommendations, or other information. The feedback loop not only improves user interaction but also improves the personalization and accuracy of the system with time. Lastly, the process loops back to the user, closing the circle of interaction, processing, recommendation, and feedback in an intelligent health assistant framework. This data is then transmitted to a data pre-processor that cleans and formats it for accurate analysis. The processed data is sent to a medical term extractor, which identifies key medical terms from the user’s input. These terms are then matched with a medical database that provides reference information about diseases and medications. Using this reference data, a disease categorizer classifies the user's health condition. Based on the categorized condition and additional reference data, the medication recommender suggests suitable treatments. These recommendations are presented to the user through a user-friendly output interface. Finally, a feedback system allows the user to provide input on the accuracy and usefulness of the recommendations, helping to refine the system over time. , Claims:The scope of the invention is defined by the following claims
Claims:
1. A MediLearn: Medical recommendation comprising:
a) A system for gathering comprehensive health data using user-friendly interfaces that ensure effective and superior input capture.
b) The system records all pertinent health data—such as symptoms, medical background, and present conditions—and makes it accessible for analysis.
c) Ensures adaptability to various healthcare settings by enabling the collection of a broad variety of health data.
d)The system extracts medical terms, references a medical database, and categorizes possible diseases.
e)Based on categorized conditions, it recommends medications, displays results, and collects user feedback.
f)The system extracts medical terms, references a medical database, and categorizes possible diseases.
2. As per claim 1, the system includes a robust data preprocessing and feature engineering framework. This framework transforms raw health data into meaningful features for medication recommendations through:
Standardization of data,Classification of symptoms, andDevelopment of new metrics that emphasize pertinent health patterns.
3. As mentioned in claim 1, machine learning models for prescription recommendations are developed and implemented. These models, trained on large-scale medical datasets, identify patterns linked to medical conditions and suitable treatments. The system's adaptability and efficiency are enhanced by cutting-edge learning strategies, enabling personalized prescription suggestions.
4. According to claim 1, the system provides the ability to give medication suggestions and feedback in real time. It constantly analyzes user inputs to supply quick, targeted health guidance, providing timely and appropriate recommendations for a better experience.
5. According to claim 1, the system employs an intuitive interface and advanced reporting capabilities to render health data and prescription suggestions in an easily understandable form. The features include detailed descriptions and thorough information, which help users make informed healthcare choices.