A SYSTEM FOR GENERATING ENHANCED DRUG RECOMMENDATIONS BASED ON PATIENT-GENERATED DATA AND CLINICAL DATA

Abstract

7. ABSTRACT This invention presents an Enhanced Drug Recommendation System utilizing opinion analysis and machine learning to provide personalized drug suggestions. By integrating patient-generated data, such as online reviews, with clinical information, the system delivers tailored recommendations based on individual characteristics and experiences. Key features include sentiment analysis to assess drug effectiveness, aspect-based opinion mining, and a hybrid recommendation model combining collaborative and content-based filtering. Machine learning algorithms, including logistic regression, analyse large-scale datasets to predict drug efficacy and minimize adverse reactions. A knowledge base, continuously updated with clinical findings, supports evidence-based recommendations. Privacy measures ensure secure, ethical data use, enhancing patient satisfaction and healthcare outcomes by improving medication adherence and safety. The figure associated with abstract is Fig. 1.

Specification

Description:4. DESCRIPTION
Technical Field of the Invention

The present invention related to computer science and healthcare technology. More particularly, focusing on the development of an intelligent drug recommendation system that utilizes patient-generated data and clinical data to enhance healthcare decision-making and optimize treatment outcomes.

Background of the Invention

In recent years, the healthcare industry has seen a surge in the application of technology to enhance patient outcomes and improve clinical decision-making. One critical area of interest is the development of advanced drug recommendation systems that can leverage vast amounts of data to provide personalized and precise medication suggestions for patients. Traditional drug recommendation systems typically rely on clinical data and expert opinions, which, while essential, often fall short in addressing the unique, individualized needs of patients. This is particularly true in an era where patient-generated data, including online reviews, social media posts, and other digital sources, offers additional insights into real-world drug experiences, effectiveness, side effects, and patient satisfaction.

The present invention, an Enhanced Drug Recommendation System, addresses these limitations by incorporating advanced opinion analysis techniques, machine learning algorithms, and real-time integration of both clinical and patient-generated data. By extracting sentiment, analyzing drug-specific aspects, and leveraging collaborative and content-based filtering methods, this invention aims to improve the quality and relevance of drug recommendations, support personalized medicine, and ultimately enhance healthcare outcomes.

Need for Enhanced Drug Recommendation Systems
The growing availability of patient-generated data has opened new avenues for creating more accurate, relevant, and patient-centered drug recommendation systems. This trend is driven by several factors:

Data Explosion: The rise of social media, health forums, and online review platforms has resulted in a substantial increase in patient-generated data. Patients frequently share their experiences, including the effectiveness of drugs, adverse effects, and levels of satisfaction. Harnessing this wealth of data can provide a more nuanced view of drug performance beyond clinical trials and controlled studies.

Shift Toward Personalized Medicine: Modern healthcare emphasizes the need for tailored treatments that align with individual patient profiles, including their genetic background, lifestyle, and preferences. A one-size-fits-all approach is increasingly viewed as insufficient, making it essential to consider data beyond standard clinical information. Enhanced drug recommendation systems, such as the present invention, can incorporate both clinical and patient-generated data to offer a more personalized approach to drug selection.

Drug Safety and Risk Management: Adverse drug reactions (ADRs) are a significant concern in healthcare, impacting patient safety and increasing healthcare costs. Traditional drug recommendation systems often miss subtle patterns in drug interactions and patient responses. By analysing real-world data, this invention provides an additional layer of safety, identifying drugs with lower risks of ADRs and thereby minimizing potential harm to patients.

Improving Patient Adherence and Satisfaction: Patient satisfaction and adherence to prescribed treatments are influenced by the alignment of treatment plans with patient preferences and expectations. An enhanced drug recommendation system that considers patient feedback and preferences can boost adherence rates, which is critical for achieving optimal treatment outcomes.

Limitations of Existing Drug Recommendation Systems
While various systems have been developed to assist in drug recommendation, they often face several challenges, including:

Data Quality Issues: Patient-generated data can be unstructured, noisy, and prone to biases, making it challenging to extract accurate information. This data varies in format and quality, requiring robust preprocessing to ensure its usability in machine learning models.

Opinion Analysis Complexity: Extracting meaningful insights from unstructured text data, such as reviews and social media posts, requires advanced opinion analysis techniques, including sentiment analysis and aspect extraction. Traditional systems often lack the sophistication needed to identify nuanced insights from such data, resulting in incomplete or inaccurate recommendations.

Integration Challenges with Clinical Data: Combining patient-generated data with clinical data presents another challenge. Clinical data is structured and follows a standardized format, while patient-generated data is more subjective and diverse in nature. Integrating these two data types is essential for creating a comprehensive recommendation system that provides well-rounded suggestions.

Ethical and Privacy Concerns: Utilizing patient-generated data raises ethical concerns, particularly regarding data privacy, potential biases, and the risk of misinformation. Existing systems must take precautions to anonymize patient data, prevent biases, and ensure data integrity, but many fall short of meeting these standards, risking patient trust and compliance with regulatory requirements.

Brief Summary of the Invention

The following presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

It is a primary objective of the invention is to develop a personalized drug recommendation system that leverages opinion analysis and machine learning to provide tailored drug suggestions based on individual patient characteristics and experiences.

It is yet another object of the invention to improve drug effectiveness by recommending treatments that are more likely to benefit specific patients, aligning with their unique medical needs and preferences.

It is yet another object of the invention to reduce adverse drug reactions by identifying and recommending drugs with a lower likelihood of side effects for individual patients.

It is yet another object of the invention to enhance patient satisfaction by offering drug recommendations that better match patient expectations and experiences.

It is yet another object of the invention to contribute to cost-effective healthcare by optimizing drug usage and minimizing unnecessary or ineffective treatments, ultimately reducing healthcare costs.

According to an aspect of the present invention, a system for generating enhanced drug recommendations is disclosed. The system comprising data acquisition module, data preprocessing module, opinion mining and sentiment analysis module, feature engineering module, machine learning module, knowledge base module, recommendation generation module, user interface module, and privacy and ethics module.

In accordance with the aspect of the present invention, the Enhanced Drug Recommendation System harnesses machine learning and opinion analysis techniques to provide personalized drug recommendations that cater to individual patient needs. Traditional drug recommendation systems often rely on clinical data alone, which may overlook unique patient experiences and nuanced insights available from patient-generated data, such as online reviews and social media. By incorporating this real-world feedback, the present invention ensures a more comprehensive and patient-centred approach to drug recommendation, addressing the limitations of conventional systems.

In accordance with the aspect of the present invention, advanced opinion mining techniques, including sentiment analysis and aspect extraction, are used to analyze patient reviews and discussions. These methods enable the system to identify specific attributes of each drug, such as effectiveness, side effects, and overall satisfaction, from unstructured patient-generated data. By combining these insights with clinical data, the system offers a holistic view of drug performance, tailored to each patient's needs and preferences.

In accordance with the aspect of the present invention, the system utilizes a hybrid recommendation model that integrates both collaborative and content-based filtering. Collaborative filtering identifies drugs based on similar patient profiles and shared experiences, while content-based filtering matches drugs with similar characteristics and medical indications. This dual approach provides a more accurate and relevant recommendation, as it learns from both patient similarities and drug attributes, ensuring that the suggested treatments are well-suited to individual patient profiles.

In accordance with the aspect of the present invention, machine learning algorithms are employed to process and predict drug recommendations based on user input, such as specific disease conditions or symptoms. The invention rigorously evaluates multiple machine learning models-such as Logistic Regression, SVM, and decision trees-to identify the most effective algorithm for analyzing patient data and predicting suitable drugs. This data-driven methodology enhances the reliability and accuracy of drug recommendations, ultimately improving patient outcomes.

In accordance with the aspect of the present invention, the system addresses ethical and privacy concerns related to using patient-generated data by implementing robust data anonymization and consent mechanisms. It ensures that the recommendation process is unbiased and respects patient privacy, adhering to healthcare regulations and guidelines. This approach not only safeguards sensitive information but also promotes the ethical use of data, fostering patient trust and acceptance of the technology.

In accordance with the aspect of the present invention, the Enhanced Drug Recommendation System offers a user-friendly interface that allows healthcare providers and patients to access tailored recommendations easily. Through this platform, patients receive evidence-based suggestions that align with their personal health histories and preferences, while healthcare providers gain valuable insights to support clinical decision-making. This integration of technology into healthcare enables more effective, patient-centered treatment, setting a new standard in personalized medicine and drug recommendation.

Advantages of present invention:
• Enhanced Clinical Decision-Making: Offers healthcare professionals a valuable tool for selecting appropriate medications, improving patient care outcomes.
• Improved Patient Satisfaction: Provides patients with personalized drug recommendations that align with their individual needs and preferences.
• Advancements in Medical Technology: Represents a significant step forward in the application of machine learning and natural language processing to healthcare.

Further scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, the detailed description and specific examples, while indicating preferred embodiments of the invention, will be given by way of illustration along with complete specification.

Brief Summary of the Drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

Fig. 1a and 1b (100) illustrates the overall architecture of system for generating enhanced drug recommendations based on patient-generated data and clinical data, in accordance with the exemplary embodiment of the present invention.

Fig. illustrates use case diagram in the unified modeling language (UML), in accordance with the exemplary embodiment of the present invention.

Fig. 3 illustrates the class diagram in the unified modeling language (UML), in accordance with the exemplary embodiment of the present invention.

Fig. 4 illustrates the sequence diagram in the unified modeling language (UML), in accordance with the exemplary embodiment of the present invention.

Fig. 5 illustrates the activity diagram in the unified modeling language (UML), in accordance with the exemplary embodiment of the present invention.

Fig. 6 illustrates the collaboration diagram in the unified modeling language (UML), in accordance with the exemplary embodiment of the present invention.

Fig. 7a-7k illustrates different screens from UCI machine learning website, in accordance with the exemplary embodiment of the present invention.



Detailed Description of the Invention

The present disclosure emphasises that its application is not restricted to specific details of construction and component arrangement, as illustrated in the drawings. It is adaptable to various embodiments and implementations. The phraseology and terminology used should be regarded for descriptive purposes, not as limitations.

The terms "including," "comprising," or "having" and variations thereof are meant to encompass listed items and their equivalents, as well as additional items. The terms "a" and "an" do not denote quantity limitations but signify the presence of at least one of the referenced items. Terms like "first," "second," and "third" are used to distinguish elements without implying order, quantity, or importance.

According to the exemplary embodiment of the present invention, a system for generating enhanced drug recommendations (100) comprising data acquisition module, data preprocessing module, opinion mining and sentiment analysis module, feature engineering module, machine learning module, knowledge base module, recommendation generation module, user interface module, and privacy and ethics module.

In accordance with the exemplary embodiment of the present invention, a Data Acquisition Module is configured to collect extensive patient-generated data from multiple sources, including online reviews, social media, and clinical databases. This module gathers patient insights on drug effectiveness, side effects, and overall satisfaction. By consolidating this data with clinical sources, the system achieves a comprehensive understanding of drug outcomes, capturing both professional assessments and patient experiences.

In accordance with the exemplary embodiment of the present invention, a Data Preprocessing Module is employed to clean and prepare the collected data. This module removes irrelevant content, special characters, and inconsistencies, while normalizing various formats to ensure data quality and uniformity. Preprocessing is crucial for transforming raw patient reviews and social media posts into structured, analysable formats that can be processed accurately by subsequent modules.

In accordance with the exemplary embodiment of the present invention, an Opinion Mining and Sentiment Analysis Module utilizes advanced NLP techniques to analyse patient-generated data for sentiment and specific drug attributes, such as efficacy, side effects, and satisfaction levels. This module conducts aspect-based sentiment analysis, extracting particular features (e.g., drug cost, convenience) and measuring sentiment for each aspect, allowing for a nuanced understanding of patient opinions. By breaking down these aspects, the system can identify strengths and potential issues associated with each drug.

In accordance with the exemplary embodiment of the present invention, a Feature Engineering Module transforms processed data into structured features for machine learning models. Techniques like TF-IDF (Term Frequency-Inverse Document Frequency), bag-of-words, and vectorization are applied to quantify and represent text data. These features enable the system to interpret patient feedback as numeric input, suitable for predictive analysis. This module converts patient language into actionable insights, preparing data for the recommendation engine.

In accordance with the exemplary embodiment of the present invention, a Machine Learning Module applies a hybrid recommendation model by combining collaborative and content-based filtering to provide personalized drug suggestions. This module leverages collaborative filtering to draw patterns from patient similarities and shared experiences while using content-based filtering to match drugs based on medical indications and side effects. Algorithms such as Logistic Regression and other machine learning models, including SVM and decision trees, are used to predict the most suitable drugs for each patient profile, optimizing drug recommendations based on both patient history and drug properties.

In accordance with the exemplary embodiment of the present invention, a Knowledge Base integrated with the machine learning module maintains a continuously updated repository of information on drugs, including indications, interactions, side effects, and clinical trial data. This module ensures that the system's recommendations are evidence-backed, integrating the latest clinical findings and regulatory updates to offer reliable and up-to-date guidance on drug options. By connecting with clinical and regulatory databases, the knowledge base supports evidence-based recommendations and enhances the system's adaptability to new developments in pharmacology.

In accordance with the exemplary embodiment of the present invention, a Recommendation Generation Module uses insights from the machine learning module to deliver drug suggestions tailored to each patient's needs. This module analyses correlations between patient profiles and processed data, considering factors such as patient medical history, drug efficacy, and likelihood of adverse reactions. The hybrid filtering approach in this module refines the relevance of recommendations, ensuring that each suggestion aligns closely with patient preferences, medical needs, and past treatment outcomes.

In accordance with the exemplary embodiment of the present invention, a User Interface Module is developed to facilitate seamless interaction between patients, healthcare providers, and the recommendation system. This interface displays drug recommendations with detailed information on predicted efficacy, potential side effects, and alternative options. The user interface enhances engagement by providing patients with insights into their treatment options and helping healthcare providers make informed prescribing decisions.

In accordance with the exemplary embodiment of the present invention, a Privacy and Ethics Module ensures secure and ethical handling of patient data. This module implements data anonymization, encryption, and secure storage practices to protect sensitive information, complying with regulatory standards for data privacy. Additionally, it incorporates ethical protocols to maintain transparency and fairness, addressing potential biases in data analysis and ensuring unbiased recommendations.

Now referring to the figures, Fig. 1 illustrates the modular components of the enhanced drug recommendation system and their interactions. The Data Acquisition Module collects patient-generated data and clinical data, feeding it into the Data Preprocessing Module for cleaning and normalization. The Opinion Mining and Sentiment Analysis Module then extracts specific insights, which are transformed into structured features by the Feature Engineering Module. These features are processed by the Machine Learning Module, which combines collaborative and content-based filtering to generate drug recommendations. The Knowledge Base integrates clinical data, ensuring evidence-backed suggestions. Finally, the Recommendation Generation Module and User Interface Module present tailored drug options to users, while the Privacy and Ethics Module secures data integrity and compliance.

Fig. 2 shows a use case diagram in the Unified Modeling Language (UML) is a type of behavioral diagram defined by and created from a Use-case analysis. Its purpose is to present a graphical overview of the functionality provided by a system in terms of actors, their goals (represented as use cases), and any dependencies between those use cases. The main purpose of a use case diagram is to show what system functions are performed for which actor. Roles of the actors in the system can be depicted.

Fig. 3 illustrates UML Class Diagram illustrates the main classes and their relationships in the drug recommendation system. It includes three primary classes: Admin, User, and Drug. The Admin class has attributes for authentication (username, password) and methods to manage system operations, such as uploading datasets, viewing reviews, analysing data, and predicting outcomes. The User class holds user information like Username, Password, Email ID, and Age. It provides methods for user actions like registering, logging in, posting drug reviews, and logging out. The Drug class includes attributes for the drug's name (Drugname) and user-provided reviews (Drug review).

Fig. 4 illustrates a sequence diagram in Unified Modeling Language (UML) is a kind of interaction diagram that shows how processes operate with one another and in what order. It is a construct of a Message Sequence Chart. Sequence diagrams are sometimes called event diagrams, event scenarios, and timing diagrams.

Fig. 5 illustrates activity diagram which is another important behavioral diagram in UML diagram to describe dynamic aspects of the system. Activity diagram is essentially an advanced version of flow chart that modeling the flow from one activity to another activity.

Fig. 6 illustrates collaboration which is a type of interaction diagram because it describes how and in what order a group of objects works together. These diagrams are used by software developers and business professionals to understand requirements for a new system or to document an existing process.

Fig. 7a illustrates Trained Dataset, first row represents dataset column names such as drug name, condition, review and rating and remaining rows contains dataset values and we will used above REVIEWS and RATINGS to trained machine learning models. Below is the test data which contains only disease name and machine learning will predict Drug name and ratings.

Fig. 7b illustrates diagram of Test Dataset. Only the disease name and machine learning will predict ratings and drug names. To implement this Application designed by following modules
a. Upload Drug Review Dataset: using this module upload dataset to application
b. Read & Preprocess Dataset: using this module read all reviews, drug name and ratings from dataset and form a features array.
c. TF-IDF Features Extraction: features array will be input to TF-IDF algorithm which will find average frequency of each word and then replace that word with frequency value and form a vector. If word not appear in sentence, then 0 will be put. All reviews will be considered as input features to machine learning algorithm and RATINGS and Drug Name will be consider as class label.
d. Train Machine Learning Algorithms: using this module we will input TF-IDF features to all machine learning algorithms and then trained a model and this model will be applied on test data to calculate prediction accuracy of the algorithm.
e. Comparison Graph: using this module we will plot accuracy graph of each algorithm
f. Recommend Drug from Test Data: using this module upload disease name test data and then ML will predict drug name and ratings.

To run project double click on 'run.bat' file to get below screen

Fig. 7c shows Home page in which we can click on 'Upload Drug Review Dataset' button to upload dataset to application.

Fig. 7d shows screen of Upload trained dataset. We can selecting and uploading DRUG dataset and then click on 'Open' button to load dataset and to get below screen.

Fig. 7e illustrates the graph, in which we can see dataset loaded and in graph x-axis represents ratings and y- axis represents total number of records which got that rating. Now close above graph and then click on 'Read & Preprocess Dataset' button to read all dataset values and then preprocess to remove stop words and special symbols and then form a features array.

Fig. 7f is a screen of Drug name graph is to see from all reviews stop words and special symbols are removed and in graph I am displaying TOP 20 medicines exist in dataset. In above graph x-axis represents drug name and y-axis represents its count. Now close above graph and then click on 'TF-IDF Features Extraction' button to convert all reviews in to average frequency vector.

Fig. 7g shows Word Average Frequency. in which all reviews converted to TF-IDF vector where first row represents review WORDS and remaining columns will contains that word average frequency and if word not appear in review, then 0 will put. Now scroll down above screen to view some non- zero frequency values.

Fig. 7h shows performance screen for each algorithm calculate accuracy, precision, recall and FSCORE and in all algorithms Logistic regression has got high performance and now click on 'Comparison Graph' button to get below graph.

Fig. 7i shows graph of accuracy in which x-axis represents algorithm name and y-axis represents accuracy, precision recall and FSCORE where each different colour bar will represent one metric and in above graph, we can see Logistic Regression got high performance. Now close above graph and then click on 'Recommend Drug from Test Data' button to upload test data and to get predicted results drug name and ratings.

Fig. 7j shows screen selecting and uploading 'testData.csv' file and then click on 'Open' button to load test data and get below prediction result.

Fig. 7k shows result screen for each disease name application has predicted recommended drug name and ratings
, C , Claims:5. CLAIMS
I/We Claim:
1. A system for generating enhanced drug recommendations based on patient-generated data and clinical data (100), the system comprising:
a data acquisition module (102) configured to collect patient-generated data from online reviews, social media, and clinical data sources, where patient-generated data includes information about drug effectiveness, side effects, and patient experiences;
a data preprocessing module (104) configured to clean and process the collected data, including the removal of irrelevant content and normalization of data, for consistency and quality;
an opinion mining and sentiment analysis module (106) configured to extract sentiment and specific aspects of drug experiences from patient-generated data using natural language processing (NLP) techniques, wherein the sentiment analysis includes aspect-based analysis to determine drug efficacy, side effects, and user satisfaction;
a feature engineering module (108) configured to transform processed data into structured features using techniques such as TF-IDF (Term Frequency-Inverse Document Frequency), bag-of-words, and vectorization methods for input into machine learning algorithms;
a machine learning module (110) configured to employ a hybrid recommendation model combining collaborative filtering and content-based filtering techniques to analyze patient data, clinical data, and opinion-mined data, wherein the module applies logistic regression and other machine learning algorithms to predict optimal drug recommendations for specific patient profiles;
a knowledge base integrated with the machine learning module (112), containing comprehensive and up-to-date information on drugs, their uses, interactions, and side effects, and continuously updated with recent clinical findings;
a recommendation generation module (114) configured to generate drug recommendations for individual patients by analyzing the correlation between the patient profile and processed data, wherein recommendations consider patient characteristics, drug efficacy, and likelihood of adverse reactions;
a user interface module (116) configured to allow interaction with patients and healthcare providers, enabling personalized drug recommendations and feedback collection;
a privacy and ethics module (118) configured to anonymize patient data, ensure data privacy, and maintain system transparency to address ethical considerations in utilizing patient-generated data.

2. The system as claimed in claim 1, wherein the opinion mining and sentiment analysis module utilizes aspect extraction techniques to analyze individual components of patient feedback, identifying specific drug attributes such as cost, convenience, and adverse effects.

3. The system as claimed in claim 1, wherein the machine learning module applies reinforcement learning to adjust recommendations based on patient feedback, allowing for continuous improvement of drug suggestion accuracy.

4. The system as claimed in claim 1, wherein the knowledge base includes integration with clinical trial databases and regulatory updates to provide an evidence-backed recommendation for new and existing drugs.

5. The system as claimed in claim 1, wherein the recommendation generation module employs a hybrid approach by combining content-based filtering and collaborative filtering to enhance the relevance of drug recommendations based on patient history and preferences.

6. The system as claimed in claim 1, wherein the privacy and ethics module anonymizes patient data through encryption and secure storage practices, ensuring compliance with regulatory standards for data protection.

7. The system of claim 1, wherein the user interface module is configured to display drug recommendations with detailed information about predicted efficacy, potential side effects, and alternative drug options, enhancing patient understanding and engagement in drug selection.

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