MEDICINE SIDE EFFECTS PREDICTION SYSTEM

Abstract

Disclosed herein is a system (100) for automated prediction of pharmacological side effects using a Long Short-Term Memory (LSTM) model, comprising a user device (102) with an interactive user interface (104) for data entry and management. The microprocessor (106) controls the system’s operations, including data input (108) and pre-processing (110) modules that ensure accurate and clean data for the prediction model. The training and testing module (112) uses LSTM to process sequential drug data, while the prediction module (114) forecasts potential side effects. The output module (116) displays results, integrated with data analytics (118) to enhance model performance. The notification module (120) ensures real-time updates to users on the prediction status. Additionally, real-time data processing (122) and data visualization (124) modules offer intuitive graphical representations of outcomes. The system supports multi-language interaction (126) and securely stores all data in a database (128), optimizing prediction accuracy and patient safety.

Specification

Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to pharmacological system, more specifically, relates to medicine side effects prediction system based on machine learning.
BACKGROUND OF THE DISCLOSURE
[0002] Pharmacological systems, specifically focusing on the prediction of drug side effects using advanced machine learning techniques. More particularly it addresses the development of a system that enhances medication efficacy and patient safety by predicting potential adverse drug reactions using sequential data models. This invention integrates drug information, patient histories, and clinical trial results with deep learning methodologies to streamline and digitize traditional side effect prediction processes, improving overall accuracy and adaptability.
[0003] Traditional system used for predicting pharmacological side effects face several disadvantages that limit their effectiveness. A major drawback is the use of static models, which are unable to account for temporal patterns in data and often rely on limited datasets from clinical trials. This static nature prevents the system from adapting to new drug combinations or real-time patient information, leading to reduced prediction accuracy. Moreover, these traditional systems frequently require separate models for feature extraction, pattern recognition, and prediction, which increases computational costs and complicates the model training process.
[0004] The reliance on manual feature engineering also contributes to a lack of scalability and adaptability, restricting the ability to continuously improve the model's predictions as new data becomes available.
[0005] In contrast, the present invention overcomes these limitations by implementing a unified framework that combines the strengths of long short-term memory networks, convolutional layers, and deep neural networks. The system efficiently handles sequential data, capturing long-term dependencies in drug and side effect information while adapting dynamically to new inputs. This approach not only improves predictive precision, reducing the likelihood of adverse reactions, but also enables real-time learning from updated drug and patient data. By integrating comprehensive datasets and automating deep feature extraction, the system eliminates the need for separate feature engineering, making it highly scalable and adaptable to evolving pharmacological data. This innovation leads to more reliable predictions, enhances patient safety through proactive risk management, and reduces clinical trial costs by providing accurate side effect predictions earlier in the drug development process.
[0006] Thus, in light of the above-stated discussion, there exists a need for a medicine side effects prediction system.
SUMMARY OF THE DISCLOSURE
[0007] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0008] According to illustrative embodiments, the present disclosure focuses on a medicine side effects prediction system which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0009] The present disclosure solves all the above major limitations of a system for medicine side effects prediction.
[0010] An objective of the present disclosure is to improve the prediction accuracy of drug side effects by using LSTM networks to process sequential data, enhancing patient safety.
[0011] Another objective of the present disclosure is to provide an automated system for continuously learning from new drug data, ensuring real-time updates and improving the relevance of side effect predictions.
[0012] Another objective of the present disclosure is to integrate data from multiple sources, including drug chemical structures and patient histories, to deliver more comprehensive predictions of side effects.
[0013] Another objective of the present disclosure is to reduce the cost and duration of clinical trials by offering reliable side effect predictions that help identify potential risks early in the drug development process.
[0014] Another objective of the present disclosure is to prevent overfitting by implementing dropout layers within the LSTM network, ensuring the model generalizes well to unseen data.
[0015] Another objective of the present disclosure is to automate data preprocessing tasks such as tokenization, padding, and label encoding, streamlining the preparation of drug and side effect data for modelling.
[0016] Yet another objective of the present disclosure is to enable healthcare providers to make proactive decisions by offering timely and precise predictions, reducing the occurrence of adverse drug reactions.
[0017] Yet another objective of the present disclosure is to allow scalability and adaptability within the system so it can accommodate new drugs and emerging side effect data as it becomes available.
[0018] Yet another objective of the present disclosure is to utilize cloud computing platforms to process large datasets, enabling efficient model training and deployment in real-world applications.
[0019] Yet another objective of the present disclosure is to improve drug safety regulations by providing regulatory bodies and pharmaceutical companies with an efficient tool for predicting side effects, supporting informed decision-making.
[0020] In light of the above, in one aspect of the present disclosure, a medicine side effects prediction system is disclosed herein. The system comprises a user device configured to control the system via an application that allow users such as researchers, medical professionals, and administrators to perform tasks like uploading drug datasets, configuring model parameters, and viewing prediction results. The system includes a user interface connected to the user device and configured to allow users to interact with the system through a secure web interface. This interface allows for data submission, model training, testing, and prediction visualization, with easy access to all functionalities and secure logins via unique user credentials. The system also includes a microprocessor connected to the user interface and configured to handle automated data processing, model training, and side effect prediction tasks further comprises. The system also includes a data input module configures to receive drug data from the user device. The system also includes a pre-processing module integrated into the microprocessor and configured to clean, normalize, and transform sequential drug-related data, including patient histories, drug interactions, and clinical reports. The system also includes a training and testing module integrated into the microprocessor and configured to train the LSTM model using sequential data including pharmacological histories and patient responses. This module further divides the data into training and testing sets. The system also includes a prediction module connected with the training and testing module and configured to predict potential side effects for given drugs. The module generates predictions using historical drugs usage data and patient characteristics, ensuring timely and accurate prediction of possible adverse reactions. The system also includes an output module connected with the prediction module and configured to present the prediction results in user friendly formats, including graphical visualizations, detailed reports, and downloadable files such as pdfs. The system also includes a database connected with the preprocessing module and configured to store all datasets, model parameters, and prediction results securely. The database ensures that user data, including drug reports, side effects records, and patient information, is stored safely and can be retrieved for further analysis or further model training.
[0021] In one embodiment, the data analytics module processes large datasets, including drug information, side effect reports, and patient history, using advanced machine learning techniques such as LSTM to generate predictions and insights.
[0022] In one embodiment, the notification module provides timely alerts to healthcare providers, patients, and researchers, notifying them of predicted side effects, new data updates, or critical findings, ensuring proactive risk management.
[0023] In one embodiment, the real time data processing module enables the system to intake and analyze incoming data in real-time, continuously updating predictions and ensuring that the system adapts to newly available information, such as recently approved medications or emerging side effect trends.
[0024] In one embodiment, the data visualization module presents the analytical results in an intuitive and interactive format, allowing users to explore data trends, side effect risks, and predictive model performance through charts, graphs, and heatmaps.
[0025] In one embodiment, the multi-languages module supports the system's use in various linguistic environments, allowing the interface and reports to be translated into multiple languages, enhancing its accessibility for international healthcare providers and pharmaceutical professionals.
[0026] In light of the above, in another aspect of the present disclosure a method for medicine side effects prediction system is disclosed herein. The method comprises allowing users such as researchers, medical professionals, and administrators to perform tasks via a user device. The method also includes allowing users to interact with the system through a secure web interface via a user interface. The method also includes handling automated data processing model training, and side effect prediction tasks further comprises via a microprocessor. The method also includes Normalizing and transform sequential drug-related data, including patient histories, drug interactions, and clinical reports via a data input module. The method also includes training the LSTM model using sequential data, including pharmacological histories and patient responses. This module further divides the data into training and testing sets via a pre-processing module. The method also includes predicting potential side effects for given drugs and module generates predictions using historical drugs via a prediction module. The method also includes connecting to the output module and is configured to provide graphical representation of prediction results via a data visualization. The method also includes configuring to present the prediction results in user friendly formats, including graphical visualizations, detailed reports, and downloadable files such as pdfs via an output module. The method also includes storing all datasets, model parameters, and predicting results securely. The database ensures that user data, including drug reports, side effects records, and patient information, is stored safely via a database.
[0027] These and other advantages will be apparent from the present application of the embodiments described herein.
[0028] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0029] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0030] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0031] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0032] FIG.1 illustrates a block diagram of a medicine side effects prediction system, in accordance with an exemplary embodiment of the present disclosure;
[0033] FIG. 2 illustrates a schematic of training the dataset used by the LSTM model, in accordance with an exemplary embodiment of the present disclosure;
[0034] FIG. 3 illustrates a flowchart of a method outlining the sequential steps for classifying for medicine side effects prediction system, in accordance with an exemplary embodiment of the present disclosure;
[0035] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0036] The medicine side effects prediction system is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0037] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0038] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0039] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0040] The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0041] The terms "having", "comprising", "including", and variations thereof signify the presence of a component.
[0042] Referring now to FIG. 1 to FIG. 3 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrate the block diagram of a medicine side effects prediction system 100, in accordance with an exemplary embodiment of the present disclosure.
[0043] The system 100 may include a device 102, a user interface 104, a microprocessor 106, a data input module 108, a pre-processing module 110, a training and testing module 112, a prediction module 114, an output module 116, a data analytics module 118, a notification module 120, a real time processing module 122, a data visualization module 124, multi-language module 126, a database module 128.
[0044] The user devices 102, such as computers, tablets, or smartphones. these devices serve as the main interaction points with the system, enabling remote access to its features.
[0045] The user interface 104, which provides a web-based front end for seamless user experience across devices. This interface acts as the main portal where users perform tasks such as data input, viewing predictions, or analysing reports.
[0046] The microprocessor 106 acts as the central processing unit for the system, orchestrating operations across the various modules. It ensures that data flows seamlessly between modules, manages system resources, and processes user requests. The CPU is responsible for task execution, system stability, and the overall coordination between components, ensuring that different operations do not conflict with one another.
[0047] The data input module 108 plays a critical role in collecting the raw data from users. This data may include drug information, reported side effects, usage contexts and other relevant inputs. The system supports various data formats, allowing structured and unstructured inputs. It also handles data validation, ensuring that the inputs conform to the required format before being passed to subsequent modules.
[0048] The pre-processing module 110, which ensures that the model learns from historical data and is validated against new data to avoid overfitting.
[0049] The training and testing module 112, where machine learning algorithms are trained using historical data. The module supports model selection, hyperparameter tuning, and cross-validation to ensure that the most accurate model is chosen. It also divides the data into training and testing sets, enabling the model to learn from the training set while being evaluated on the unseen testing data. This ensures the system is capable of generalizing to new inputs, reducing overfitting.
[0050] The prediction module 114 takes over. This module applies the trained models to new input data to generate predictions. For example, given a set of drug names and reported side effects, the model predicts potential outcomes such as adverse reactions or efficacy improvements. The predictions may be probabilistic, indicating the likelihood of different side effects, or categorical, indicating a definitive prediction for a given drug.
[0051] The output module 116, which manages the presentation of results to users. This module is responsible for formatting the predictions in a user-friendly manner, displaying relevant metrics and ensuring that the output is accessible via the user interface.
[0052] The data analytics module 118, which performs in-depth analysis of the data and predictions. This module provides insights into trends, patterns, and correlations found within the data. For example, it may reveal that certain drugs consistently show higher side effect rates in specific patient demographics. Such insights are useful for improving decision-making in clinical trials or prescribing practices.
[0053] The notification module 120 ensures that users are alerted to important updates or new predictions. For instance, clinicians may be notified if a drug they are prescribing is predicted to have high-risk side effects in real-time. The module can send notifications via email, SMS, or other integrated communication channels, ensuring timely delivery of critical information.
[0054] The real-time data processing module 122 allows the system to handle data inputs in real-time. For example, if new patient data or drug reports are entered into the system, this module ensures that the data is processed, and predictions are made without significant delays. This is especially crucial in dynamic environments such as hospitals or clinical research settings, where decisions need to be made quickly.
[0055] The data visualization module 124 enhances the interpretability of the system's outputs by transforming raw data and predictions into visual formats, such as graphs, charts, or dashboards. This enables users to easily comprehend complex information and make data-driven decisions. For example, a researcher could visualize trends in side effect occurrences over time, or a doctor could quickly assess the predicted risk levels for different medications.
[0056] The multi-language module 126, which allows the interface and outputs to be translated into multiple languages. This ensures that users from different regions can interact with the system in their preferred language, broadening the system's usability across international borders.
[0057] The database 128, which serves as the central repository for all stored data. This includes historical input data, pre-processed data, trained models, prediction outputs, and logs of user interactions. The database is designed for scalability and security, ensuring that large amounts of data can be stored and retrieved efficiently while maintaining privacy.
[0058] FIG. 2 illustrates a schematic of training the dataset used by the LSTM model, in accordance with an exemplary embodiment of the present disclosure;
[0059] The schematic 200 processes original data 202, related to drugs, which may include essential details such as the drug's name, reported side effects, and its intended usage.
[0060] The schematic combines 204 pieces of information into a single text string for easier processing. This step results in a combined text string.
[0061] The text string is prepared, the system 200 employs a tokenizer to convert each unique word within the combined string into a corresponding integer. Tokenizer 206 generates a mapping between words and integers.
[0062] The tokenizer is applied, the combined text string is transformed into a sequence of numbers based on the word-to-integer mapping.
[0063] To ensure uniformity across all sequences, the system 200 applies padding 210 to standardize the length of each sequence. padding 210 adds zeroes to the start of shorter sequences.
[0064] FIG. 3 illustrates a flowchart of a method outlining the sequential steps for classifying for medicine side effects prediction system, in accordance with an exemplary embodiment of the present disclosure;
[0065] The method 300 may include, at step 302, configure to control the system via an application that allow users such as researchers, medical professionals, and administrators to perform tasks like uploading drug datasets, configuring model parameters, and viewing prediction results via a user device, at step 304, Allow users to interact with the system through a secure web interface via a user interface, at step 306, Configure to handle automated data processing model training, and side effect prediction tasks further comprises via a microprocessor, at step 308, Configure to receive drug data from the user device Normalizing and transform sequential drug-related data, including patient histories, drug interactions, and clinical reports via a data input module, at step 310, train the LSTM model using sequential data including pharmacological histories and patent responses. This module further divides the data into training and testing sets via a pre-processing module, at step 312, predict potential side effects for given drugs. The module generates predictions using historical drugs usage data and patient characteristics, ensuring timely and accurate prediction of possible adverse reactions via a prediction module, at step 314, connecting to the output module and is configured to provide graphical representation of prediction results via a data visualization, at step 316 configure to present the prediction results in user friendly formats, including graphical visualizations, detailed reports, and downloadable files such as pdfs via an output module, at step 318 store all datasets, model parameters, and prediction results securely. The database ensures that user data, including drug reports, side effects records, and patient information, is stored safely and can be retrieved for further analysis or further model training via a database.
[0066] At step 302, configure to control the system via an application that allows users such as researchers, medical professionals, and administrators to perform tasks like uploading drug datasets, configuring model parameters, and viewing prediction results from various devices.
[0067] At step 304, allow users to interact with the system through a secure web interface where authorized users can log in, navigate features, and upload or analyze data, with security measures like encrypted authentication.
[0068] At step 306, configure to handle automated data processing model training, and side effect prediction tasks such as data processing, model training, and prediction generation, ensuring smooth and efficient operation.
[0069] At step 308, configure to receive drug data from the user device normalizing and transforming sequential drug-related data, including patient histories, drug interactions, and clinical reports normalizing it, and preparing it for analysis, ensuring consistency in data formats.
[0070] At step 310, train the LSTM model using sequential data including pharmacological histories and patent responses. This module further divides the data into training and testing sets allowing the model to learn patterns and predict outcomes.
[0071] At step 312, predict potential side effects for given drugs. The module generates predictions using historical drug usage data and patient characteristics, ensuring timely and accurate prediction of possible adverse reactions and helping users make informed decisions about drug safety.
[0072] At step 314, connecting to the output module and being configured to provide graphical representations of prediction results are converted into easy-to-understand graphs and charts, allowing users to quickly interpret risks and trends associated with different drugs.
[0073] At step 316, configure to present the prediction results in user friendly formats, including graphical visualizations, detailed reports, and downloadable files such as pdfs for easy review and sharing.
[0074] At step 318, the database securely stores all datasets, model configurations, and prediction results. It ensures safe retrieval for further analysis and future model training.
[0075] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0076] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0077] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0078] Disjunctive language such as the phrase "at least one of X, Y, Z," unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0079] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A system (100) for predicting medicine side effects, wherein the system (100) comprises:
a user device (102) configured to control the system (100) via an application that allows users to upload drug datasets, adjust model parameters, and view prediction results;
a user interface (104) integrated into the user device (102) and configured to allow users to interact with the system (100);
a microprocessor (106) connected to the user interface (104) and configured to handle automated data processing, model training, and side effect prediction tasks, wherein the microprocessor (106) further comprises:
a data input module (108) configured to receive drug data from the user device;
a pre-processing module (110) configured to clean, normalize, and transform sequential drug-related data, including patient histories, drug interactions, and clinical reports;
a prediction module (114) configured to predict potential side effects for given drugs based a trained long short-term memory model; and
a data visualization module (124) configured to provide a graphical representation of prediction results, including risk levels of specific side effects and the distribution of side effects across patient demographics;
an output module (116) configured to present the prediction results in user friendly formats on the user device (102), including graphical visualizations, detailed reports, and downloadable files such as pdfs;

2. The system (100), as claimed in claim 1, wherein the prediction module (114) utilizes a pre-trained machine learning model, which is stored in a cloud database and trained on historical pharmaceutical data.
3. The system (100), as claimed in claim 1, wherein the system (100) further comprises the data analytics module (118) configured to perform automated data filtering, sorting, and categorization of side effects reports, allowing users to analyze historical patterns and improve prediction accuracy.
4. The system (100), as claimed in claim 1, wherein the system (100) further comprises a notification module (120) configured to automatically alert medical professionals and users when the system predicts high risk side effects.
5. The system (100) as claimed in claim 1, wherein the system (100) further comprises a real time data processing module (122) that is integrated into the microprocessor (106) and is configured to handle continuous streaming of new drug usage data.
6. The system (100) as claimed in claim 1, wherein the system (100) further comprises a training and testing module (112), is integrated into the microprocessor (106) and is configured to divide the processed data into training and testing datasets, and to train the LSTM model using sequential data, including pharmacological histories and patient responses.
7. The system (100) as claimed in claim 1, wherein the system (100) is further configured to support multiple languages to provide the system (100) functionalities, including input, predictions, and results visualization, in multiple languages.
8. A method (300) for medicine side effects prediction system, wherein the method (300) comprises:
allowing users such as researchers, medical professionals, and administrators to perform tasks via a user device (302);
allowing users to interact with the system (100) through a secure web interface via a user interface (304);
handling automated data processing model training, and side effect prediction tasks further comprises via a microprocessor (306);
normalizing and transforming sequential drug-related data, including patient histories via a data input module (308);
training the LSTM model using sequential data, including pharmacological histories and patient responses and this module further divides the data into training and testing sets via a pre-processing module (310);
predicting potential side effects for given drugs and the module generates predictions using historical drug via a prediction module (312);
connecting to the output module and is configured to provide a graphical representation of prediction results via a data visualization (314);
configuring to present the prediction results in user friendly formats, including graphical visualizations, detailed reports, and downloadable files such as pdfs via an output module (316);
storing all datasets, model parameters, and prediction results securely in the database ensures that user data, including drug reports, side effects records, and patient information, is stored safely via a database (318).

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