Cloud-based AI-powered data analytics platform for personalized healthcare and therapy

Abstract

A cloud-based, AI-powered data analytics platform for personalized healthcare and therapy, consisting of: • A data collection module configured to capture patient data from various sources, including electronic health records (EHRs), wearable devices, and sensors; • A cloud storage module configured to securely store the collected data in a cloud environment; • A data processing module configured to preprocess the collected data by removing noise and standardizing formats for analysis; • A computer and machine learning module configured to analyze pre-processed data and identify patterns to generate personalized healthcare insights; • A user interface configured to provide medical professionals with an intuitive interface for interacting with the system, viewing insights, and managing patient care.

Description

Technical background:Technical area:

[0001] This utility model relates to a cloud-based AI-powered data analytics platform for providing personalized healthcare and therapy. The platform integrates advanced data analytics and machine learning algorithms to analyze patient data and provide personalized treatment recommendations and health monitoring.

[0002] The goal of personalized healthcare is to individualize medical treatment according to each patient's characteristics, needs, and choices. Traditional healthcare systems sometimes face challenges in providing personalized treatments due to their limited data analytics capabilities. These issues are addressed by this utility model, which leverages cloud computing and artificial intelligence to process large amounts of patient data, enabling accurate and personalized suggestions from healthcare professionals.

[0003] The use of machine learning, patient information, and analytics is being used in modern medicine to improve patient engagement and evaluate treatment effectiveness. One of the most important aspects of modern medicine is tailoring therapies to the individual patient. This method helps identify patient needs and improves the overall patient experience.

[0004] The utility model describes a data analytics platform based on artificial intelligence and hosted in the cloud. Its purpose is to provide personalized healthcare and treatment. To analyze patient data, the platform integrates powerful data analytics and machine learning algorithms. This gives the platform the ability to provide customized treatment suggestions and health monitoring. Personalized healthcare is about individualizing medical treatment according to each patient's characteristics, needs, and preferences. This is the intent of personalized healthcare. Due to their limited data analytics capabilities, traditional healthcare systems often encounter difficulties in providing medical care tailored to the individual patient.These problems are addressed by this utility model, which leverages cloud computing and artificial intelligence to manage vast amounts of patient data, enabling medical experts to provide precise, personalized advice.

[0005] In modern medicine, machine learning, patient information, and analytics are used to improve patient engagement and evaluate treatment effectiveness. The ability to tailor treatments to the specific needs of individual patients is among the most significant achievements of modern medicine. The utility model proposes a platform that performs patient data analyses using machine learning and artificial intelligence algorithms. This platform would be located in the cloud. The platform consists of modules stored on a secure cloud architecture and is responsible for tasks such as data acquisition, storage, processing, and interpretation. Thanks to its ability to interpret data in real time, The system can generate personalized health insights and treatment recommendations. To facilitate interaction between healthcare professionals and the system, as well as the display of insights and management of patient care, the utility model is designed to provide an intuitive user interface. This system includes a data collection module designed to collect patient data from various sources, such as electronic health records (EHRs), wearable devices, and sensors.

[0006] The utility model proposes a cloud-hosted platform that uses machine learning and AI algorithms to analyze patient data. The platform consists of modules housed in a secure cloud architecture that are responsible for data collection, storage, processing, and interpretation. By using real-time data processing, the system is capable of generating personalized health insights and treatment suggestions.

[0007] The utility model focuses on a cloud-based, AI-powered data analytics platform for personalized healthcare and therapy. The platform integrates cutting-edge machine learning and data analytics algorithms to analyze patient data in real time and deliver personalized treatment recommendations and continuous health monitoring.

[0008] This technological innovation eliminates limitations in traditional healthcare systems, which often struggle with data analysis due to resource constraints. Leveraging cloud computing, the system processes large amounts of data from various sources, such as electronic health records (EHRs), wearable devices, and sensors. This ensures that healthcare professionals can provide accurate and personalized recommendations based on the analyzed data.

[0009] The platform consists of several interconnected modules: 1. Data collection module: Collects patient data from various sources. 2. Cloud storage module: Stores the collected data securely while maintaining accessibility. 3. Data processing module: Preprocesses data by removing noise and standardizing formats for consistent analysis. 4. AI and Machine Learning Module: Applies sophisticated algorithms to recognize patterns and enable personalized insights. 5. User Interface: The goal is to provide healthcare professionals with an intuitive user interface for seamless interaction with the system, including viewing insights and managing patient care.

[0010] This innovative approach transforms traditional healthcare into a data-driven, patient-centered model, improving treatment effectiveness and the patient experience. Short descriptions of three characters: 1. Fig. : System Architecture A schematic overview of the platform's architecture, showing the connection between data collection, processing, cloud storage, AI analytics, and the user interface. 2. Fig. : Data flow diagram A visual representation of the flow of data within the platform, from patient data capture to cloud storage and processing to personalized insights for healthcare providers. 3. Fig. : Workflow Overview Demonstrates the system's workflow, focusing on real-time monitoring, patient data entry, and the generation of actionable therapy recommendations. Detailed description of the figure (example: system architecture - Figure 1):

[0011] Fig. illustrates the platform's system architecture and highlights its key components and their connections: 1. Data Acquisition Module: Interfaces to external data sources such as EHRs, wearable devices, and IoT sensors. It ensures seamless data aggregation from multiple input streams. 2. Cloud Storage Module: Uses a secure cloud infrastructure for data storage. It provides scalability and ensures compliance with healthcare data privacy and security standards. 3. Data processing module: Processes raw data by removing inconsistencies, noise, and duplicates. This step ensures consistency and prepares the data for subsequent analysis. 4. AI and Machine Learning Module: Uses advanced algorithms to analyze preprocessed data. This module identifies patterns, tracks health trends, and generates personalized treatment recommendations. 5. User Interface: Serves as an interaction layer for healthcare professionals. It features a high-resolution touchscreen display for visualizing data insights, managing patient profiles, and monitoring progress in real time.

[0012] Each module communicates seamlessly via secure protocols, forming a cohesive system that delivers robust, personalized healthcare solutions. Detailed description of Figure 2: Data flow diagram

[0013] Fig. shows the data flow diagram of the cloud-based AI-powered data analytics platform for personalized healthcare and therapy. This diagram describes the data flow across the system components, from initial capture to actionable insights. Below is a detailed description of each component and its role in the data flow: 1. Data input sources: ◯ Electronic health records (EHRs): Contains patient medical history, diagnoses, and treatment plans provided by healthcare facilities. ◯ Wearable devices: Collects real-time data such as heart rate, physical activity, and sleep patterns. ◯ Sensors and IoT devices: Collect environment-specific health-related data such as air quality and temperature. 2. Data acquisition module: This module serves as the entry point for all patient-related data. It consolidates inputs from the above-mentioned sources, ensuring seamless integration and minimizing data loss. 3. Cloud storage module: After capture, the data is securely transferred to the cloud storage system. This module encrypts and organizes the data for efficient access, scalability, and compliance with healthcare data protection standards (e.g., GDPR or HIPAA). 4. Data processing module: ◯ Preprocessing : Filters and cleans the raw data by removing inconsistencies, duplicates and noise. ◯ Standardization : Converts different formats into a uniform structure for optimized analysis. 5. Module “HI and Machine Learning” : ◯ Pattern recognition: Identifies trends and correlations in the preprocessed data. ◯ Predictive models: Use predictive analytics to predict health outcomes and recommend therapies. ◯ Real-time monitoring: Continuously evaluates incoming data to detect anomalies or urgent health risks. 6. Generating insights: The processed and analyzed data is transformed into actionable insights, such as personalized treatment recommendations, health alerts, or progress reports. 7. User interface: This is the final point in the data flow where medical professionals access the system. It provides: ◯ Visualization of insights: charts, graphs, and reports that summarize the analysis. ◯ Patient management tools: Options to update profiles, track patient progress, and adjust treatment plans based on insights. ◯ Real-time notifications: Alerts for all critical health conditions identified by the AI ​​module. Total data flow:

[0014] The process begins with data input from external sources, continues with secure storage and preprocessing, undergoes advanced AI analysis, and concludes with a user-friendly output for healthcare professionals. This efficient and seamless workflow ensures that healthcare providers receive timely, accurate, and actionable recommendations for personalized therapy and monitoring. Detailed description of Figure 3: Workflow overview

[0015] Fig.Shows the workflow overview of the cloud-based AI-powered data analytics platform for personalized healthcare and therapy. This diagram depicts the sequence of operations and interactions between the system components, emphasizing the real-time and iterative nature of the processes.

[0016] Below is a detailed explanation of the workflow and its components: 1. Data collection and entry: • Data sources: Patient data is collected from various sources, including: ◯ Electronic health records (EHRs): medical histories, laboratory results, and diagnostic information. ◯ Wearable devices: Biometric data such as heart rate, blood pressure and activity level. ◯ IoT sensors: environmental and contextual data (e.g. temperature, humidity). • Integration : The system integrates these different data sources into a unified input stream via the data acquisition module . 2. Data preprocessing: • Noise removal: Eliminates irrelevant or erroneous data points. • Data standardization: Converts different data formats into a standardized structure to ensure compatibility and accurate analysis. • Validation: Ensures the integrity and completeness of the collected data before proceeding with the analysis. 3. AI-powered analysis: • Pattern recognition: The AI ​​and machine learning module detects patterns, correlations and trends in the pre-processed data. • Predictive models: Use predictive analytics to identify potential health risks, treatment effectiveness, or disease progression. • Customization: Adapts results to the specific needs and characteristics of each patient. 4. Real-time monitoring: • Continuously monitors incoming data streams for changes or anomalies. • Provides real-time alerts and updates, enabling timely intervention by medical professionals. 5. Generating personalized insights: • Therapeutic Recommendations : Provides customized treatment plans, medication suggestions, and lifestyle changes based on the analysis. • Health alerts: Indicates critical problems, such as abnormal vital signs, that require immediate attention. • Progress reports: Provides regular updates on patient health trends and treatment effectiveness. 6. User interaction via intuitive user interface: • Visualization tools: Displays results using interactive dashboards, graphs, and charts to support decision-making. • Patient management: Enables healthcare professionals to: ◯ Review the findings. ◯ Update patient profiles. ◯ Adjust treatment plans. • Real-time notifications: Sends alerts and reminders to healthcare providers in urgent cases. 7. Feedback loop: • The workflow includes a feedback mechanism through which healthcare providers can enter additional data, change parameters, or provide observations. This data is integrated back into the system to refine future analyses and recommendations.

Claims

[1] A cloud-based AI-powered data analytics platform for personalized healthcare and therapy, consisting of: • A data collection module configured to collect patient data from various sources, including electronic health records (EHR), wearable devices, and sensors; • A cloud storage module configured to securely store the collected data in a cloud environment; • A data processing module configured to preprocess the collected data by removing noise and standardizing formats for analysis; • An AI and machine learning module configured to analyze the pre-processed data and identify patterns to generate personalized healthcare insights; • A user interface configured to provide healthcare professionals with an intuitive interface to interact with the system, view insights, and manage patient care. [2] The platform of claim 1, wherein the AI and machine learning module further comprises advanced algorithms to provide real-time monitoring and personalized therapy recommendations. [3] The platform of claim 1, wherein the user interface comprises a high-resolution touchscreen display for intuitive navigation and data visualization.

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