AI-powered cloud data analytics platform for early detection and prevention of diseases

Abstract

A system with a data accumulation module for collecting patient information from various sources, including electronic health records (EHRs), practical devices, and sensors.

Description

[0001] This utility model relates to an AI-powered cloud data analytics platform for early disease detection and prevention. The platform integrates advanced data analytics and machine learning algorithms to analyze patient data and provide proactive health monitoring and early intervention strategies. BACKGROUND:

[0002] Timely detection and proactive measures against diseases are essential to minimize healthcare costs and improve treatment outcomes. Traditional healthcare systems often struggle to manage large-scale data sets for rapid diagnosis and intervention. This utility model addresses these challenges by leveraging cloud computing and artificial intelligence to examine patient data, facilitate early disease detection, and drive proactive health management. The advent of artificial intelligence (AI) is fundamentally changing the healthcare landscape, reshaping methods for diagnosing and treating diseases.The application of artificial intelligence in medical diagnosis utilizes advanced methods such as machine learning and deep learning to examine patient data, including medical history, laboratory test results, genetic information, and medical imaging. These AI-driven systems significantly improve diagnostic accuracy by detecting complex patterns and anomalies in the data. The key features of artificial intelligence in the field of medical diagnosis include data processing, pattern recognition, prediction, and decision support. The integration of artificial intelligence in the healthcare sector has witnessed remarkable progress, from basic rule-based systems such as MYCIN to the fields of machine learning and deep learning.Artificial intelligence systems currently serve as essential elements in personalized medicine, facilitating treatment planning and improving patient management. The integration of real-time data with predictive analytics enables a future-oriented approach to healthcare. Summary of the invention:

[0003] The utility model proposes a cloud-based platform that uses AI and machine learning algorithms to analyze patient data. The platform includes modules for data collection, storage, processing, and analysis, all hosted on a secure cloud infrastructure. The system provides early detection alerts and preventive healthcare recommendations based on real-time data analytics.

[0004] The implementation of AI has significantly improved diagnostic precision, accelerated processes, and improved patient outcomes in a wide range of medical fields. This technology has proven extremely useful in various areas, including radiology, pathology, chronic disease prognosis, and earlier identification of rare and genetic disorders, as well as in dermatology, ophthalmology, neuroscience, and emergency medicine.

[0005] Medicine, along with remote patient monitoring. Radiology uses artificial intelligence systems to improve the precision and effectiveness of imaging techniques such as near-infrared (NIR) X-rays, CT scans, and ultrasound. These technologies can detect and analyze abnormalities more precisely than manual evaluations, leading to faster diagnosis and reporting times. Pathology uses artificial intelligence to examine complex biological data, thus improving the interpretation of lab results and abnormality slides. Artificial intelligence instruments can detect irregular patterns in blood test results and perform automated screening for infectious diseases.

[0006] The application of predictive analytics in chronic diseases involves using artificial intelligence to predict the onset and progression of chronic diseases by examining large-scale patient data sets. This contributes to proactive disease management, reduced hospital admissions, and improved patient compliance with preventive measures. Uncommon and hereditary diseases often go undetected or are diagnosed late due to their complexity and rarity. Artificial intelligence represents a promising advance in this area by detecting complex genetic patterns and abnormalities.AI systems in dermatology and skin disease detection use advanced analytics of high-resolution images of moles or lesions, enabling the differentiation between benign and cancerous growths and predicting the recurrence of skin diseases. Ophthalmology uses artificial intelligence to facilitate the early detection of eye diseases such as diabetic retinopathy, glaucoma, and macular degeneration. Neurological disorders are identified using brain imaging techniques, seizure prediction methods, and assessments for stroke diagnosis. In emergency medicine, artificial intelligence increases the efficiency of triage and diagnostic procedures by systematically prioritizing patient cases by severity, assessing injuries, and utilizing virtual assistants. Figure 1

[0007] The image is a flowchart depicting the data processing and analysis process in a healthcare system. The flowchart begins with "User Interface" and leads to "Data Acquisition." From "Data Acquisition," the data flows to "Cloud Storage" and "Data Analysis and Machine Learning." "Cloud Storage" connects to "AI and Machine Learning Module," which in turn connects to "Real-Time Monitoring and Preventive Insights."

[0008] "Real-time monitoring and preventive insights" is linked to "Patient data (EHR, wearables, sensors, etc.)." This flowchart illustrates the integration of various components for efficient data management and analysis and highlights the role of AI and machine learning in providing real-time monitoring and preventive insights based on patient data. Figure 2

[0009] The image is a flowchart illustrating a systematic process for data processing in healthcare. The flowchart includes four main steps: 1. Data collection 2. Data analysis and machine learning 3. Data processing and analysis 4. Early detection and prevention

[0010] Each step is connected by arrows that indicate the sequential flow from one step to the next. This process illustrates the path from initial data collection through advanced analytics to proactive health management. Figure 3

[0011] The attached figure is a flowchart illustrating the process of generating personalized treatment recommendations based on patient data. The flowchart consists of four main components: 1. Patient data (EHR, wearables, sensors, etc.): This is the first input and includes electronic health records (EHR), wearable device data, and sensor data. 2. Data preprocessing module: This module processes the raw patient data to prepare it for analysis. 3. AI and Machine Learning Module: This module uses artificial intelligence and machine learning techniques to analyze the preprocessed data. 4. Personalized treatment recommendations: The end result is personalized treatment recommendations based on the analysis performed by the AI ​​and machine learning module. Detailed description of Figure 1 1. Patient data (EHR, wearables, sensors, etc.) ◯ Description: This block represents the sources of patient data. It includes electronic health records (EHR), wearable devices such as fitness trackers, and various sensors for monitoring health parameters. ◯ Role: These data sources provide the raw data required for further analysis and processing. 2. Data collection ◯ Description : Data collection is the process of gathering information from the various sources mentioned above. ◯ Function: It ensures that all relevant health data of the patient is recorded and made available for analysis. 3. Data preprocessing module ◯ Description : This module preprocesses the collected data and includes steps such as cleaning, normalization and transformation. ◯ Role: Preprocessing prepares the data for analysis by removing errors, handling missing values, and converting data into an appropriate format. 4. Cloud storage ◯ Description : Cloud storage is used to store the preprocessed data securely and reliably. ◯ Role: It provides scalable storage solutions and ensures that data can be accessed when needed. 5. AI and Machine Learning Module ◯ Description: This module applies artificial intelligence (AI) and machine learning (ML) algorithms to the preprocessed data. ◯ Role: AI and ML techniques are used to analyze the data, identify patterns, and gain insights that can support medical decisions. 6. Data processing and analysis ◯ Description: This block represents the core phase of data processing and analysis. ◯ Role : It involves detailed analysis of data to gain meaningful information and insights. 7. Real-time monitoring and preventive insights ◯ Description: This component enables real-time monitoring of patient health status and generates preventive insights. ◯ Role: Real-time data helps in the early detection of potential health problems and enables timely interventions. 8. Personalized treatment recommendations ◯ Description: Personalized treatment recommendations are generated based on insights from AI and ML. ◯ Function: These recommendations are tailored to the individual patient’s health data and ensure personalized treatment and care. Detailed description of Figure 2 1. Data collection • Description: This block represents the first step in collecting health data from different sources. • Role: Health data can come from electronic health records (EHRs), wearable devices, sensors, and other medical devices. This is the basic step where raw data is collected for further processing. 2. Data preprocessing module • Description : This module is responsible for preprocessing the collected data. • Role: This includes cleaning the data, handling missing values, and converting the data into a format suitable for analysis. This step ensures that the data is accurate and ready for further processing. 3. Cloud storage • Description : This block indicates the storage of preprocessed data in the cloud. • Role: Cloud storage provides a secure and scalable solution for storing large amounts of data. It ensures that the data can be accessed and retrieved for analysis when needed. 4. AI and Machine Learning Module • Description: This module applies artificial intelligence (AI) and machine learning (ML) techniques to the data. • Role: AI and ML algorithms analyze the data to uncover patterns, gain insights, and predict outcomes. This step is critical for making informed medical decisions. 5. Real-time monitoring and preventive insights • Description: This component focuses on real-time monitoring of health data and providing preventive insights. • Function: It continuously monitors the patient's health status in real time and generates alerts or insights to prevent potential health problems. This proactive approach helps with early detection and timely intervention. 6. Personalized treatment recommendations • Description : Personalized treatment recommendations are generated based on the analysis . • Role : This step ensures that treatment plans are tailored to the individual needs of patients, thereby improving the effectiveness of medical interventions.

[0012] This block diagram shows the structured flow of healthcare data from collection to analysis, leading to real-time monitoring and personalized treatment recommendations. Each part plays a critical role in ensuring comprehensive and effective healthcare through advanced data management and AI techniques. Detailed description of Figure 3 1. Patient data (EHR, wearables, sensors, etc.) • Description: This block represents the first sources of patient health data. This data can come from electronic health records (EHR), wearable devices such as fitness trackers, and various health monitoring sensors. • Role : These devices and records provide comprehensive health data and capture various physiological parameters that are crucial for subsequent analysis and processing. 2. Data collection • Description : Data collection involves collecting health data from the various sources mentioned above. • Role: This step ensures that all relevant patient health information is captured accurately and in a timely manner for further processing. 3. Data preprocessing module • Description: This module preprocesses the collected data by cleaning, normalizing and converting it into a format suitable for analysis. • Role: Preprocessing is critical to ensure that the data is of high quality, error-free and ready for analysis. 4. Cloud storage • Description : Cloud storage is used to securely store the preprocessed health data. • Role: It provides a scalable and reliable storage solution and ensures that data is accessible for analysis when needed. 5. AI and Machine Learning Module • Description: This module applies artificial intelligence (AI) and machine learning (ML) algorithms to the preprocessed data. • Role: The AI ​​and ML algorithms analyze the data to identify patterns, gain insights, and predict outcomes that can help in medical decisions. 6. Data processing and analysis • Description: This block represents the core phase of data processing and analysis, in which detailed insights are extracted from the data. • Role: Advanced data processing techniques and analytics are used to extract meaningful information that can be used for healthcare decision making. 7. Real-time monitoring and preventive insights • Description: This component enables real-time monitoring of patient health status and generates preventive insights. • Role: Continuous monitoring helps in the early detection of potential health problems and enables timely interventions, improving patient outcomes. 8. Personalized treatment recommendations • Description: Personalized treatment recommendations are generated based on the insights gained from AI and ML analysis. • Role: These recommendations are tailored to each patient's health data to ensure that treatment plans are customized to their specific needs.

Claims

[1] A system with a data accumulation module for collecting patient information from various sources, including electronic health records (EHR), hands-on devices, and sensors. [2] A computing device with a cloud-based storage module that ensures the secure storage of collected data in a cloud environment. [3] A system with a data mining module for preprocessing the collected data, eliminating noise and standardizing formats for analysis purposes. [4] A computing device with an AI and machine learning module to accurately process the data, examine and detect patterns that indicate the early onset of a disease. [5] A system with an early detection and prevention module designed to generate alerts for the timely detection of diseases and provide recommendations for preventive health measures. [6] A computer device with a user interface that provides healthcare professionals with easy and efficient monitoring of alerts and patient care.

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