SCREENING SYSTEM FOR REDUCING TRANSFUSION-TRANSMISSIBLE INFECTION RISKS

Abstract

The invention relates to a demographic-based screening system (100) for reducing transfusion-transmissible infection (TTI) risks in blood donation. The system includes a data input module (101) to collect and organize demographic data from potential donors, and a demographic analysis engine (102) to process this information using machine learning and statistical models to identify correlations between demographic characteristics and TTI risks. A risk assessment module (103) calculates a personalized risk score for each donor, and a recommendation generator (104) produces tailored screening guidance. The system features a user interface (109) for healthcare professionals to input data and review recommendations, and continuously updates its risk models using machine learning (108). Additional functionalities include geospatial analysis, AI-powered image analysis, and blockchain-based data management, with the potential for offline data collection and privacy-preserving federated learning. The system adapts dynamically to new data and outcomes to enhance blood donation safety.

Specification

Description:The following is a step-by-step description of the invention, detailing the components, and their functionalities mentioned below:

The core system comprises several interconnected modules that work together to provide comprehensive demographic-based screening system (100) for blood donors. These modules include:

Data Input Module (101) is essentially the basic intake module that collects and aligns the pertinent information of demographics about the potential blood donor. It has a user-friendly interface which makes data input easy and error-free while still allowing for smooth use. In addition, it possesses robust interface capabilities which easily tie up with in-place management systems of hospitals or blood banks. The module comprises complex validation mechanisms which check the input for accuracy and completeness, thus ensuring information integrity and adequately collected data that is helpful in effective management of donors.

Demographic Analysis Engine (102) is a sophisticated module that interrogates incoming data for complex patterns and correlations between demographic characteristics and TTI risks. Advanced statistical models and machine learning modules are used in the robust engine to identify subtle relationships and trends in that data. The engine dynamically refines its predictive capabilities by tapping into comprehensive databases of historic incidence data for TTI across highly stratified demographic groups, providing health professionals with key decisions that make a difference.
Important features include:
- Advanced statistical modelling for high-level data analysis.
- Pattern recognition and anomaly detection modules using machine learning.
- The integration of a vast database of incidence of historical TTI data in order to identify demographic-specific risk factors.

Risk Assessment Module (103) calculates a risk score for each potential donor based on demographic analysis. It uses a proprietary module that measures age, location, and medical history among other factors. Dynamic risk thresholds are adjusted over time to changes in emerging trends of Transfusion-Transmitted Infections. This module provides a proactive approach to donor screening and blood supply safety with constant updates aligned to current research. Its objective is the safe enhancement of the blood supply.

Recommendation Generator (104) produces personalized screening recommendations for healthcare professionals, streamlining the donor evaluation process. This expert system leverages a rule-based approach, incorporating current blood screening guidelines and protocols. The features include:

- Rule-based expert system for generating evidence-based recommendations.
- Customizable templates to accommodate varying healthcare settings.
- Seamless integration with established guidelines and protocols.
- Dynamic updates to reflect evolving research and regulatory changes.

Demographic Data Collection (105) module gathers a comprehensive array of information from potential blood donors, including age, gender, ethnicity, language, occupation, lifestyle factors, geographical location, travel history, educational background, socioeconomic indicators, medical history, family background, residency status, and behavioural indicators. This extensive data collection enables accurate identification of potential risk factors, informs donor eligibility, and enhances blood safety protocols. By considering these diverse demographic factors, the system ensures a secure and reliable blood supply, safeguarding the health of recipients and supporting informed decision-making among healthcare professionals.

Risk Factor Identification (106) module analyses demographic data to identify potential risk factors for TTI. Correlation analysis establishes the link between demographic traits and documented cases of TTI, shedding light on associations and trends. Emerging risk patterns are detected in specific demographic groups. Their profiles of risk factors are continuously updated with new data as well as research findings. High-risk groups and vulnerable populations are pinpointed. It enhances blood safety and informs the donor screening strategy that is evidence-based. It identifies risk factors, thus allowing the health worker to take focused and targeted measures to avoid the risk of TTI.

Screening Recommendations (107) module generates personalized guidance for blood donors, ensuring optimal safety protocols. Recommendations include additional serological tests, specific donor interview questions, or temporary, permanent deferral advice based on risk assessment. Donors with elevated risk scores are flagged for follow-up protocols. High-risk demographics or travel histories trigger enhanced screening. These evidence-based recommendations empower healthcare professionals to make informed decisions. They minimize Transfusion-Transmitted Infection (TTI) risks and safeguard the blood supply. This tailored approach optimizes donor screening and recipient safety. healthcare providers confidently proceed with blood transfusions.

Machine Learning Modules (108) enhance risk assessment capabilities using advanced modules. Neural networks identify complex patterns in large datasets, while decision trees ensure transparent decision-making. Ensemble methods combine multiple models for improved accuracy. Reinforcement learning adapts to evolving Transfusion-Transmitted Infection (TTI) trends. These modules continuously refine risk assessments, optimizing donor screening. Artificial intelligence improves blood safety and informs data-driven decisions. The system stays ahead of emerging TTI risks, ensuring a safer blood supply.

User Interface (109) offers a user-friendly experience for healthcare professionals, streamlining donor screening and enhancing decision-making. Intuitive data input forms simplify donor information entry. Risk assessments are clearly visualized for quick understanding. Recommendations are displayed in an easy-to-understand format. Customizable settings adapt to diverse healthcare environments and workflows. This interface minimizes errors and optimizes efficiency. Healthcare professionals navigate the system effortlessly, accessing critical information with ease. Ultimately, ensuring blood safety and recipient well-being.

A method (200) for demographic-based screening to reduce transfusion-transmissible infection (TTI) risks in blood donation involves several steps:
Collecting demographic data from potential blood donors using a data input module (201): In this initial step, a data input module is employed to gather demographic information from potential blood donors. The data collected typically includes factors such as age, gender, ethnicity, geographical location, medical history, and lifestyle habits. The information is entered manually by the donor or healthcare professionals, and extracted from existing databases if available. The aim of this step is to ensure that a broad range of demographic variables is captured, as these are essential in identifying potential risk factors that influence the likelihood of transfusion-transmissible infections (TTIs).

Analysing the collected demographic data using a demographic analysis engine to identify potential risk factors associated with various demographic characteristics (202): Once the demographic data is collected, it is processed by a demographic analysis engine. This system examines the data for patterns and correlations between certain demographic characteristics and TTI risks. For example, certain regions or population groups is statistically more likely to carry specific infectious diseases. The analysis engine utilizes historical data, epidemiological studies, and pre-established risk profiles to highlight potential risk factors. This step is critical for identifying at-risk groups and individuals based on their demographic attributes.

Calculating a personalized risk score for each donor using a risk assessment module (203): After identifying demographic-based risk factors, a personalized risk score is calculated for each donor through a risk assessment module. This score quantifies the donor's overall likelihood of carrying a transfusion-transmissible infection. The score is derived using module that weighs each risk factor based on its relevance to specific infections. The module integrate additional donor-specific factors like medical history, previous donations, or travel history, thus creating a comprehensive assessment that aids in donor risk stratification.

Generating tailored screening recommendations using a recommendation generator based on the calculated risk scores (204): Based on the calculated risk scores, the system then generates customized screening recommendations for each donor. A recommendation generator evaluates the risk levels and suggests appropriate next steps for screening or deferral. For example, a donor with a high-risk score might be advised to undergo further medical testing, while a low-risk donor might be cleared for donation. The recommendations are tailored to ensure that screening processes are efficient, minimizing unnecessary tests while ensuring high-risk individuals receive appropriate care.

Presenting the risk assessments and recommendations through a user interface to healthcare professionals (205): In this step, the calculated risk scores and screening recommendations are presented to healthcare professionals via a user-friendly interface. The interface displays the donor's risk profile along with clear guidelines on the recommended course of action. This includes whether to approve, defer, or request additional tests from the donor. Healthcare professionals also interact with the system to manually adjust or verify the recommendations, ensuring that medical expertise complements the automated decision-making process.

Continuously updating and refining the risk assessment models using a machine learning module based on new data and outcomes (206): The final step in the method involves using a machine learning module to continuously improve the accuracy of the risk assessment models. The module learns from new donor data, outcomes from previous blood donations, and emerging research on TTIs. As new information becomes available, the system refines its module, enabling more accurate risk predictions over time. This dynamic updating process ensures that the screening system remains responsive to changing epidemiological trends and improves its capacity to reduce the risk of transfusion-transmissible infections.

Method of Performing the Invention:

Donor Registration and BMI Measurement:
• Upon arrival, the donor registers at the blood donation center, providing basic information such as name, age, gender, geographic location, and contact details.
• In addition to this, the system collects BMI data through the following steps:
• Height and weight measurement to calculate BMI.
• BMI is categorized into underweight, normal weight, overweight, or obese based on WHO standards.
• Other demographic data such as ethnicity, occupation, travel history, and medical history are also collected to form a detailed profile.

BMI and Health Risk Assessment:

• BMI is incorporated into the demographic screening model because it is an indicator of underlying health conditions that affects the immune system or increase susceptibility to infections. For example:
• Underweight donors are malnourished or have chronic health conditions affecting their immunity.
• Obese donors have metabolic conditions like Type 2 diabetes or hypertension, which can weaken the immune system and increase infection risk.
• The system analyses the BMI along with other demographic factors (e.g., age, gender, and geographic location) to assess potential links between BMI categories and the likelihood of carrying TTIs.

Risk Stratification Based on Demographics and BMI:

• Using a risk stratification module, the system evaluates the likelihood of a donor having a TTI based on their demographic profile, including BMI factors;
• Age: Certain age groups may have higher TTI risks.
• Gender: Prevalence of some infections differs between men and women.
• Geographic location: High-risk regions for diseases like HIV or Hepatitis B.
• Lifestyle and behaviour: High-risk activities such as intravenous drug use, multiple sexual partners, and international travel.
• BMI: Different BMI categories correlates with immune suppression or other health complications, raising the risk of infections.
• Donors are then categorized into low-risk, medium-risk, or high-risk groups, based on both demographic factors and BMI.


Medical and Behavioural Screening:

• After demographic and BMI risk stratification, donors undergo additional medical screening to further assess their health status:
• The medical examination includes questions about recent symptoms (e.g., fever, fatigue, infections), any chronic conditions, or ongoing medications.
• Behavioral factors are revisited, such as high-risk sexual behavior, IV drug use, or recent travel to regions with high infection rates.
• The system evaluates the results of this screening in conjunction with the donor's demographic and BMI data.

TTI-Specific Testing:

• Based on the risk categorization (which includes BMI considerations), donors who are in the medium or high-risk groups are flagged for comprehensive TTI-specific testing:
• HIV: Screening for antibodies or antigens to detect infection.
• Hepatitis B and C: Testing for viral markers in the blood.
• Syphilis: Serological tests to detect syphilis antibodies.
• For low-risk donors, the system may streamline testing to routine blood safety measures, minimizing unnecessary use of resources.

BMI-Linked Infection Patterns:
• The system has the ability to monitor and evaluate patterns between BMI categories and the occurrence of certain infections, particularly those that may be linked to immune system function (such as HIV or Hepatitis C). Over time, it accumulates data on:
• How obesity or underweight status correlates with increased risk for certain TTIs.
• Whether higher BMI is linked to chronic conditions that may compromise immunity and increase susceptibility to infection.

Donor Eligibility Decision:

• The system makes a final decision on donor eligibility by integrating the following data:
• Demographic risk assessment: Age, gender, geography, lifestyle.
• BMI risk profile: Whether the donor's BMI contributes to increased TTI risk or affects the immune system's ability to fight infections.
• Medical screening results: Based on physical symptoms, health history, and lifestyle factors.
• TTI test results: Laboratory confirmation of infection status.
• High-risk donors (with positive TTI results or concerning health issues) are deferred from donation. Low-risk or medium-risk donors who test negative for TTIs proceed to blood donation.

Data Storage and Monitoring:

• All donor information including demographic data, BMI, risk stratification results, and TTI test results are securely stored in a centralized database.
• The system continuously monitors donor trends, identifying patterns in TTI prevalence in relation to BMI categories. This enables centers to:
• Track the health and infection status of repeat donors.
• Analyse correlations between BMI and infection risks over time, updating algorithms to improve screening accuracy.
• The database also allows for real-time adjustments based on emerging public health threats, such as new infections or outbreaks linked to specific demographics or BMI categories.

Predictive Analytics and System Evolution:

• The system utilizes machine learning to continuously improve the accuracy of its risk assessments. By analysing incoming donor data, it refines its ability to predict TTI risks based on the following:
• Emerging health patterns: New correlations between BMI, demographics, and infection rates.
• Regional variations: Adjustments for local infection prevalence or changing health conditions in specific geographic areas.
• Epidemiological trends: Adapting to new research on the impact of BMI on immunity and disease susceptibility.
• The system is also updated periodically with data from public health organizations (e.g., WHO, CDC) on global and regional trends in TTI prevalence.

BMI and Infection Risk Insights:

• Incorporating BMI into a demographic-based screening system provides important insights:
• Underweight individuals may have compromised immune systems due to malnutrition or chronic illnesses, making them more susceptible to infections.
• Obese individuals often have conditions such as diabetes or cardiovascular disease, which can reduce immune function and increase their risk of infections, including TTIs.
• By analysing BMI in combination with other demographic factors, the system can:
• Detect patterns of immune suppression related to specific BMI categories.
• Guide additional testing protocols for donors with BMI-related health risks.
• Optimize resource allocation by focusing tests on high-risk BMI profiles.

The method includes:

• Utilizing a combination of web-based and mobile interfaces for data input, allowing flexibility in various healthcare settings.
• Implementing robust data encryption and anonymization protocols to ensure donor privacy and compliance with healthcare data regulations.
• Employing a microservices architecture for the backend systems, allowing for scalability and easy updates to individual components.
• Utilizing containerization technologies like Docker for consistent deployment across different computing environments.
• Implementing a continuous integration/continuous deployment (CI/CD) pipeline for regular updates to the machine learning models and risk assessment modules.
• Providing regular training and support for healthcare professionals to ensure proper use of the system and interpretation of its recommendations.
• Establishing a feedback loop with blood banks and transfusion centers to continuously validate and improve the system's performance.

• By following this method, the Demographic-Based Screening System is effectively implemented to significantly reduce TTI risks in blood donation processes while maintaining high standards of efficiency and data security.
, Claims:1. A demographic-based screening system (100) for reducing transfusion-transmissible infection (TTI) risks in blood donation, comprising:
- a data input module (101) configured to collect and organize demographic information from potential blood donors;
- a demographic analysis engine (102) configured to process the demographic information using statistical models and machine learning modules to identify patterns and correlations between demographic characteristics and TTI risks;
- a risk assessment module (103) configured to calculate a risk score for each potential donor based on the processed demographic information;
- a recommendation generator (104) configured to produce tailored screening recommendations based on the calculated risk scores;
- a demographic data collection (105) module gathers a comprehensive array of information;
- a risk factor identification (106) module for analysing demographic data;
- screening recommendations (107) module for generating personalized guidance; and
- a user interface (109) configured to allow healthcare professionals to input data, view risk assessments, and receive recommendations;
wherein, the system is configured to continuously update and refine its risk assessment models using machine learning modules (108) based on new data and outcomes.

2. A method (200) for demographic-based screening to reduce transfusion-transmissible infection (TTI) risks in blood donation, comprising the steps of:
a) collecting demographic data from potential blood donors using a data input module (201);
b) analysing the collected demographic data using a demographic analysis engine to identify potential risk factors associated with various demographic characteristics (202);
c) calculating a personalized risk score for each donor using a risk assessment module (203);
d) generating tailored screening recommendations using a recommendation generator based on the calculated risk scores (204);
e) presenting the risk assessments and recommendations through a user interface to healthcare professionals (205); and
f) continuously updating and refining the risk assessment models using machine learning module based on new data and outcomes (206).

3. The demographic-based screening system as claimed in claim 1, wherein the data input module (101) is implemented as a mobile application with offline functionality for data collection in remote or resource-limited settings.

4. The demographic-based screening system as claimed in claim 1, wherein the demographic analysis engine (102) includes AI-powered image analysis capabilities for extracting additional demographic information from photographs of potential donors.

5. The demographic-based screening system as claimed in claim 1, wherein the risk assessment module (103) incorporates geospatial analysis to create dynamic risk maps based on donor locations and real-time epidemiological data.

6. The demographic-based screening system as claimed in claim 1, wherein the recommendation generator (104) includes natural language processing capabilities for analysing transcripts or recordings of donor interviews to identify potential risk factors.

7. The demographic-based screening system as claimed in claim 1, wherein the demographic data (105) is stored and managed using a blockchain-based system to ensure data security, integrity, and compliance with data protection regulations.

8. The demographic-based screening system as claimed in claim 1, wherein the risk factor identification (106) capabilities include integration and analysis of multi-omics data, including genomic, proteomic, and metabolomic information.

9. The demographic-based screening system as claimed in claim 1, wherein the screening recommendations (107) include an adaptive questionnaire generator that dynamically creates personalized sets of additional screening questions for each donor.

10. The demographic-based screening system as claimed in claim 1, wherein the machine learning module (108) implement a federated learning approach to allow individual healthcare facilities to train local models on their data while preserving donor privacy.

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