Machine Learning Infection Risk Assessment for Building Materials
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Solution Overview
Problem
Hospital-onset infections pose a significant risk due to poor quality control in preventable infections within healthcare environments, exacerbated by antimicrobial resistance and sub-optimal environmental cleaning practices, with a lack of effective resources and standards for infection prevention in building infrastructure materials.
Innovation Solution
A system and method for building infrastructure and infection prevention data analysis using machine learning to assess and predict the risk of infection based on infrastructure materials, involving the creation of an electronic repository of performance metrics, assignment of performance scores, and integration with trusted database sources, including peer-reviewed journals and Material Safety and Data Sheets, to guide design for infection prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional building materials and infrastructure designs are used in healthcare environments, then construction costs and material availability are improved, but infection prevention capability deteriorates
Solution Approach 1:
The system performs preliminary assessment of building materials during the design phase rather than after construction. The machine learning model evaluates material properties, antimicrobial effectiveness, and infection risk before materials are selected and installed, allowing prevention strategies to be built into the infrastructure from the outset rather than requiring complex retrofits later
Solution Approach 2:
The patent introduces an intermediary machine learning assessment system that bridges the gap between traditional material selection and infection prevention outcomes. This intermediary layer analyzes material properties, cleansability, and antimicrobial characteristics to provide risk scores that guide material selection, translating complex material science data into actionable design decisions
2Measurement precision
If comprehensive performance metrics are collected and analyzed for all building materials, then infection prevention accuracy is improved, but data processing time and computational resources worsen
Solution Approach 1:
Performance metrics for building materials are collected and stored in a database during the design phase before actual material selection occurs. The system pre-processes and organizes data on material properties, antimicrobial testing results, and cleansability characteristics in advance, so that when design decisions need to be made, the information is already prepared and readily available for rapid assessment
Solution Approach 2:
The machine learning model uses trained models and pre-computed performance metrics stored in a database rather than analyzing raw material data from scratch each time a material needs to be evaluated. The system creates simplified representations of material performance based on historical data and testing results, allowing rapid assessment without re-processing all underlying data
3Productivity
If machine learning models are trained on extensive material performance data, then prediction accuracy for infection risk is improved, but system development complexity and data requirements worsen
Solution Approach 1:
The machine learning assessment system is designed to evaluate multiple types of building materials (surfaces, coatings, fixtures, finishes) using a unified framework and common performance metrics. The same machine learning model can assess diverse materials by evaluating their antimicrobial properties, cleansability, and structural characteristics through consistent criteria, reducing the need for separate specialized systems for each material type
Data Source
AI summary
The present disclosure presents systems and methods for building infrastructure and infection prevention data analysis. One such method comprises building contents of an electronic repository of performance metrics related to material pathogen propagation or reduction; assigning, via machine learning, a performance score or grade to a certain infrastructure building material based on the performance metrics associated with the building material; and predicting and outputting, via the machine learning, a risk associated with a building based on its infrastructure building materials, wherein the infrastructure building materials comprises the certain infrastructure building material.