Machine Learning Entity Ranking for Clinical Trial Site Selection
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Solution Overview
Problem
Current solutions for evaluating entities for clinical trials are manual, dependent on user-defined weightings, and biased, prioritizing investigators and site locations based on limited discrete variables, which can lead to inefficient entity selection and biased rankings.
Innovation Solution
A computer-implemented method using machine learning and predictive analytics to analyze diverse data inputs, generating tiered rankings of entities based on their predicted ability to perform clinical trial activities and comply with protocols, incorporating performance, participation, and quality attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation methods are used with user-defined weightings, then the process is simple to implement, but the rankings become biased and based on limited variables
Solution Approach 1:
The patent replaces manual evaluation mechanisms with automated machine learning models that process multiple data sources and variables. The system uses computational algorithms to generate entity scores and rankings, substituting human judgment with data-driven automated assessment that eliminates manual bias while handling complex multi-factor evaluation.
Solution Approach 2:
The system dynamically adjusts evaluation parameters and weightings based on learned patterns from historical data rather than using fixed user-defined values. The machine learning models automatically determine the relative importance of different variables (entity performance, site characteristics, protocol compliance) and adapt these parameters to specific trial contexts, improving ranking precision without requiring manual parameter tuning.
2Productivity
If automated machine learning systems are implemented, then ranking accuracy and efficiency improve, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction before executing the full machine learning evaluation. By pre-processing data sources (entity profiles, site characteristics, historical performance) and preparing standardized inputs, the system reduces the computational burden during actual ranking operations, improving efficiency while managing resource consumption.
Solution Approach 2:
The system implements tiered evaluation where not all entities receive full computational analysis. High-priority entities or those meeting initial criteria undergo detailed machine learning assessment, while others receive streamlined evaluation. This partial application of full computational power maintains productivity for critical cases while reducing overall resource consumption.
Data Source
AI summary
A computer-implemented method includes a machine learning system receiving distinct types of data associated with multiple individual entities. For each of the individual entities, the machine learning system determines a first attribute that indicates a predicted attribute of the entity based on analysis of the data. The machine learning system also determines a second attribute that indicates a predicted quality attribute of the entity, based on analysis of the data. An attribute weighting module of the machine learning system generates weight values for each of the first attribute and the second attribute of the entity. The machine learning system generates a data structure that identifies a set of entities from among the multiple individual entities, where entities of the set are ranked based on a tier indicator that corresponds to either the first attribute, the second attribute, or both.