Machine Learning Model Identifies Implied Clinical Trial Criteria
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Solution Overview
Problem
Clinical trial eligibility and disqualifying criteria are often not clearly written, leading to ambiguities and manual searches being time-consuming and prone to missing relevant trials, with automated methods struggling to accurately match patients with suitable trials due to difficulties in parsing intent and understanding temporal relationships.
Innovation Solution
A machine learning model is trained using a corpus of clinical trial specifications, including explicitly stated criteria, metadata, and patient data to identify implied criteria, which are then used to augment trial specifications and filter out ineligible trials, reducing false positives in patient recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of clinical trial criteria is performed by doctors and clinical staff, then accuracy in identifying eligible trials is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent introduces natural language processing (NLP) technology as an intermediary between clinical trial criteria and patient records. The NLP system automatically parses and understands the semantic meaning of eligibility and disqualifying criteria, enabling automated matching without manual review while maintaining high accuracy through advanced language understanding capabilities.
Solution Approach 2:
The patent replaces the manual mechanical review process with an automated computational system. The system uses NLP algorithms to parse clinical trial specifications and automatically compare them against patient records, substituting human manual analysis with machine-based automated processing that is both faster and scalable.
2Productivity
If automated methods are used to analyze patient records and clinical trial definitions, then productivity increases, but measurement precision decreases due to inability to parse intent and understand temporal relationships
Solution Approach 1:
The patent replaces traditional automated keyword-matching methods with advanced NLP-based automated analysis. The NLP system can understand the semantic intent of criteria statements and interpret temporal relationships in clinical trial definitions, maintaining high accuracy while providing full automation.
Solution Approach 2:
The patent changes the analytical parameters from simple keyword matching to comprehensive semantic understanding. The NLP system analyzes the meaning, context, and temporal aspects of clinical trial criteria, transforming the matching process from rigid pattern recognition to flexible semantic interpretation that captures nuanced relationships.
3Ease of operation
If institutional procedures prioritize local clinical trials, then ease of operation is improved, but loss of information occurs as potentially relevant external trials are overlooked
Solution Approach 1:
The patent creates a universal automated matching system that can evaluate clinical trials from any institution on equal footing. The NLP-based system objectively assesses all trials against patient eligibility criteria without institutional bias, enabling the system to handle both local and external trials uniformly while identifying the most relevant options regardless of location.
4Device complexity
If explicit criteria alone are used for trial matching, then device complexity is reduced, but measurement precision decreases due to ambiguities in poorly written criteria
Solution Approach 1:
The patent transforms the analysis from surface-level explicit criteria to deep semantic interpretation. The NLP system parses the meaning behind criteria statements, resolving ambiguities by understanding context and intent. This allows the system to handle poorly written or ambiguous criteria accurately without increasing apparent system complexity.
Data Source
AI summary
A method and apparatus for identifying implied criteria for a clinical trial is disclosed. An example method generally includes generating a training data set from a corpus of clinical trial specifications. The training data set may include at least a first sample corresponding to a first trial. The first sample may include a first feature based on one or more explicitly stated trial criteria, a second feature based on metadata describing the first trial, and a third feature based on patient data of patients associated with the first trial. A machine learning model is trained, using a supervised learning approach, based on the training data set. A system processes a second trial as an input to the trained machine learning model to determine one or more implied criteria that are not explicitly enumerated in a specification for the second trial.