Patient Cohort Prediction Interface for Clinical Data Analysis
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Solution Overview
Problem
There is a lack of effective methods to quickly and comprehensively analyze vast amounts of medical data, including clinical, molecular, and demographic information, to predict patient responses and survival rates effectively.
Innovation Solution
A system and user interface are developed to predict patient population responses to treatments by identifying key inflection points in data distributions, using a pre-existing dataset to define patient cohorts, and employing automated analysis of clinical, molecular, and phenotypic data through a reactive user interface, enabling the identification of outlier groups and prediction modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If vast amounts of medical data are collected and stored for each patient, then the completeness and comprehensiveness of patient information is improved, but the complexity and difficulty of analyzing this data quickly and efficiently worsens
Solution Approach 1:
The patent segments the vast medical data into distinct cohorts based on shared characteristics (demographic, clinical, molecular, genomic). By dividing the homogeneous data mass into heterogeneous cohorts, the system makes the data more manageable and analyzable while preserving all original information. Each cohort represents a segmented portion of the patient population with specific attributes, enabling focused analysis without losing the broader context.
Solution Approach 2:
The system changes the parameters of data analysis by transforming raw medical data into cohort-based groupings with defined characteristics. By altering how data is organized and queried (from individual patient records to cohort-level aggregates), the system enables efficient analysis while maintaining access to underlying detailed information. This parameter transformation allows complex data to be processed through standardized cohort definitions and queries.
2Measurement precision
If comprehensive patient data is analyzed to predict treatment responses and survival rates, then the precision of predictions is improved, but the time required for analysis worsens
Solution Approach 1:
The patent performs preliminary actions by pre-defining cohorts with specific characteristics and pre-calculating their responses to treatments. Instead of analyzing individual patient data from scratch each time a prediction is needed, the system has already organized patients into cohorts and computed their aggregate responses in advance. This preliminary organization and computation dramatically reduces the time required for new predictions while maintaining precision by leveraging the comprehensive cohort data.
3Loss of information
If detailed cohort analysis is performed to identify outlier groups, then the ability to discover therapeutic insights is improved, but the computational resources required worsens
Solution Approach 1:
The system segments the patient population into cohorts and further identifies outlier cohorts within those segments. By dividing the analysis into hierarchical levels (overall cohorts, then outlier detection within cohorts), the system reduces computational complexity compared to analyzing all patients individually for outliers. Each segmentation level processes a smaller, more focused subset of data, reducing overall computational resource requirements while preserving therapeutic insights.
Solution Approach 2:
The system changes analytical parameters by shifting from individual patient-level outlier detection to cohort-level outlier detection. This parameter change in the scale of analysis (from individual to group level) significantly reduces computational resources required, as cohort-level analysis processes aggregated data rather than individual records. The approach maintains therapeutic insight discovery by identifying cohorts with unusually high or low response rates, which are clinically significant patterns.
Data Source
AI summary
A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.