Precision Cohort Treatment Selection via Patient Similarity
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Solution Overview
Problem
Existing treatment guidelines are generic and do not provide sufficient specificity for determining the most effective treatment option for a particular patient, as they only indicate eligibility rather than predicting treatment efficacy.
Innovation Solution
A method that extracts attributes from electronic health records, generates a training dataset, and trains a patient similarity model to identify a precision cohort of patients similar to the index patient, determining result statistics for various treatments within this cohort to select the most effective treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic treatment guidelines based on randomized clinical trials are used, then broad applicability and ease of implementation are improved, but treatment efficacy prediction for individual patients deteriorates
Solution Approach 1:
The patent segments the patient population into precision cohorts based on cognitive similarity rather than treating all patients uniformly. By dividing patients into subgroups with similar cognitive profiles and treatment responses, the system enables personalized treatment predictions while maintaining systematic organization. This resolves the contradiction by allowing generic guidelines to operate at the cohort level while providing individualized predictions within each cohort.
Solution Approach 2:
The patent changes the parameters used for patient classification from traditional demographic and clinical parameters to cognitive parameters (cognitive similarity scores, cognitive profiles). This parameter transformation enables more precise treatment efficacy predictions by capturing subtle differences in patient cognition that affect treatment response, while still allowing for systematic analysis and implementation.
2Device complexity
If treatment guidelines indicate only patient eligibility rather than treatment efficacy, then simplicity and clarity are improved, but personalization and treatment selection capability deteriorate
Solution Approach 1:
The patent introduces precision cohorts as an intermediary layer between generic treatment guidelines and individual patient treatment decisions. These cohorts act as a mediator that translates broad eligibility criteria into personalized treatment recommendations by grouping patients with similar cognitive characteristics. This resolves the contradiction by maintaining the simplicity of generic guidelines while adding the adaptability needed for personalization through the cohort intermediary.
Solution Approach 2:
The patent adds a new dimension to treatment guidelines by incorporating cognitive similarity metrics beyond traditional eligibility criteria. Instead of solely relying on demographic and clinical parameters, the system introduces cognitive profiling as an additional dimension for patient classification. This enables personalized treatment selection while maintaining the structured framework of traditional guidelines.
3Adaptability or versatility
If multiple treatments satisfy all guidelines for a patient, then treatment option availability is improved, but treatment selection difficulty deteriorates
Solution Approach 1:
The patent applies local quality by providing different levels of treatment recommendation precision for different patient cohorts. Instead of applying a single selection method to all patients, the system tailors the treatment selection approach to each precision cohort's characteristics. This resolves the contradiction by making treatment selection manageable through cohort-specific strategies while maintaining multiple treatment options for each cohort.
Solution Approach 2:
The patent implements feedback mechanisms where treatment outcomes from precision cohort members are used to refine future treatment selections. By analyzing which treatments work best within each cognitive similarity cohort, the system provides feedback that guides treatment selection for new patients in the same cohort. This reduces selection complexity by leveraging accumulated knowledge from similar patients while preserving treatment option availability.
Data Source
AI summary
A plurality of attributes are extracted from a plurality of electronic health records, where each electronic health record is associated with a patient in a plurality of patients. Additionally, a training data set and a scoring data set are generated based on the plurality of attributes, and a patient similarity model is trained based on the training data set. A precision cohort is identified, where the precision cohort includes patients in the plurality of patients from the scoring data set that are similar to a first patient based on an electronic health record of the first patient and the similarity model. At least one result statistic for each of a plurality of treatments given to patients in the precision cohort is determined, and a first treatment of the plurality of treatments is selected for the first patient based at least in part on the determined result statistics.