Trajectory Phenotype Clustering for Personalized Treatment Planning
Solution Overview
Problem
Current healthcare approaches often rely on a 'wait and see' method for treating patients, leading to extended treatment periods and increased risk of adverse outcomes due to the lack of accurate prediction of a patient's future longitudinal trajectory for various disorders.
Innovation Solution
A method that compiles treatment records and applies clustering algorithms and machine learning techniques to identify trajectory phenotypes, enabling the prediction of a patient's future disorder state and development of personalized treatment plans based on classification into specific subgroups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a wait and see approach is used for medical care, then treatment decisions are made based on observed changes, but treatment periods are extended and healthcare resources are wasted
Solution Approach 1:
The system performs preliminary classification of patients into trajectory phenotypes using clustering algorithms and machine learning models before treatment begins. This allows treatment plans to be proactively tailored to predicted longitudinal trajectories, eliminating the need to wait for observed changes and reducing treatment periods while improving decision accuracy.
2Productivity
If personalized treatment plans are developed without trajectory prediction, then treatment is based on general approaches, but treatment effectiveness is reduced and adverse outcomes increase
Solution Approach 1:
The system changes the parameter of treatment personalization by incorporating trajectory phenotype classification into treatment planning. By using clustering algorithms to identify distinct trajectory patterns and applying machine learning to predict future states, the system transforms generic treatment approaches into personalized plans based on predicted longitudinal trajectories, thereby improving treatment effectiveness and reliability.
3Measurement precision
If clustering algorithms and machine learning techniques are applied to identify trajectory phenotypes, then accurate prediction of future disorder states is achieved, but system complexity increases
Solution Approach 1:
The system segments the patient population into distinct trajectory phenotypes using clustering algorithms applied to longitudinal data. This segmentation divides the complex problem of predicting individual patient trajectories into manageable groups with similar patterns, achieving high prediction accuracy while maintaining system tractability through categorical classification.
Data Source
AI summary
A method for treating a patient having a specified disorder includes the steps of: (a) compiling a treatment records database that includes patient longitudinal trajectories, (b) applying a plurality of clustering algorithms to identify a plurality of especially shaped, trajectory phenotypes for the disorder, (c) using a plurality of model selection techniques to determine the number and shape of the trajectory phenotypes, (d) compiling an intake database for said prior patients that includes background-characterizing factors for each of the patients, (d) using on the intake database analysis techniques to identify a means for classifying that predicts for a to-be-treated patient which of the trajectory phenotypes that is most similar to the real-time longitudinal trajectory that the to-be-treated patient will exhibit, and (e) identifying for the to-be-treated patient a treatment plan based on the to-be-treated patient's predicted trajectory phenotype.