Dynamic Treatment Regime Model With Prototype Layer
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Solution Overview
Problem
Machine learning systems for dynamic treatment regimes in healthcare struggle with interpretability and flexibility, as they often rely on single policies that fail to adapt to changing patient needs.
Innovation Solution
A method for training a healthcare treatment machine learning model by segmenting patient trajectories, using a prototype layer to learn representative vectors and an imitation learning layer to select treatment actions based on input states and skill embeddings, allowing for interpretable and transferable skills across different trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single policy is used to handle patient treatment decisions, then the model structure is simple, but the model cannot adapt to changing patient needs and lacks flexibility
Solution Approach 1:
The patent segments the patient trajectory into multiple time steps or stages, allowing different policies to be applied at different segments. This segmentation enables the model to adapt to changing patient needs at different stages of treatment while maintaining manageable complexity through modular policy structures.
Solution Approach 2:
The patent implements dynamic treatment regimes where the policy can change over time based on patient response. The model transitions from static single-policy approaches to dynamic multi-policy frameworks that adapt to evolving patient conditions, improving flexibility without requiring complete redesign of the entire model architecture.
2Measurement precision
If machine learning systems use historical records to build treatment policies, then treatment recommendations can be data-driven, but the underlying rationales become uninterpretable
Solution Approach 1:
The patent introduces intermediate representations such as latent variables, attention maps, or explanation layers that mediate between the raw historical data and the final treatment recommendations. These intermediaries preserve interpretability by providing explicit rationales for policy decisions while still leveraging the accuracy benefits of data-driven learning from historical records.
Solution Approach 2:
The patent implements feedback mechanisms that allow the model to explain its reasoning by tracing back through the decision process. By providing explanations as feedback to users and clinicians, the system maintains interpretability while continuing to learn from historical data, creating a feedback loop that improves both accuracy and transparency.
Data Source
AI summary
Methods and systems for training a healthcare treatment machine learning model include segmenting a patient trajectory, which includes a sequence of patient states and treatment actions. A machine learning model is trained based on segments of the patient trajectory, including a prototype layer that learns prototype vectors representing respective classes of trajectory segments and an imitation learning layer that learns a policy to select a treatment action based on an input state and a skill embedding.