Interpretable Temporal Disease Risk Profiles
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Solution Overview
Problem
Current technologies lack the ability to provide interpretable and contextual temporal disease risk data, making it difficult to understand and analyze an individual's health progression over time in relation to disease risk.
Innovation Solution
A computer-implemented method using a risk scoring machine learning model generates a temporal disease risk profile, comprising risk score nodes with timepoints, and provides node-specific weight distributions to illustrate the contribution of health indicators to disease risk, allowing for dynamic interpretation and display via user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional disease risk assessment methods are used, then computational resources are conserved, but the ability to provide interpretable temporal disease risk data is lost
Solution Approach 1:
The patent segments the disease risk assessment into multiple risk score nodes, each representing a specific timepoint with associated health indicators. This segmentation allows the system to provide detailed, interpretable temporal risk data while managing complexity through modular organization of risk factors and timepoints.
Solution Approach 2:
The patent adds a temporal dimension to disease risk assessment by generating risk scores across multiple timepoints rather than a single static assessment. This transforms the risk data from a one-dimensional snapshot to a multi-dimensional temporal profile, enabling interpretation of risk progression over time.
2Loss of information
If detailed node-specific weight distributions are provided, then interpretability of health indicator contributions is improved, but information processing complexity increases
Solution Approach 1:
The patent applies local quality by providing node-specific weight distributions that are tailored to each individual risk score node. Each node receives customized weightings for health indicators based on their specific contribution to the risk at that timepoint, rather than applying uniform weights across all nodes. This enables precise interpretation of which health factors drive risk at each stage.
3Loss of information
If temporal disease risk profiles are generated for multiple timepoints, then understanding of health progression over time is improved, but computational processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-processing and organizing health indicator data into structured node record data objects before generating risk scores. This preliminary organization of data by timepoints and health indicators enables efficient computation of temporal risk profiles, reducing the processing time required for multi-timepoint assessments.
Data Source
AI summary
Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, providing a temporal disease risk profile describing a likelihood of disease onset over time for an individual in a dynamically interpretable manner. Interpretability of the temporal disease risk profile is enabled by providing additional and contextual information, such as weight distributions of various health indicators, factors, and features. In an embodiment, an example method comprises generating a temporal disease risk profile comprising risk score nodes based at least in part on providing a plurality of record data objects to a risk scoring machine learning model configured to generate a risk score; providing the temporal disease risk profile for display via a first user interface comprising a plurality of interactable node mechanisms each corresponding to a risk score node; and providing a node-specific weight distribution comprising one or more sub-nodal weight values for display via a second user interface.