Tensor Encoding for ADAS-Cog Prediction Accuracy
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Solution Overview
Problem
Current methods for predicting cognitive function in Alzheimer's disease, particularly using the ADAS-Cog scale, face challenges in accurately predicting individual patient outcomes due to heterogeneous health trajectories and limited data efficiency in clinical trials.
Innovation Solution
A method involving machine learning processes to generate a synthetic model of a target patient's or cohort's ADAS-Cog score trajectory by encoding data into tensors across patients, time, and treatment plans, allowing for prediction under unseen treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical methods are used to predict ADAS-Cog scores, then the prediction process is simple and interpretable, but the prediction accuracy is insufficient due to heterogeneous health trajectories and limited data efficiency
Solution Approach 1:
The patent transforms the prediction approach by changing the parameter representation from traditional statistical parameters to tensor decompositions. The ADAS-Cog scores are represented as tensors that capture multi-dimensional relationships across patients, time points, and treatment plans, enabling more accurate predictions through low-rank approximations that preserve heterogeneous trajectories while improving data efficiency
Solution Approach 2:
The patent creates a composite modeling framework that integrates multiple data sources and dimensions into a unified tensor structure. This composite approach combines information from different patients, time points, and treatment plans into a single mathematical object that can be efficiently decomposed and used for counterfactual predictions
2Measurement precision
If clinical trials include more patients and treatment plans to improve prediction accuracy, then the data coverage increases, but the trial cost and time requirements increase
Solution Approach 1:
The patent performs preliminary tensor decomposition on the observed data to extract latent patterns and relationships before making predictions. By pre-processing the data to identify low-rank structures and canonical patient profiles, the system can efficiently generate counterfactual predictions without requiring additional clinical trial data collection
Solution Approach 2:
The patent creates synthetic counterfactual data by copying and transforming observed patient trajectories. Using the decomposed tensor structure, the system generates predicted ADAS-Cog scores for untreated or alternative treatment scenarios by leveraging patterns from observed patients, effectively creating virtual copies of clinical outcomes without enrolling additional patients
3Measurement precision
If individual patient data is used for prediction, then the personalization accuracy improves, but the data sparsity and overfitting risk increase
Solution Approach 1:
The patent merges individual patient data with population-level patterns through tensor decomposition. By representing the data as a three-way tensor across patients, time, and treatment plans, the method combines individual trajectory information with aggregate patterns from all patients, enabling personalized predictions that are regularized by population-level constraints and reduce overfitting
Solution Approach 2:
The patent creates a universal tensor decomposition model that serves multiple functions simultaneously. The same decomposed tensor structure is used for both describing individual patient trajectories and making counterfactual predictions across different patients and treatment plans, enabling the system to generalize from limited individual data through shared latent structures
4Reliability
If dropout patients are excluded from analysis, then the bias from incomplete data is reduced, but the sample size and statistical power decrease
Solution Approach 1:
The patent uses tensor decomposition as an intermediary to handle dropout data. Instead of directly excluding or imputing dropout patients, the method decomposes the complete tensor structure to infer missing values based on latent patterns from observed data, thereby reducing bias while preserving statistical power through the mediating mathematical transformation
Data Source
AI summary
A method for predicting a patient or patient cohort's cognition score on the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog). The method comprises obtaining data relating to a plurality of patients or patient cohorts, the data including information relating to the longitudinal trajectories of the plurality of patients', or patient cohorts', ADAS-Cog score over time, each patient or patient cohort having undergone a treatment plan selected from a plurality of treatment plans; encoding the data into a tensor across patients or patient cohorts, time and treatment plan; generating a synthetic model of a target patient or target patient cohort using a machine learning process and the tensor; and predicting an ADAS-Cog score of the target patient or target patient cohort under a target treatment plan selected from the plurality of treatment plans, using the synthetic model.