Decision Tree Classification for Mild Cognitive Impairment Diagnosis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for diagnosing and treating Alzheimer's disease face challenges in early detection, accurate diagnosis, and selecting appropriate patient cohorts for clinical trials, often resulting in ineffective treatment demonstrations due to incorrect diagnoses and overfitting issues.
Innovation Solution
A system utilizing a computer to receive and process normalized learning data, tune decision trees, and classify patient data to determine patient threshold values, allowing for the selection of a cohort group or patient at risk of converting to Alzheimer's disease, thereby improving diagnosis and treatment options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional neuropsychological tests are used for diagnosis, then clinicians can assess cognitive abilities, but the large number of tests creates data clutter that makes diagnosis difficult
Solution Approach 1:
The patent segments the large set of neuropsychological test results into meaningful groups or dimensions (e.g., memory, attention, language, executive function). By organizing tests into categories and analyzing patterns within each segment, the system reduces data clutter while preserving diagnostic information. This segmentation allows clinicians to focus on relevant cognitive domains rather than being overwhelmed by individual test scores.
Solution Approach 2:
The patent merges multiple neuropsychological test results into a unified diagnostic profile or summary metric. By combining information from numerous tests into a consolidated view that highlights key diagnostic features, the system reduces the apparent complexity while maintaining the diagnostic value of individual tests. This merging process creates a coherent diagnostic picture from scattered test data.
2Measurement precision
If machine learning methods are used for patient classification, then diagnosis accuracy can be improved, but overfitting causes incorrect diagnosis and poor sensitivity/specificity
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model's predictions are continuously evaluated against actual patient outcomes. By incorporating performance feedback (such as sensitivity and specificity metrics) into the model training process, the system adjusts parameters to avoid overfitting. This feedback loop ensures that the model generalizes well to new patients while maintaining high diagnostic accuracy on known cases.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting machine learning model parameters based on the specific patient population and data characteristics. Rather than using fixed parameters, the system adapts parameters such as classification thresholds, feature weights, and model complexity to match the underlying data distribution. This flexibility prevents overfitting while maintaining high diagnostic precision across different patient cohorts.
3Productivity
If a large cohort is recruited for clinical trials, then statistical power increases, but including patients unlikely to benefit reduces ability to prove treatment efficacy
Solution Approach 1:
The patent applies preliminary action by using the machine learning classification system to pre-screen and identify patients most likely to benefit from the treatment before they are recruited into the clinical trial. By performing this preliminary selection based on prognostic markers and patient characteristics, the trial cohort is enriched with responsive patients, increasing the likelihood of demonstrating treatment efficacy while maintaining adequate statistical power.
Data Source
AI summary
Methods and systems for selecting a cohort group or a patient at risk from a population of patients with mild cognitive impairment. The methods include using a computer configured to perform the steps: receiving normalized learning data from a portion of the population of patients; tuning a set of decision trees on the normalized learning data; receiving patient data from one or more patients of the population, wherein the patient data is independent from the learning data; classifying the patient data with the tuned set of decision trees to obtain patient threshold values; and displaying the patient threshold values.