Alzheimer Risk Scoring Platform Using ML on Clinical Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic protocols for Alzheimer's disease are costly and limited, often requiring expensive confirmatory tests and relying on neurologist screenings, which can lead to delayed identification of pre-symptomatic individuals at elevated risk for developing Alzheimer's.
Innovation Solution
An Alzheimer's identification platform utilizing machine learning techniques to assign an Alzheimer's risk score based on laboratory test results, prescription data, diagnosis data, age, and gender, allowing for pre-screening of individuals before symptoms appear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic protocols are used, then confirmatory tests can be performed, but the cost increases and pre-symptomatic individuals are not identified early enough
Solution Approach 1:
The system performs preliminary screening using machine learning models to assess Alzheimer's risk before symptoms manifest. By analyzing laboratory test results, prescription data, diagnosis data, age, and gender, the platform identifies pre-symptomatic individuals early, enabling timely intervention before confirmatory tests would be needed.
Solution Approach 2:
The machine learning risk assessment system acts as an intermediary between routine medical data and formal Alzheimer's diagnosis. It processes and interprets various medical data types to generate risk scores, serving as an intermediate step that enables early identification without requiring immediate expensive confirmatory testing.
2Measurement precision
If expensive confirmatory tests are used, then diagnosis accuracy is improved, but health system cost-effectiveness decreases
Solution Approach 1:
The system uses inexpensive, readily available medical data (laboratory results, prescription records, diagnosis data) as input for the machine learning models, replacing the need for expensive confirmatory tests. These disposable-like data sources are already part of routine healthcare records and can be processed without additional significant cost.
Solution Approach 2:
The platform leverages existing medical data that healthcare systems already collect and store, eliminating the need for separate expensive testing infrastructure. The machine learning system processes these self-contained data sources to generate risk assessments, making the system self-sufficient and cost-effective.
3Reliability
If neurologist screenings are relied upon, then professional expertise is utilized, but accessibility and screening volume are limited
Solution Approach 1:
The machine learning model creates a virtual copy of neurologist expertise by learning from and replicating the diagnostic reasoning patterns of experienced professionals. This digital replica can process multiple patients simultaneously without fatigue, maintaining diagnostic reliability while dramatically increasing screening capacity.
Solution Approach 2:
The platform serves multiple functions: it processes routine medical data, generates risk assessments, prioritizes patients for further screening, and provides educational resources. This multi-functionality allows a single system to handle diverse tasks, increasing overall productivity while maintaining reliable screening through standardized algorithms.
Data Source
AI summary
A computer system and computer implemented method for determining a risk level of a candidate patient for developing Alzheimer's disease are provided. Laboratory test results from a laboratory are received at a computing device having one or more processors. The laboratory test results correspond to the candidate patient. Prescription date indicative of medications taken by the candidate patient are received at the computing device. Diagnosis data indicative of medical diagnoses associated with the candidate patient are received at the computing device. Age and gender associated with the candidate patient are received at the computing device. Features of the laboratory test results are preprocessed into categories. An Alzheimer's risk score associated with the candidate patient is generated by the computing device utilizing at least one machine learning model based on the prescription data, diagnosis data, age, gender and categorized features. The Alzheimer's risk score is output by the computing device.