Adaptive Diagnostic Instrument for Faster Behavioral Classification
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Solution Overview
Problem
Traditional methods for diagnosing behavioral disorders, developmental delays, and neurological impairments are inefficient and inaccurate due to the relatedness of condition types, leading to overlapping symptoms and incorrect diagnoses, and often require lengthy questionnaires that are time-consuming and resource-intensive.
Innovation Solution
A computer-implemented method using a diagnostic instrument that receives input, generates a model of likelihood for multiple conditions, identifies the next input to reduce uncertainty, and provides an efficient classification based on a machine learning model and Monte Carlo methods, reducing the number of questions needed for accurate diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional observational techniques and questionnaires are used to evaluate behavioral disorders, developmental delays, and neurological impairments, then comprehensive data collection is achieved, but the evaluation process becomes time-consuming and resource-intensive
Solution Approach 1:
The diagnostic instrument dynamically adapts the questionnaire based on previous responses, selecting and presenting subsequent questions in real-time based on the individual's answers. This dynamic adaptation allows the system to focus on relevant symptoms and differentiate between related conditions more efficiently, reducing unnecessary questions while maintaining diagnostic accuracy.
Solution Approach 2:
The system changes parameters of the evaluation process by using machine learning models to determine which questions to ask next based on the current state of diagnostic uncertainty. The questionnaire structure transforms from a fixed sequence to a flexible, adaptive sequence that optimizes information gathering efficiency.
2Reliability
If multiple interviews are conducted to ensure accurate diagnosis, then diagnostic thoroughness is improved, but cost and resource consumption increase
Solution Approach 1:
The diagnostic instrument incorporates feedback loops where each response influences subsequent question selection. The machine learning model continuously updates the diagnostic probability distribution based on incoming responses, allowing the system to adaptively refine the evaluation path and reduce the number of interviews needed while maintaining reliability.
Solution Approach 2:
The patent replaces the mechanical system of multiple sequential interviews with a computational system that uses machine learning algorithms to process responses and guide the evaluation. This substitution automates the diagnostic reasoning process, reducing the need for repeated human interviews while maintaining or improving diagnostic reliability.
3Measurement precision
If comprehensive questionnaires are administered to differentiate between related conditions, then diagnostic coverage is improved, but the number of questions and administrative burden increase
Solution Approach 1:
The comprehensive questionnaire is segmented into adaptive modules where only relevant sections are administered based on initial responses. The machine learning model identifies which symptom clusters are most relevant for differentiating between specific conditions, allowing the system to segment the evaluation into focused subsets rather than administering all questions uniformly.
Solution Approach 2:
The system performs partial action by administering only the necessary subset of questions required to achieve sufficient diagnostic confidence. Rather than completing the full comprehensive questionnaire, the adaptive instrument stops when the machine learning model determines that diagnostic uncertainty has been reduced to an acceptable level, avoiding excessive questioning.
4Loss of information
If traditional techniques are used to evaluate overlapping symptoms, then all symptoms are captured, but correct differentiation between conditions becomes difficult
Solution Approach 1:
The system performs preliminary action by using machine learning models to pre-analyze response patterns and identify which symptoms are most discriminative for differentiating between related conditions. The questionnaire is guided by pre-computed insights about which symptom combinations are most useful for condition differentiation, allowing the system to capture relevant information more efficiently.
Data Source
AI summary
Described herein is software used to evaluate individuals such as children for behavioral disorders, developmental delays, and neurological impairments. Specifically, described herein is software configured for use along with methods, devices, systems, and platforms that are used to analyze to aid in the positive or negative diagnosis of individuals for one or more behavioral disorders, developmental delays, and neurological impairments.