Preoperative Panel ML Models for Faster Comprehensive Assessment
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Solution Overview
Problem
Traditional preoperative evaluations are time-consuming and inefficient, necessitating a more effective and efficient approach for comprehensive preoperative assessments.
Innovation Solution
An apparatus and method utilizing a pre-operative panel system that includes a processor and memory to receive ECG data, generate panel outputs using machine-learning models, and create a pre-operative data structure through training and generating panel outputs based on correlations between subject data, panel focuses, and panel outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional individual tests are used for preoperative evaluation, then comprehensive assessment can be achieved, but the process becomes time-consuming and inefficient
Solution Approach 1:
The preoperative evaluation is segmented into multiple specialized machine-learning models, each focusing on a specific aspect (e.g., cardiac risk, pulmonary function, renal function). This segmentation allows parallel processing of different assessment dimensions, reducing overall evaluation time while maintaining comprehensive coverage through the collective output of all models.
Solution Approach 2:
The system performs preliminary actions by pre-training multiple specialized machine-learning models on extensive datasets before the actual evaluation. During the evaluation process, these pre-trained models can immediately process patient data in parallel, eliminating the need for sequential analysis and significantly reducing the time required for comprehensive preoperative assessment.
2Reliability
If multiple individual tests are conducted separately, then thorough evaluation is achieved, but the complexity and inefficiency of the process increases
Solution Approach 1:
Multiple specialized machine-learning models are merged into a unified preoperative panel system that processes patient data through all models simultaneously. The system combines the outputs of these models into a single comprehensive preoperative data structure, achieving thorough evaluation while managing complexity through integrated architecture and standardized data interfaces.
Solution Approach 2:
The preoperative panel system is designed as a universal platform that can handle multiple types of evaluations (cardiac, pulmonary, renal, etc.) through a single integrated system. Each machine-learning model within the panel is multi-functional, capable of processing various input data types and contributing to the overall preoperative assessment, thereby reducing the need for separate specialized systems.
Data Source
AI summary
An apparatus and method for generating a preoperative data structure using a pre-operative panel are disclosed. The apparatus includes a memory containing instructions configuring at least a processor to receive subject data including ECG data, generate a plurality of panel outputs as a function of the subject data using a pre-operative panel machine-learning module including a plurality of panel machine-learning models, wherein each of the plurality of panel machine-learning models is configured to generate one panel output for one panel focus, wherein generating the plurality of panel outputs includes generating a plurality of sets of panel training data, training each of the plurality of panel machine-learning models using each of the plurality of sets of panel training data and generating the plurality of panel outputs using the plurality of trained panel machine-learning models and generate a pre-operative data structure as a function of the plurality of panel outputs.