POUR Risk Prediction in Spinal Surgery Using Dual ML Models
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Solution Overview
Problem
Post-operative urinary retention (POUR) after spinal surgery occurs in a significant number of patients, leading to increased hospital stays and costly bladder interventions, with existing treatments being uncomfortable and inefficient.
Innovation Solution
A machine learning approach using a combination of multiple regression and neural network models to predict and treat POUR by analyzing demographic, medical history, and surgical data, employing a neural network with two hidden layers and an output layer to classify patients and determine appropriate treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models (neural network and multiple regression) are used to predict POUR outcomes, then prediction accuracy and patient classification effectiveness are improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent divides the prediction system into two separate machine learning models: a neural network model and a multiple regression model. Each model processes the patient data independently and generates separate probability predictions. This segmentation allows the system to leverage the strengths of both model types while managing computational complexity through modular architecture.
Solution Approach 2:
The patent combines the output probabilities from the neural network model and the multiple regression model to generate a final POUR prediction. By merging the results from both independent models, the system achieves enhanced prediction accuracy through ensemble methodology, where the complementary strengths of different algorithmic approaches are integrated.
2Reliability
If early prediction and targeted treatment of POUR are implemented, then patient outcomes and hospital efficiency are improved, but additional monitoring equipment and intervention resources are required
Solution Approach 1:
The patent implements prediction of POUR outcomes before the actual surgical procedure takes place, using preoperative patient data including demographics, medical history, and surgical parameters. This preliminary prediction allows clinicians to identify high-risk patients in advance and prepare appropriate preventive measures or treatment protocols, improving outcomes without requiring intensive postoperative monitoring resources for all patients.
Solution Approach 2:
The patent applies targeted treatment strategies based on individual patient risk profiles generated by the prediction models. Instead of uniform monitoring and treatment for all patients, the system enables customization of care intensity and resource allocation according to each patient's specific predicted risk level, thereby improving outcomes while optimizing resource utilization.
Data Source
AI summary
Techniques for treating a subject undergoing spinal surgery include obtaining first data that indicates demographic, medical history or surgery information. A first probability for the subject developing post-operative urinary retention (POUR) is generated by inputting the first data into an input layer of a neural network trained with training data that indicates corresponding information for retrospective patients of spinal surgery and POUR outcomes for those patients. A signal is sent, which indicates a POUR classification for the subject based at least in part on the first probability. The subject is then treated based at least in part on the signal. A binomial regression models trained on a subset of training data is used optionally to produce a second probability. Optionally, the signal indicates a classification based on first or second cutoffs for the two probabilities.