Dynamic Machine Learning Model for Surgical Complication Prediction
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Solution Overview
Problem
Current methods for predicting postoperative complications in surgery are inadequate, relying on standardized protocols and registry data that fail to provide personalized and timely warnings, leading to increased healthcare costs and resource burdens.
Innovation Solution
A method for predicting complications using a dynamically trained machine learning model that receives and updates patient data in real-time, allowing for localized training and adaptation to specific hospital settings, enabling personalized risk assessments and proactive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized protocols and registry data are used for predicting postoperative complications, then the prediction method is simple and standardized, but the prediction accuracy and personalization are insufficient
Solution Approach 1:
The patent implements dynamic retraining of the machine learning model using newly acquired patient data after surgery. The model transitions from a static trained state to a dynamically updating local model that continuously learns from new outcomes, thereby improving prediction accuracy while managing complexity through automated incremental learning processes
Solution Approach 2:
The system changes the state of the machine learning model from a fixed trained model to a locally adapted model by updating model parameters using local patient data. This parameter adaptation allows the system to achieve higher prediction accuracy for specific hospital populations while maintaining a manageable complexity through targeted local training rather than complete model redesign
2Adaptability or versatility
If a generally trained machine learning model is used, then the model can be applied broadly across different hospitals, but the model does not adapt to local patient populations and settings
Solution Approach 1:
The system performs preliminary training of a general machine learning model on broad registry data before deployment. This pre-trained model serves as a foundation that can be quickly adapted to local settings through subsequent local training on hospital-specific data, reducing the overall training time compared to training from scratch while achieving local adaptability
Solution Approach 2:
The patent implements a nested training structure where a locally trained model is embedded within a generally trained model framework. The local model inherits the general knowledge from the broadly trained model and adds hospital-specific adaptations, creating a hierarchical structure that achieves both broad applicability and local customization efficiently
3Reliability
If real-time tracking of complication incidence is implemented, then timely warnings can be provided, but the system complexity and data processing requirements increase
Solution Approach 1:
The system implements continuous feedback loops where patient outcomes are tracked in real-time and fed back to the machine learning model. This feedback mechanism enables timely warnings by continuously monitoring new data and updating risk predictions, while managing complexity through automated feedback processing and incremental model updates rather than complex manual analysis systems
Solution Approach 2:
The patent maintains continuous data collection and model updating processes that operate throughout the patient care continuum. The machine learning model continuously learns from new patient data and provides ongoing risk assessments, ensuring timely warnings without requiring intermittent complex retraining cycles or manual intervention
Data Source
AI summary
Disclosed are systems and methods for continuously detecting, and optionally classifying, abnormal perfusion patterns in tissue by means of fluorescence imaging. One embodiment relates to a computer implemented method for detecting (and/or identifying) one or more areas having an abnormal perfusion pattern in tissue of a subject, for example during a medical procedure, the method including: continuously acquiring fluorescence images of the tissue, wherein the fluorescence images are associated with a fluorescent output signal correlated with an input signal defined by a series of boluses of at least one fluorescent imaging agent, and wherein the series of boluses is administered with a predefined and/or controlled duration between subsequent boluses, analyzing the fluorescence images, identifying at least one tissue area with normal perfusion, defining a normal perfusion pattern (in an intensity domain and) in a time domain, and detecting, in the fluorescence images, possible tissue areas with abnormal (non-normal) perfusion pattern.