Perioperative Risk Algorithm for Postoperative Complication Prediction
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Solution Overview
Problem
Current methods lack effective identification and management strategies for patients at high risk of developing postoperative complications, leading to increased morbidity, mortality, and healthcare costs following surgical procedures.
Innovation Solution
An automated analytics framework implementing a perioperative risk algorithm that utilizes electronic health records to calculate patient-level probabilistic risk scores for eight major postoperative complications, incorporating data transformation, feature selection, and machine learning models to predict mortality risks and generate personalized risk panels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated analytics framework with machine learning models is implemented to predict postoperative complications, then prediction accuracy and patient risk identification capability is improved, but system complexity and computational resources required increase
Solution Approach 1:
The system segments the prediction task into multiple independent machine learning models, each targeting a specific postoperative complication type. This modular approach allows each model to specialize in predicting particular complications while maintaining overall system manageability and accuracy.
Solution Approach 2:
The patent introduces an automated analytics framework as an intermediary layer between electronic health record data and clinical decision-making. This framework processes raw data through standardized pipelines including data cleaning, feature extraction, and model inference, reducing the complexity burden on end users.
2Reliability
If comprehensive electronic health record data is processed through data transformation and feature selection to generate personalized risk panels, then patient-level risk prediction capability is improved, but data processing time and computational load increase
Solution Approach 1:
The system performs preliminary data transformation and feature selection during the model training phase, preparing standardized feature sets in advance. This pre-processing work reduces the computational burden during actual prediction, enabling faster real-time risk assessment when needed clinically.
Solution Approach 2:
The patent transforms raw electronic health record data into standardized numerical features through parameter changes, including normalization, encoding, and feature engineering. These transformations convert heterogeneous medical data into a unified format that machine learning models can efficiently process.
3Reliability
If real-time risk prediction and visualization is provided to support clinical decision-making, then patient outcome improvement and complication prevention capability is improved, but implementation cost and infrastructure requirements increase
Solution Approach 1:
The system is designed as a universal platform that can predict multiple types of postoperative complications using a single integrated architecture. The same framework handles diverse complication types through separate but coordinated machine learning models, reducing implementation costs compared to developing separate systems for each complication.
Solution Approach 2:
The patent leverages existing electronic health record infrastructure and data storage systems, copying and integrating with already-deployed hospital information systems. This approach avoids the need to build entirely new data collection and management infrastructure, reducing implementation barriers.
Data Source
AI summary
Methods and systems disclosed herein utilize an automated analytics framework to implement a perioperative complication risk algorithm that uses existing clinical data in electronic health records to forecast patient-level probabilistic risk scores for eight major postoperative complications. An example method includes accessing health record data for a patient, normalizing the accessed health record data to generate a health record data set for the patient, transforming one or more features from the health record data set, selecting one or more transformed features from the health record data set, calculating risk probabilities for one or more complication risk categories based on the health record data set and the selected one or more features, calculating mortality risk probabilities for one or more mortality risk categories based on the calculated risk probabilities, and generating a personalized risk panel based on the calculated risk probabilities and the calculated mortality risk probabilities.