Mortality Prediction System Using Timepoint-Specific Classifiers
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Solution Overview
Problem
Existing scoring systems for predicting patient mortality in ICUs are unreliable due to the need for specific investigations and conditions that may not be performed or recorded, making them inaccurate and difficult to automate in Clinical Decision Support Systems.
Innovation Solution
A method and apparatus that utilize a fully automated system to collect and process data from vital measurement devices and Electronic Medical Records to train classifiers for each measurement timepoint, predicting mortality risk without relying on manual observations, and transmit alarms to health administration servers when a high risk is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scoring systems (APACHE II, SAPS II, MPM, SOFA) are used to predict patient mortality, then mortality prediction can be performed, but the accuracy deteriorates due to reliance on specific investigations and conditions that may not be performed or recorded
Solution Approach 1:
The patent extracts and removes the dependency on specific investigations and conditions from the mortality prediction system. Instead of requiring complete data from specific tests and conditions, the system uses readily available electronic health record data and vital signs, eliminating the bottleneck caused by missing or unperformed investigations.
Solution Approach 2:
The patent creates a universal mortality prediction model that can handle multiple data sources and types (vital signs, lab results, demographic information) without requiring specific conditions to be met. The model is designed to work with incomplete or varying data sets, making it universally applicable across different clinical settings and patient populations.
2Extent of automation
If traditional scoring systems are used, then mortality assessment can be performed, but automation becomes difficult due to manual observation requirements
Solution Approach 1:
The system enables self-service automation by directly interfacing with electronic health record systems and vital signs monitoring devices. The mortality prediction model automatically retrieves and processes data without requiring manual observation or data entry, allowing the system to serve itself by pulling data from existing automated clinical information sources.
Solution Approach 2:
The patent replaces the mechanical process of manual observation and data collection with automated electronic data retrieval and processing. The system uses computer algorithms to automatically analyze patient data and generate mortality predictions, substituting human manual operations with automated computational processes.
3Measurement precision
If comprehensive data collection from multiple sources is implemented, then prediction accuracy improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent segments the mortality prediction system into distinct modular components: data collection modules that gather information from various sources (vital signs, lab results, demographics), a processing module that applies the prediction model, and an output module that delivers results. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining comprehensive data collection.
Data Source
AI summary
A method, non-transitory computer readable medium and apparatus for predicting mortality of a current patient are disclosed. For example, the method includes receiving data associated with a plurality of different patients with known mortality outcomes, wherein the data includes a subset of data for each one of a plurality of different measurement timepoints for each one of the plurality of different patients, calculating n number of classifiers, wherein n is equal to a number of the plurality of different measurement timepoints, receiving data associated with the current patient at an i-th measurement timepoint, predicting the current patient has a high mortality risk based on an output of the i-th classifier of the n number of classifiers and transmitting a signal to a health administration server to cause an alarm to be generated in response to the high mortality risk that is predicted.