Acute Kidney Disease Prediction With Immune Data and Decision Trees
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Solution Overview
Problem
Current clinical indicators for acute kidney disease (AKD), such as serum creatinine and blood urea nitrogen, have low accuracy in predicting the progression of AKD due to varying patient conditions, leading to challenges in timely and accurate medical intervention.
Innovation Solution
A method and system using a decision tree algorithm to analyze immune cell population data obtained through flow cytometry, combined with serum creatinine and blood urea nitrogen values, to establish an AKD prediction model, facilitating early and accurate prediction of AKD progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If serum creatinine and blood urea nitrogen are used as clinical indicators to evaluate acute kidney disease, then the evaluation can be performed using conventional methods, but the accuracy is low due to varying patient conditions
Solution Approach 1:
The patent combines multiple data sources including immune cell population data from flow cytometry, serum creatinine levels, blood urea nitrogen levels, and other clinical parameters into a comprehensive prediction model. This integration of diverse indicators allows the system to maintain high prediction accuracy while adapting to varying patient conditions, resolving the contradiction between measurement precision and adaptability
Solution Approach 2:
The patent transforms the prediction approach by changing from relying on single clinical parameters to using a multi-parameter decision tree model that incorporates immune cell populations, renal function indicators, and other variables. This parameter transformation enables the system to adapt to different patient conditions while maintaining high prediction accuracy for acute kidney disease progression
2Reliability
If conventional clinical indicators are used for AKD evaluation, then the evaluation process is simple, but the timing of treatment cannot be grasped accurately
Solution Approach 1:
The patent implements preliminary prediction of acute kidney disease progression before actual disease occurrence by analyzing immune cell population changes and clinical parameters in advance. The decision tree model enables early identification of high-risk patients, allowing treatment timing to be grasped accurately before disease progression, thus improving reliability without requiring overly complex real-time monitoring systems
Solution Approach 2:
The patent segments the prediction process into distinct stages using a decision tree structure, where different immune cell populations and clinical parameters are analyzed at different nodes to determine progression risk. This segmentation allows the system to manage complexity by breaking down the prediction task into manageable steps while maintaining high treatment timing accuracy
3Measurement precision
If immune cell population data combined with decision tree algorithm is used, then prediction accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent replaces complex manual evaluation processes with an automated decision tree algorithm that systematically analyzes immune cell population data and clinical parameters. This substitution of mechanical analysis with algorithmic processing improves prediction accuracy while managing system complexity through automated, rule-based decision-making that reduces the need for complex human judgment processes
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method and system enable rapid and precise determination of AKD development, reducing inconsistencies in medical judgment and enabling timely medical interventions, thereby improving patient outcomes.
Implementation Method 1
the immune cell population data were obtained by analyzing the peripheral blood samples using a flow cytometer
Data Source
AI summary
Embodiments of the present disclosure are directed to a prediction method and system for acute kidney disease. The prediction method includes the following steps: inputting a plurality of immune cell population data, a plurality of serum creatinine values, and a plurality of blood urea nitrogen values through an input device, and storing the immune cell population data, the serum creatinine values, and the blood urea nitrogen values in a storage device; accessing a processor to the storage device, and using the immune cell population data, the serum creatinine values, and the blood urea nitrogen values as parameters to establish an acute kidney disease prediction model with a decision tree algorithm; obtaining an immune cell population data, a serum creatinine value, and a blood urea nitrogen value assessed through the input device, and using the processor to perform an interpretation program to obtain an interpretation result of acute kidney disease; outputting the interpretation result of acute kidney disease through an output device. The system includes an input device, a storage device, a processor, and an output device.