Decision Tree Prediction of Acute Kidney Disease from Immune Cell Data
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Solution Overview
Problem
Current clinical indicators for acute kidney disease, such as serum creatinine and blood urea nitrogen, have low accuracy in predicting the progression of acute kidney disease, leading to inconsistent judgments and delayed medical interventions.
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 predict acute kidney disease, employing a supervised learning approach to establish an accurate prediction model.
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 of prediction is low leading to inconsistent judgments
Solution Approach 1:
The patent combines multiple data sources including immune cell population data from flow cytometry, serum creatinine levels, and blood urea nitrogen values into a unified prediction model. This integration of diverse indicators through a decision tree algorithm achieves high accuracy in predicting acute kidney disease progression while maintaining systematic organization of the evaluation process
Solution Approach 2:
The patent introduces a decision tree algorithm as an intermediary computational tool that processes multiple clinical indicators and immune cell data. This algorithmic mediator transforms complex multi-parameter data into accurate predictions of acute kidney disease progression, resolving the inconsistency in conventional evaluation methods
2Reliability
If conventional clinical indicators are used for evaluation, then the evaluation method is simple, but the accuracy is low and medical interventions are delayed
Solution Approach 1:
The patent enables early prediction of acute kidney disease progression by analyzing immune cell population changes and clinical indicators before actual disease progression occurs. The decision tree model identifies high-risk patients in advance, allowing timely medical interventions that prevent progression to chronic kidney disease
Solution Approach 2:
The patent implements a feedback mechanism where the prediction model continuously evaluates patient data and provides risk assessments that guide clinical decision-making. This feedback loop enables dynamic monitoring and timely adjustment of treatment strategies based on predicted disease progression risks
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
Enables early and accurate prediction of acute kidney disease, reducing misjudgment and enabling timely medical interventions, thereby improving patient outcomes and reducing the burden on medical staff.
Implementation Method 1
the immune cell population data were obtained by analyzing the peripheral blood samples using a flow cytometer
Data Source
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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.