Prognosis Prediction Device Using Machine Learning for Pneumonia
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Solution Overview
Problem
Current technologies face challenges in predicting the prognosis of diseases like pneumonia, which is a major cause of death among elderly individuals, due to unclear factors involved.
Innovation Solution
A prognosis prediction device and program that utilizes a combination of clinical information and machine learning algorithms, such as decision trees and random forests, to output prognosis information based on input factor information, including clinical data, medical test results, and treatment responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional prediction methods using genetic codes are applied to pneumonia, then genetic disease prognosis can be predicted, but pneumonia prognosis cannot be predicted due to unclear factors
Solution Approach 1:
The patent transforms the prediction approach by changing from genetic code-based parameters to multi-dimensional clinical parameters including laboratory test results, imaging data, and treatment response information. This parameter transformation enables pneumonia prognosis prediction by utilizing parameters that are actually informative for this disease type rather than relying on genetic codes which are unclear for pneumonia.
Solution Approach 2:
The patent segments the prognosis prediction process into multiple stages: initial prognosis prediction based on admission data, intermediate updates based on treatment response, and final prognosis determination. This segmentation allows the system to handle the uncertainty by progressively refining predictions as more clinical information becomes available during treatment.
2Measurement precision
If machine learning models are trained with multiple types of clinical information, then prediction accuracy for pneumonia improves, but the complexity of the prediction system increases
Solution Approach 1:
The patent implements a universal prediction framework that can handle multiple types of clinical information (laboratory tests, imaging, treatment responses) through a single machine learning system. The model is designed to accept diverse input formats and automatically process them, reducing the need for separate specialized systems for each data type while maintaining high prediction precision.
Solution Approach 2:
The machine learning model automatically performs feature selection and weighting, identifying which clinical parameters are most predictive without requiring manual configuration. The system self-optimizes by learning from training data which combinations of clinical information provide the best prognosis predictions, reducing the complexity burden on users.
3Reliability
If prediction results are updated continuously with new clinical information, then prognosis accuracy improves over time, but the processing time and computational resources increase
Solution Approach 1:
The patent implements periodic update cycles where the prognosis prediction is refreshed at specific clinical milestones (e.g., after 48 hours of treatment, after completing a treatment course) rather than continuously. This periodic approach maintains reliable predictions by incorporating new clinical information at meaningful intervals without the continuous computational burden of real-time updates.
Solution Approach 2:
The system performs preliminary prognosis prediction at admission using available data, establishing an initial baseline before treatment begins. This preliminary action allows clinicians to make early decisions while the system continues to learn from subsequent treatment responses, reducing the need for extensive real-time processing while maintaining prediction reliability.
Data Source
AI summary
A prognosis prediction device 1 includes a circuitry for receiving which receives a combination of information including known factor information including at least one type of clinical information and known prognosis information, and circuitry for machine learning which performs machine learning of at least one machine learning model by at least one machine learning algorithm such that the corresponding known prognosis information is outputted in response to an input of the received known factor information. A result of a machine learning is used in a process for predicting a prognosis of a prognosis prediction target patient.