Machine Learning System for Patient Mortality Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current healthcare systems face challenges in identifying clusters of patients with similar features related to a particular illness and determining the most critical factors that reduce mortality, leading to inefficient resource allocation and higher healthcare costs.
Innovation Solution
A machine learning-driven system that analyzes historical patient datasets to identify clusters and rank treatable health parameters based on their impact on mortality, providing personalized treatment recommendations to reduce mortality and increase survival rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning algorithms are used to analyze patient data and identify mortality-reducing factors, then patient survival rates improve, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex medical decision-making process into distinct functional modules: data collection module, cluster analysis module, feature ranking module, and recommendation module. Each module handles a specific aspect of the analysis, making the overall system more manageable and interpretable while maintaining high predictive accuracy for patient outcomes
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw electronic health record data into structured patient clusters and ranked feature lists. This intermediary layer acts as a bridge between raw data and clinical decisions, reducing the complexity burden on both the system architecture and end-users by pre-processing and organizing information before presentation
2Measurement precision
If comprehensive patient data is analyzed to identify all factors influencing mortality, then prediction accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary clustering of patients based on their electronic health records before detailed analysis. By pre-grouping patients into clusters with similar characteristics, the system reduces the search space for identifying mortality-reducing factors, enabling faster processing while maintaining comprehensive analysis of relevant features within each cluster
Solution Approach 2:
The patent extracts and ranks only the most critical features within each patient cluster that have the greatest impact on mortality. Instead of analyzing all possible patient data uniformly, the system identifies and focuses on the top-ranked features for each cluster, reducing computational burden while preserving prediction accuracy by concentrating resources on the most influential factors
3Productivity
If clusters of patients with similar features are identified and treated differently, then treatment effectiveness improves, but resource allocation complexity increases
Solution Approach 1:
The system applies local quality by providing customized treatment recommendations specific to each patient cluster rather than uniform treatment protocols. Each cluster receives targeted interventions based on the ranked features most relevant to that group's characteristics, improving treatment effectiveness by addressing the specific needs of each cluster while maintaining systematic resource allocation through automated clustering
Data Source
AI summary
A diagnostic and decision support technology is provided for determining the likely prognosis and potential treatment for patients experiencing a condition, such as COVID-19, for example. In particular, a mechanism is provided for receiving a historical patient dataset comprising one or more historical health parameters associated with a plurality of historical patients. Additionally, a patient dataset is received comprising one or more patient health parameters associated with a patient. A cluster is identified based on the similarity of the patient dataset and a plurality of historical patient datasets. From the cluster, a set of treatable features are identified and evaluated for their potential impact on the patient's successful recovery from the condition. A recommendation is generated based on the evaluation as to what feature should be treated first to decrease the mortality of the patient.