Machine Learning Decision Engine for Healthcare Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing volume of healthcare data exchanged between providers makes it infeasible for humans to effectively evaluate and make decisions, necessitating improved data management and analysis using advanced machine learning techniques.
Innovation Solution
A system that processes large volumes of healthcare data using machine learning to extract insights, generate predictions, and provide recommendations by parsing data files, loading them into database tables, and employing machine learning models for automated interventions and notifications, with the ability to track health metrics and adjust interventions based on trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human evaluation of healthcare data is used, then decision-making can be made with understanding and context, but the process becomes infeasible as data volume grows
Solution Approach 1:
The patent replaces the mechanical system of human data evaluation with an automated machine learning system. The system uses trained models to process healthcare data, generate insights, and make decisions without human intervention in the evaluation process. This substitution enables the system to handle large volumes of data while maintaining consistent decision quality through standardized algorithms.
2Productivity
If automated machine learning processing is implemented, then data processing capacity increases, but the system requires complex infrastructure and model training
Solution Approach 1:
The patent segments the complex machine learning system into distinct functional modules: data ingestion components that collect healthcare data, processing components that prepare and clean the data, model training components that develop predictive models, and deployment components that deliver insights. This segmentation allows each module to be developed, maintained, and scaled independently, reducing overall system complexity.
Solution Approach 2:
The patent introduces intermediary layers between raw data and final decisions, including data preprocessing pipelines that standardize inputs, feature engineering components that extract meaningful patterns, and model evaluation layers that validate predictions. These intermediaries simplify the core processing logic while managing the complexity of handling diverse healthcare data sources.
3Measurement precision
If continuous model training is performed to improve future predictions, then prediction accuracy improves, but computational resources and time are consumed
Solution Approach 1:
The patent implements periodic model training instead of continuous training, where models are retrained at scheduled intervals or when triggered by specific conditions such as accumulating a threshold amount of new data or detecting performance degradation. This periodic approach maintains prediction accuracy while significantly reducing computational resource consumption compared to continuous training.
Solution Approach 2:
The patent applies partial retraining strategies where only specific components of the model are updated rather than complete retraining, or where training is performed on sampled subsets of data when full retraining is not necessary. This approach maintains sufficient prediction accuracy while reducing the computational burden of continuous full-model training.
Data Source
AI summary
Systems and methods for medical intervention using machine learning techniques are provided. One or more embodiments include receiving data associated with a medical condition from a user. Based on the user, a patient profile is retrieved from a database. An intervention is determined for the medical condition based on a patient history provided as an input to a machine learning model that recommends interventions. The intervention is sent to a user device associated with the user. Changes are monitored in one or more health metrics associated with the user. The machine learning models are re-trained based on the monitored changes and the intervention.