Heart Condition Prediction Using Latent Data Transformation
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Solution Overview
Problem
Current methods for predicting the evolution of heart-related conditions are subjective and biased towards healthcare provider experience, limited by specific medical device inputs, and unable to efficiently cross-compare a wide range of health variables, leading to unreliable predictions.
Innovation Solution
A computer-implemented method that transforms a set of health variable inputs into a latent data representation space using a data space transformation model and a prediction model, allowing for the determination of index values representing the risk of unfavorable outcomes, which is more objective and versatile, capable of using various inputs from different sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-based prediction methods are used, then predictions can be made using available health variables, but the predictions are biased by healthcare provider experience and cannot efficiently cross-compare a high number of health variables
Solution Approach 1:
The patent replaces the mechanical system of human-based prediction with an automated computational system. A processing unit executes a method that automatically retrieves health variables from multiple sources, normalizes them, and performs cross-comparison without human intervention. This substitution eliminates bias from healthcare provider experience while efficiently handling numerous health variables through systematic computational processing.
2Productivity
If automated methods using specific medical device inputs are used, then prediction speed is improved, but the methods are limited to specific device inputs and cannot use a wide variety of data sources
Solution Approach 1:
The patent implements a universal prediction system that can process health variables from multiple diverse data sources including implantable medical devices, insertable medical devices, external medical devices, and health records. The system normalizes inputs from different sources and types, enabling it to function with any combination of data sources rather than being limited to specific device inputs, thus achieving both speed and versatility.
Solution Approach 2:
The patent introduces a normalization step as an intermediary process between data retrieval and prediction. This intermediary normalizes health variables from different sources and types into a common format, allowing the system to efficiently process diverse data without being constrained by source-specific formats, thereby maintaining prediction speed while expanding data source compatibility.
3Reliability
If comprehensive health variable data is collected from multiple sources, then prediction reliability is improved, but data processing complexity and time requirements increase
Solution Approach 1:
The patent performs preliminary actions by retrieving and normalizing health variables from multiple data sources before the actual prediction process. The system systematically collects data from implantable devices, insertable devices, external devices, and health records in advance, preparing the data structure and normalization factors beforehand. This preliminary data preparation enables faster and more reliable predictions when needed, as the comprehensive data is already organized and ready for analysis.
Data Source
AI summary
A computer-implemented method for predicting an evolution of at least one heart-related condition of a patient, the method including, for each heart-related condition: a) based on a set of inputs, at least part of the set of inputs being representative of a temporal evolution of a corresponding health variable among a predetermined set of health variables of the patient, computing a set of transformed variables, the set of transformed variables being a representation of the set of inputs in a predetermined latent data representation space; and b) determining, based on the computed set of transformed variables, at least one index value, each representative of a respective risk of an unfavorable outcome of the heart-related condition.