Deep Learning EHR Prediction with Attention Mechanisms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Healthcare providers face challenges in efficiently managing and allocating attention to the vast amount of information in electronic health records, leading to a 'poverty of attention' and difficulties in predicting future clinical events and highlighting relevant medical events in a timely manner.
Innovation Solution
A system and method utilizing deep learning models to aggregate and standardize electronic health records, incorporating attention mechanisms to predict future clinical events and summarize pertinent past medical events, displayed on a provider-facing interface to assist healthcare providers in prioritizing patient care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models with attention mechanisms are implemented to predict and summarize medical events, then prediction accuracy and information relevance are improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments the complex task of clinical prediction into multiple components: data aggregation from EHRs, standardization of medical records, deep learning model processing, attention mechanism for relevance weighting, and summary generation. This segmentation allows each component to be optimized independently while managing overall system complexity.
Solution Approach 2:
The patent introduces an intermediary attention mechanism that acts as a mediator between the raw medical data and the prediction output. This attention mechanism weights and prioritizes relevant features, enabling accurate predictions while reducing the computational burden by focusing only on critical information rather than processing all data uniformly.
2Reliability
If comprehensive electronic health records are analyzed to predict future clinical events, then prediction reliability is improved, but information processing time and attention requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-aggregating and standardizing electronic health records before prediction is needed. Medical records are organized and pre-processed into structured formats, allowing the deep learning model to quickly access and analyze relevant information when prediction is required, thereby reducing real-time processing time while maintaining comprehensive data analysis.
Solution Approach 2:
The patent replaces manual information processing and cognitive attention with automated deep learning models. The attention mechanism algorithmically identifies and weights relevant features without human intervention, substituting mechanical computational processes for human cognitive efforts, thereby improving both reliability and processing efficiency.
3Quantity of substance
If deep learning models process vast amounts of medical data, then completeness of medical event coverage is improved, but computational energy consumption increases
Solution Approach 1:
The attention mechanism implements local quality by assigning different weights to different parts of the input data based on their relevance to the prediction task. Instead of uniformly processing all medical data with the same computational resources, the system focuses computational energy on locally important features and patterns, thereby processing vast amounts of data efficiently while reducing overall energy consumption.
Data Source
AI summary
A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.