Clinical Trend Detection Without Data Imputation
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Solution Overview
Problem
Clinical time series data analysis is hindered by the lack of quantitative definition of trends, irregular sampling, and the lack of interpretability of existing methods like recurrent neural networks (RNNs), which are computationally expensive and difficult for clinicians to understand.
Innovation Solution
A system that automatically identifies quantitative trends from clinical data by using a set of parameter definitions for deviation and abnormality detection, allowing for straightforward analysis and alerting clinicians to potential clinical states without the need for data imputation or complex neural networks, utilizing a batch deviation detection module to compare measurements against defined thresholds and times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recurrent neural networks (RNNs) are used for pattern mining in clinical time series, then prediction accuracy is improved, but interpretability deteriorates and computational cost increases
Solution Approach 1:
The patent extracts the essential pattern recognition functionality from complex RNNs by implementing a rule-based system that identifies specific temporal patterns (e.g., trends, deviations, repetitions) in clinical time series data. This extraction approach maintains prediction accuracy for key clinical patterns while eliminating the computational complexity and interpretability issues of neural networks.
Solution Approach 2:
The patent changes the fundamental parameters of the analysis system by transitioning from continuous-weight neural network parameters to discrete, clinically-interpretable parameters such as threshold values, time windows, and pattern rules. This parameter transformation enables both accurate prediction and full interpretability of the detection logic.
2Ease of operation
If data imputation is performed to interpolate between sample times, then regular sampling requirements are met, but data artifacts are created that cause prediction inaccuracies
Solution Approach 1:
Instead of imputing missing data to create regular samples, the patent inverts the approach by designing the analysis method to natively handle irregular sampling intervals. The system computes temporal patterns based on actual observation times rather than forcing data into regular time bins, thereby avoiding artifact creation while maintaining analytical capability.
3Reliability
If quantitative definition of trends is based on single clinician's experience, then clinical relevance is maintained, but automation and objectivity deteriorate
Solution Approach 1:
The patent implements feedback by allowing clinicians to review and adjust the automatically generated quantitative trend definitions based on their clinical expertise. The system iteratively refines threshold values and pattern parameters through clinician feedback, thereby maintaining clinical relevance while achieving full automation and objectivity in the detection process.
Data Source
AI summary
A method (100) for automated prediction of a clinical state, comprising: receiving (120) a set of parameter definitions for a clinical state, the set of parameter definitions comprising a definition for a deviation value, a definition for a deviation time, a definition for a value threshold, and a definition for a value time; receiving (130) a plurality of measurements for at least one feature for a patient, the plurality of measurements taken over a span of time; identifying (140), within the plurality of measurements for at least one feature for a patient, a deviation and/or an abnormality predicting the clinical state, comprising: predicting (150), based upon identification of a deviation and/or abnormality, that the patient is susceptible to or experiencing the clinical state; and providing (160), via a user interface of the event monitoring system, an alert that the patient is susceptible to or experiencing the clinical state.