AML Prognosis Prediction With Attention-Based Temporal Modeling
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Solution Overview
Problem
Current clinical methods for predicting acute myeloid leukemia (AML) prognosis lack accuracy due to the complex integration of heterogeneous and longitudinal clinical variables, often relying on static attributes without considering temporal aspects, leading to varying treatment outcomes and a lack of consensus in clinical decision-making.
Innovation Solution
A neural network, specifically an attention-based bidirectional long short-term memory (Att-BLSTM), is trained on retrospective patient data to integrate both static and time-dependent clinical variables, converting them into time-series representations and static variables, which are then combined to predict prognosis outcomes such as mortality and relapse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static attributes are used for prognosis prediction, then the model is simple and easy to implement, but the prediction accuracy is insufficient due to lack of temporal information
Solution Approach 1:
The patent transforms static clinical attributes into dynamic time-series representations by capturing temporal progression patterns. The Att-BLSTM model processes time-dependent attributes (e.g., blood cell counts, treatment responses) as sequences, allowing the system to model how patient conditions evolve over time rather than using fixed static values, thereby improving prediction accuracy while managing complexity through structured temporal processing
Solution Approach 2:
The patent adds a temporal dimension to traditional static prognostic models. By converting longitudinal clinical data into time-series representations and using attention mechanisms to weight temporal patterns, the model moves from two-dimensional static feature space to three-dimensional time-aware feature space, enabling more accurate prognosis prediction that accounts for disease progression dynamics
2Reliability
If conventional clinical methods are used for prognosis prediction, then the approach is easy to operate, but the integration of heterogeneous and longitudinal clinical variables is insufficient leading to varying treatment outcomes
Solution Approach 1:
The patent merges multiple types of clinical data (static attributes like demographics and cytogenetics, time-dependent attributes like blood cell counts and treatment responses) into a unified prognostic model. The Att-BLSTM architecture integrates heterogeneous data sources by processing them through common temporal transformation layers, creating a cohesive representation that captures both baseline characteristics and longitudinal progression patterns, thereby improving reliability while systematically managing integration complexity
Solution Approach 2:
The patent introduces time-series representation as an intermediary layer between raw clinical data and final prognosis prediction. This intermediary transformation converts diverse longitudinal measurements into standardized temporal patterns, and the attention mechanism acts as a mediator that selectively weights important temporal features, simplifying the integration of heterogeneous data while maintaining prediction reliability
Data Source
AI summary
This application relates generally to a computer implemented method comprising: receiving a medical record data from a patient, wherein said record comprising a static attribute and a time dependent progression attribute; processing the time dependent progression attributes of medical record data using a trained neural network to into time-series representation, and converting the static attributes into static variables; combining the time-series representation and static variables to multiple vectors; providing a prognosis outcome by a trained classifier using said multiple vectors; wherein the neural network is trained by steps of (a) assembling a training data set comprising a retrospective collection of patients' medical record data wherein said record data comprising collected number of static attributes, time dependent progression attributes and patients' mortality and relapse outcomes; (b) processing the time dependent progression attributes of the training data set using a neural network to convert the time dependent progression attributes into time-series representation; (c) processing the static attributes of the training data set into static variables; and (d) combining the time-series representation and static variables to train a classifier based on the combined time-series representation and static variables.