Kernekoncepter
Machine learning models in healthcare can be enhanced by a Retrieval-Enhanced Medical prediction model (REMed) that eliminates the need for manual event selection, improving prediction performance and reducing expert involvement.
Resumé
The article introduces REMed, a novel medical prediction model that leverages a Retrieval-Based Approach to handle near-infinite medical events. By eliminating the need for manual event selection, REMed outperforms baselines across 27 clinical prediction tasks. The model's architecture involves a Retriever and Predictor trained through alternating paths to process unlimited events efficiently. REMed showcases compatibility with established clinical knowledge and offers potential for accelerating medical prediction model development.
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Introduction
- ML's potential in predicting medical outcomes using EHRs.
- Challenges of input size and event selection bottleneck.
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Event Selection Methods
- Feature selection vs. observation window selection.
- Approaches to eliminate feature selection.
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Retrieval-Enhanced Medical Prediction Model
- Architecture overview of REMed.
- Training strategy involving Retriever and Predictor paths.
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Experimental Results
- Performance analysis on various datasets and tasks.
- Comparison with baselines like GenHPF and Flattened models.
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Retriever Analysis
- Evaluation of R's ability to identify relevant events based on codes, details, and timestamps.
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Discussion
- Limitations of REMed in capturing event correlations and adapting to new tasks.
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Methods
- Detailed experimental setups, including dataset utilization, baseline models, training configurations, and performance analyses.
Statistik
"We trained REMed on 27 clinical prediction tasks."
"Our contributions can thus be summarized as follows."
Citater
"We propose REMed, the first attempt to introduce the Retrieval-Based Approach to the medical prediction task."
"Our contributions can thus be summarized as follows."