Summary of Early Prediction Of Causes (not Effects) in Healthcare by Long-term Clinical Time Series Forecasting, By Michael Staniek et al.
Early Prediction of Causes (not Effects) in Healthcare by Long-Term Clinical Time Series Forecasting
by Michael Staniek, Marius Fracarolli, Michael Hagmann, Stefan Riezler
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning for early syndrome diagnosis aims to predict a ground truth label by forecasting clinical variables using time series forecasting (TSF) of clinical measurements. Instead of predicting the effect, we propose directly predicting causes and determining the effect via gold standard consensus definition. This method is interpretable to clinicians and allows model training without relying on specific labels, enabling prediction of any consensus-based label. We exemplify this with long-term TSF using Transformer models for accurate prediction of sparse clinical variables in SOFA-based Sepsis-3 and Simplified Acute Physiology Score (SAPS-II) definitions. Our experiments show best results achieved by combining standard dense encoders with iterative multi-step decoders, capturing cross-variate dependencies through student forcing training strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning tries to help doctors quickly diagnose serious illnesses like sepsis. Instead of trying to guess the outcome, we propose predicting the underlying causes and then using those predictions to determine if a patient has sepsis or not. This method is easy for doctors to understand and allows us to train models without needing specific labels, making it more versatile. We tested this approach with special types of data called time series forecasts and used powerful transformer models to predict sparse clinical variables involved in diagnosing sepsis. Our results show that a combination of standard encoders and iterative decoders works best, capturing relationships between different variables. |
Keywords
» Artificial intelligence » Machine learning » Time series » Transformer
