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Early Prediction of Causes (not Effects) in Healthcare by Long-Term Clinical Time Series Forecasting by Staniek, Michael; Fracarolli, Marius; Hagmann, Michael; Riezler, Stefan is a scholarly article available to read on EtoBox.

What is Early Prediction of Causes (not Effects) in Healthcare by Long-Term Clinical Time Series Forecasting about?

Machine learning for early syndrome diagnosis aims to solve the intricate task of predicting a ground truth label that most often is the outcome (effect) of a medical consensus definition applied to observed clinical measurements (causes), given clinical measurements observed several hours before. Instead of focusing on the prediction of the future effect, we propose to directly predict the causes via time series forecasting (TSF) of clinical variables and determine the effect by applying the gold standard consensus definition to the forecasted values. This method has the invaluable advantage of being straightforwardly interpretable to clinical practitioners, and because model training does not rely on a particular label anymore, the forecasted data can be used to predict any consensus-based label. We exemplify our method by means of long-term TSF with Transformer models, with a focus on accurate prediction of sparse clinical variables involved in the SOFA-based Sepsis-3 definition and the new Simplified Acute Physiology Score (SAPS-II) definition. Our experiments are conducted on two datasets and show that contrary to recent proposals which advocate set function encoders for time

Author

Staniek, Michael; Fracarolli, Marius; Hagmann, Michael; Riezler, Stefan

Published

2024

Language

EN