Papers with patient outcome prediction

2 papers
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning (2023.eacl-main)

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Challenge: Recent advances apply artificial intelligence to predict clinical events or infer the probable diagnosis for clinical decision support.
Approach: They propose a hypernetwork-based approach that generates task-conditioned parameters and coefficients of multitask prediction heads to learn task-specific prediction and balance the multitask learning.
Outcome: Experiments on clinical notes from the real-world MIMIC database show that the proposed model can achieve better performance than baselines and improve zero-shot prediction on unseen diagnoses.
Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness (2026.findings-acl)

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Challenge: Existing methods to accommodate missingness in clinical time series, but how to extract and use information carried by the observation process itself remains underexplored.
Approach: They propose a patient representation learning framework that leverages informative missingness to learn multimodal clinical time series from structured and textual data.
Outcome: The proposed framework improves offline treatment policy learning and adverse outcome prediction on ICU sepsis cohorts from MIMIC-III, MIMIC IV, and eICU.

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