Papers with clinical prediction tasks
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records (2024.acl-short)
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| Challenge: | Existing deep learning models for EHRs rely on knowledge from a single source and do not capture the semantic information for medical codes. |
| Approach: | They propose a Retrieval AugMentation pipeline to augment clinical prediction on EHRs . they use multiple knowledge sources to convert them into text and use consistency regularization to capture complementary information from patient visits and summarized knowledge. |
| Outcome: | Experiments on two EHR datasets show that RAM-EHR improves clinical prediction tasks. |
On the Impact of Random Seeds on the Fairness of Clinical Classifiers (2021.naacl-main)
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| Challenge: | Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s). |
| Approach: | They explore the implications of this phenomenon for model fairness across demographic groups in clinical prediction tasks over electronic health records (EHR) they find that jointly optimizing for high overall performance and low disparities does not yield statistically significant improvements. |
| Outcome: | The proposed model fairness is based on the MIMIC-III dataset, the standard dataset in clinical NLP research. |
Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models (2025.findings-naacl)
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| Challenge: | Language models (LMs) have significant potential for clinical prediction tasks . however, unreliable decisions can result in significant costs due to compromised patient safety and ethical concerns . |
| Approach: | They propose to combine ensembling and multi-tasking approaches to reduce uncertainty in EHRs by using multi-tapping methods. |
| Outcome: | The proposed framework reduces model uncertainty in white-box and black-box settings, and improves model transparency in both settings. |
MedCPI: A Construct–Personalize–Integrate Framework for KG-enhanced Clinical Prediction (2026.findings-acl)
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| Challenge: | Existing KG-enhanced approaches to clinical prediction are limited . existing approaches to personalize and integrate knowledge are weakly controlled . |
| Approach: | They propose a framework to integrate medical knowledge graphs into EHRs to support KG-enhanced clinical prediction. |
| Outcome: | The proposed framework improves on MIMIC-III and MIMIC IV tasks. |
RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown promising results for mining EHRs . translating time-stamped sequences into plain text can obscure both temporal structure and code identities, weakening the ability to capture code co-occurrence and longitudinal regularities. |
| Approach: | They propose a time-aware LLM framework that integrates structured EHR encoders through prompt tuning without modifying underlying architectures. |
| Outcome: | Experiments on MIMIC-III and MIMIC IV show that RePrompT outperforms both EHR-based and LLM-based baselines across multiple clinical prediction tasks. |