Papers with clinical prediction
Boosting Transformers and Language Models for Clinical Prediction in Immunotherapy (2023.acl-industry)
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| Challenge: | Current machine learning approaches to predict clinical outcomes are limited to tabular data and are not applicable to clinical prediction. |
| Approach: | They investigate the potential of transformers to improve clinical prediction compared to conventional machine learning approaches and address the challenge of few-shot learning in predicting rare disease areas. |
| Outcome: | The proposed model improves the accuracy of baseline models and language models under few-shot regimes and shows that it is more accurate than previous models. |
One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction (2026.acl-srw)
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| Challenge: | Existing single-agent strategies sample from one role-conditioned distribution, and multi-agend frameworks use fixed roles with flat majority voting, discarding the diagnostic signal in disagreement. |
| Approach: | They propose a case-adaptive multi-agent panel where an attending-physician agent dynamically assembles a specialist panel tailored to each case’s diagnostic uncertainty. |
| Outcome: | The proposed model outperforms baseline models on diagnostic prediction and brief hospital course generation using MIMIC-IV. |
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. |
Tree-of-Evidence: Efficient "System 2" Search for Faithful Multimodal Grounding (2026.findings-acl)
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| Challenge: | Attention-based methods fail to faithfully represent the model’s decision process when integrating heterogeneous modalities. |
| Approach: | They propose an inference-time search algorithm that frames interpretability as a discrete optimization problem. |
| Outcome: | The proposed algorithm retains over 98% of full-model AUROC with as few as five evidence units and achieves higher decision agreement and lower error than LIME, SHAP, saliency, and concept-bottleneck baselines under sparse budgets. |