RASPER: Reward-Aligned Summarization of Clinical Notes for EHR Outcome Prediction

Arya Hadizadeh Moghaddam; Mohsen Nayebi Kerdabadi; Chen Chen; Dongjie Wang; Zijun Yao

cs.AIOct 2, 2026

RASPER: Reward-Aligned Summarization of Clinical Notes for EHR Outcome Prediction

Authors: Arya Hadizadeh Moghaddam, Mohsen Nayebi Kerdabadi, Chen Chen, Dongjie Wang, Zijun Yao

Organizations: Electrical Engineering and Computer Science, University of Kansas, USA

Abstract

Unstructured discharge notes in Electronic Health Records (EHRs) often carry signal complementary to structured medical codes, holding patient-specific evidence that standardized cohort-level codes alone cannot capture. However, this evidence in notes is frequently buried in lengthy, noisy text that is not intentionally written with any specific clinical prediction in mind. Summarization is an obvious mitigation, but generic summaries, tuned for fluency rather than the outcome, routinely omit decisive evidence while retaining plausible but uninformative detail. To this end, we propose RASPER, a Reward-Aligned Summarizer for Prediction in EHR, that optimizes note summarization directly against the downstream clinical task. RASPER employs a tunable LLM-based summarizer to extract task-relevant evidence from discharge notes and trains it via reinforcement learning from prediction feedback, using a reward derived from the downstream predictor's loss. To ground the summarizer, a longitudinal encoder converts structured codes into soft prompts that incorporate each patient's clinical context into note summarization. By rewarding the quality of the resulting multimodal prediction, RASPER encourages the summarizer to retain patient-specific evidence that complements, rather than duplicates, information captured by structured codes. RASPER consistently outperforms strong baselines on both readmission prediction and medication recommendation across MIMIC-III and MIMIC-IV.

Figures & tables

Figure 1: Overview of the proposed RASPER framework. A tunable LLM summarizer transforms discharge notes into standardized visit-level summaries, which are concatenated into a patient-level summary. An LLM-aware predictor combines this patient-level summary with soft prompt embeddings from structured EHR codes for outcome prediction. The summarizer is first initialized with supervised fine-tuning and then optimized with RLPF using prediction-loss-derived reward within the PPO objective.

Figure 2: Prompt used by the tunable LLM summarizer to extract relevant information from discharge notes.

Component	Hyperparameter / Symbol	Value
Soft prompt encoder	$P$	8
LLM hidden size	$D$	2304
Max Summary length	$C_{i}$	512
Reward normalization	$\epsilon$	$1\times 10^{-8}$
Reward clipping	$r_{\max}$	2.0
PPO clipping	$\epsilon_{\text{PPO}}$	0.2

Table 2: Hyperparameters used in the proposed framework.

	MIMIC-III				MIMIC-IV
Model	Readmission		Medication		Readmission		Medication
	AUROC	PRAUC	F1	Jaccard	AUROC	PRAUC	F1	Jaccard
DL-based Methods
Deepr Nguyen et al. (2017)	0.671	0.702	0.255	0.160	0.621	0.651	0.228	0.189
RETAIN Choi et al. (2016)	0.657	0.678	0.253	0.156	0.673	0.698	0.349	0.255
GRASP Zhang et al. (2021)	0.618	0.635	0.153	0.096	0.535	0.578	0.267	0.195

Table 3: Baseline performance comparison on MIMIC-III and MIMIC-IV. Best results are in bold, and underlined results denote the second-best results that are statistically lower at the 95% confidence level.

	MIMIC-III				MIMIC-IV
Model Variant	Readmission		Medication		Readmission		Medication
	AUROC	PRAUC	F1	Jaccard	AUROC	PRAUC	F1	Jaccard
RASPER w/o EHR encoder	0.661	0.698	0.288	0.187	0.685	0.704	0.353	0.258
RASPER w/o Summarization	0.674	0.712	0.298	0.191	0.691	0.713	0.363	0.262
RASPER w/o RL (GPT only)	0.689	0.725	0.309	0.201	0.708	0.723	0.371	0.272
RASPER w/o RL (SFT only)	0.684	0.721	0.305	0.198	0.701	0.718	0.367	0.269

Table 4: Ablation studies on MIMIC-III and MIMIC-IV. Best results are in bold, and underlined results denote the second-best results that are statistically lower at the 95% confidence level.

Panel of Figure 3 — Figure 3: Performance comparison across different EHR models on MIMIC-III and MIMIC-IV.

	MIMIC-III				MIMIC-IV
Backbone	Readmission		Medication		Readmission		Medication
	AUROC	PRAUC	F1	Jaccard	AUROC	PRAUC	F1	Jaccard
Llama-3.2-1B	0.701	0.734	0.321	0.214	0.722	0.737	0.384	0.278
Qwen2.5-0.5B	0.697	0.730	0.315	0.209	0.716	0.731	0.379	0.276
Gemma-2-2B	0.704	0.738	0.326	0.218	0.725	0.742	0.388	0.282

Table 5: Comparison of LLM backbones on MIMIC-III and MIMIC-IV across two tasks.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Model	Time (s)
Deepr	0.09
RETAIN	0.06
GRASP	0.17
AdaCare	0.05
StageNet	0.11
Adore	0.21

Appendix

Table 6: Inference time comparison for a batch of 12 patients.

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Predicting future clinical outcomes from electronic health records (EHR) remains challenging due to the complexity and heterogeneity of patient data. LLMs have shown strong potential for such predictive tasks, yet existing approaches mainly focus on enhancing medical knowledge through distillation or RAG while relying on the model's internal ability to interpret contextual information. In this work, we present ReMedi (Reasoner for Medical Clinical Prediction), a framework for improving clinical outcome prediction from EHR. ReMedi generates rationale-answer pairs using a challenging sample regeneration mechanism for complex clinical questions, which leverages ground-truth answers as hints to enhance reasoning for further fine-tuning and preference tuning. ReMedi integrates ground-truth outcome guidance into the preference data construction loop, regenerating rationale-answer variants. By tuning on these rationale-answer pairs, the model improves its predictive performance. Experiments on multiple EHR prediction tasks demonstrate substantial gains of up to 19.9 percent over state-of-the-art baselines in terms of F1 score, underscoring ReMedi's effectiveness in real-world clinical prediction.

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