Public articles linked to the same research event.
arXiv The work proposes RASPER, a reward-aligned summarizer for EHR prediction: a tunable LLM-based summarizer extracts task-relevant evidence from discharge notes and is trained via reinforcement learning with a reward derived from the downstream predictor's loss, while a longitudinal encoder converts structured codes into soft prompts that inject each patient's clinical context into summarization, so that summaries retain patient-specific evidence complementing structured codes; RASPER consistently outperforms strong baselines on readmission prediction and medication recommendation across MIMIC-III and MIMIC-IV.
The work proposes RASPER, a reward-aligned summarizer for EHR prediction: a tunable LLM-based summarizer extracts task-relevant evidence from discharge notes and is trained via reinforcement learning with a reward derived from the downstream predictor's loss, while a longitudinal encoder converts structured codes into soft prompts that inject each patient's clinical context into summarization, so that summaries retain patient-specific evidence complementing structured codes; RASPER consistently outperforms strong baselines on readmission prediction and medication recommendation across MIMIC-III and MIMIC-IV.
The work proposes RASPER, a reward-aligned summarizer for EHR prediction: a tunable LLM-based summarizer extracts task-relevant evidence from discharge notes and is trained via reinforcement learning with a reward derived from the downstream predictor's loss, while a longitudinal encoder converts structured codes into soft prompts that inject each patient's clinical context into summarization, so that summaries retain patient-specific evidence complementing structured codes; RASPER consistently outperforms strong baselines on readmission prediction and medication recommendation across MIMIC-III and MIMIC-IV.
The work proposes RASPER, a reward-aligned summarizer for EHR prediction: a tunable LLM-based summarizer extracts task-relevant evidence from discharge notes and is trained via reinforcement learning with a reward derived from the downstream predictor's loss, while a longitudinal encoder converts structured codes into soft prompts that inject each patient's clinical context into summarization, so that summaries retain patient-specific evidence complementing structured codes; RASPER consistently outperforms strong baselines on readmission prediction and medication recommendation across MIMIC-III and MIMIC-IV.