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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.10455 |
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| _version_ | 1866915933351575552 |
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| author | Zhang, Hengyu Zhang, Xuyun Zhan, Pengxiang Luo, Linhao Lv, Hang Tan, Yanchao Pan, Shirui Yang, Carl |
| author_facet | Zhang, Hengyu Zhang, Xuyun Zhan, Pengxiang Luo, Linhao Lv, Hang Tan, Yanchao Pan, Shirui Yang, Carl |
| contents | Recent advances in large language models (LLMs) have enabled promising progress in diagnosis prediction from electronic health records (EHRs). However, existing LLM-based approaches tend to overfit to historically observed diagnoses, often overlooking novel yet clinically important conditions that are critical for early intervention. To address this, we propose EviCare, an in-context reasoning framework that integrates deep model guidance into LLM-based diagnosis prediction. Rather than prompting LLMs directly with raw EHR inputs, EviCare performs (1) deep model inference for candidate selection, (2) evidential prioritization for set-based EHRs, and (3) relational evidence construction for novel diagnosis prediction. These signals are then composed into an adaptive in-context prompt to guide LLM reasoning in an accurate and interpretable manner. Extensive experiments on two real-world EHR benchmarks (MIMIC-III and MIMIC-IV) demonstrate that EviCare achieves significant performance gains, which consistently outperforms both LLM-only and deep model-only baselines by an average of 20.65\% across precision and accuracy metrics. The improvements are particularly notable in challenging novel diagnosis prediction, yielding average improvements of 30.97\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10455 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning Zhang, Hengyu Zhang, Xuyun Zhan, Pengxiang Luo, Linhao Lv, Hang Tan, Yanchao Pan, Shirui Yang, Carl Computation and Language Recent advances in large language models (LLMs) have enabled promising progress in diagnosis prediction from electronic health records (EHRs). However, existing LLM-based approaches tend to overfit to historically observed diagnoses, often overlooking novel yet clinically important conditions that are critical for early intervention. To address this, we propose EviCare, an in-context reasoning framework that integrates deep model guidance into LLM-based diagnosis prediction. Rather than prompting LLMs directly with raw EHR inputs, EviCare performs (1) deep model inference for candidate selection, (2) evidential prioritization for set-based EHRs, and (3) relational evidence construction for novel diagnosis prediction. These signals are then composed into an adaptive in-context prompt to guide LLM reasoning in an accurate and interpretable manner. Extensive experiments on two real-world EHR benchmarks (MIMIC-III and MIMIC-IV) demonstrate that EviCare achieves significant performance gains, which consistently outperforms both LLM-only and deep model-only baselines by an average of 20.65\% across precision and accuracy metrics. The improvements are particularly notable in challenging novel diagnosis prediction, yielding average improvements of 30.97\%. |
| title | EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.10455 |