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arxiv: 2604.10455 · v1 · submitted 2026-04-12 · 💻 cs.CL

Recognition: unknown

EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning

Carl Yang, Hang Lv, Hengyu Zhang, Linhao Luo, Pengxiang Zhan, Shirui Pan, Xuyun Zhang, Yanchao Tan

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:00 UTC · model grok-4.3

classification 💻 cs.CL
keywords diagnosis predictionelectronic health recordslarge language modelsin-context reasoningnovel diagnosesevidence constructiondeep model guidanceclinical decision support
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The pith

EviCare integrates deep model outputs into LLM prompts to better capture novel diagnoses from electronic health records.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces EviCare as an in-context reasoning framework that first runs deep model inference to pick candidate diagnoses from patient records, then prioritizes relevant evidence and builds relational links among them. These steps create structured prompts that steer large language models away from repeating only historically common diagnoses. The authors test the method on two large real-world hospital datasets and report consistent gains over approaches that use either LLMs or deep models by themselves. A sympathetic reader would care because early detection of clinically new conditions can support timely intervention before standard patterns catch them. The central object carrying the argument is the three-stage evidence pipeline that turns raw records into focused, interpretable LLM input.

Core claim

EviCare performs deep model inference for candidate selection, evidential prioritization for set-based EHRs, and relational evidence construction for novel diagnosis prediction, then composes these signals into an adaptive in-context prompt that guides LLM reasoning more accurately than direct prompting with raw inputs.

What carries the argument

The EviCare three-stage pipeline of deep model candidate selection, evidential prioritization, and relational evidence construction that feeds into adaptive LLM prompts.

If this is right

  • Diagnosis prediction on EHR benchmarks shows higher precision and accuracy than either LLM-only or deep model-only baselines.
  • Gains are largest for novel diagnoses that do not appear in historical training patterns.
  • The evidence-based prompts make the LLM outputs more interpretable by exposing the relational links used in reasoning.
  • The hybrid approach works on both MIMIC-III and MIMIC-IV datasets without task-specific retraining of the language model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same candidate-selection and evidence-construction steps could be tested on non-diagnosis tasks such as medication recommendation or risk stratification.
  • Hospitals could run the deep model on their local data first to adapt the evidence pipeline before feeding prompts to any public LLM.
  • If the deep model component is replaced by a simpler rule-based selector, performance on novel cases would likely drop, providing a direct test of the hybrid design.

Load-bearing premise

Deep model inference can reliably select candidate diagnoses and build relational evidence that captures clinically novel conditions without selection bias or loss of critical patient details.

What would settle it

Replace the deep model candidate selector with random selection on the MIMIC-IV benchmark and check whether the reported gains in novel diagnosis prediction disappear.

Figures

Figures reproduced from arXiv: 2604.10455 by Carl Yang, Hang Lv, Hengyu Zhang, Linhao Luo, Pengxiang Zhan, Shirui Pan, Xuyun Zhang, Yanchao Tan.

Figure 1
Figure 1. Figure 1: Diagnosis Prediction Patterns Across Models. (a) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The overall framework of our proposed EviCare, which consists of (1) deep model inference for Candidate Selection, (2) Evidential Prioritization for set-based EHRs, (3) Relational Evidence Construction for novel diagnosis prediction, and (4) LLM-based Diagnosis Prediction via evidence-driven in-context learning. This analysis provides a theoretical justification for using ranked logits as an information pr… view at source ↗
Figure 3
Figure 3. Figure 3: P@10 performance on novel and overall diagnosis [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: P@10 performance on novel and overall diagnosis [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
read the original abstract

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\%.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes EviCare, an in-context reasoning framework for diagnosis prediction from EHRs that integrates deep model guidance via three steps: (1) deep model inference for candidate selection, (2) evidential prioritization for set-based EHRs, and (3) relational evidence construction for novel diagnosis prediction. These are composed into adaptive prompts for LLMs. Experiments on MIMIC-III and MIMIC-IV report average gains of 20.65% over LLM-only and deep model-only baselines across precision and accuracy, with 30.97% average improvement in novel diagnosis prediction.

Significance. If the results hold after addressing the attribution of gains to the proposed components, the work could meaningfully advance hybrid deep model-LLM systems for clinical prediction by targeting the overfitting issue in LLMs and improving detection of novel conditions. The structured evidence construction for in-context reasoning is a clear strength if empirically validated.

major comments (2)
  1. [Methods (candidate selection step)] The pipeline description of deep model inference for candidate selection does not include any mechanism, ablation, or analysis demonstrating that the candidate set reliably surfaces novel (unseen in training) diagnoses. This is load-bearing for the 30.97% novel-prediction gain, as any such lift could otherwise be explained by prompt formatting or LLM capability alone rather than the deep-model guidance.
  2. [Results and Experiments] Results section and abstract report aggregate percentage gains without specifying the exact baselines (e.g., which LLMs or deep models), metric definitions, statistical significance tests, data splits, or how 'novel' diagnoses are operationalized. This prevents assessment of whether the central empirical claims are supported.
minor comments (2)
  1. Clarify the precise composition of the adaptive in-context prompt (e.g., how prioritization scores and relational evidence are formatted) to improve reproducibility.
  2. The abstract would benefit from naming the specific metrics (precision@K, accuracy, etc.) underlying the 20.65% figure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important areas for improving the clarity and empirical rigor of the manuscript. We address each major comment below and will revise the paper accordingly to strengthen the presentation of our methods and results.

read point-by-point responses

  1. Referee: [Methods (candidate selection step)] The pipeline description of deep model inference for candidate selection does not include any mechanism, ablation, or analysis demonstrating that the candidate set reliably surfaces novel (unseen in training) diagnoses. This is load-bearing for the 30.97% novel-prediction gain, as any such lift could otherwise be explained by prompt formatting or LLM capability alone rather than the deep-model guidance.

    Authors: We agree that an explicit analysis of the candidate selection step's ability to surface novel diagnoses is necessary to attribute the reported gains specifically to the deep-model guidance rather than other factors. The current manuscript describes the deep model as selecting candidates from its predictions but does not include supporting ablations or statistics on novel diagnosis coverage. In the revised version, we will add: (i) an analysis of the proportion of novel (unseen in training) diagnoses included in the candidate sets, (ii) an ablation isolating the candidate selection component's impact on novel diagnosis performance, and (iii) discussion of how this step addresses LLM overfitting. These additions will directly address the load-bearing concern for the 30.97% figure. revision: yes

  2. Referee: [Results and Experiments] Results section and abstract report aggregate percentage gains without specifying the exact baselines (e.g., which LLMs or deep models), metric definitions, statistical significance tests, data splits, or how 'novel' diagnoses are operationalized. This prevents assessment of whether the central empirical claims are supported.

    Authors: We acknowledge these omissions in the current draft, which limit the ability to fully evaluate the claims. The revised manuscript will explicitly detail: the exact baselines (LLM-only with GPT-4 and Llama-2-70B; deep model-only with fine-tuned BERT and clinical BERT variants), metric definitions (e.g., precision@5, accuracy, and F1), statistical significance testing (paired t-tests with p-values reported), data splits (temporal 70/15/15 splits on MIMIC-III and MIMIC-IV to prevent leakage), and the operationalization of 'novel' diagnoses (conditions absent from the patient's training history and the overall training corpus). We will also incorporate these specifics into the abstract and results section for completeness. revision: yes

Circularity Check

0 steps flagged

Empirical framework with no derivation chain or self-referential elements

full rationale

The paper proposes an empirical in-context reasoning framework (deep model candidate selection, evidential prioritization, relational evidence construction) that feeds into LLM prompts and reports performance on public MIMIC-III/IV benchmarks. No equations, fitted parameters presented as predictions, or mathematical derivations appear in the described pipeline. Claims rest on experimental comparisons to LLM-only and deep-model baselines rather than any self-citation chain or definitional reduction. The 30.97% novel-diagnosis improvement is an observed empirical outcome, not a quantity forced by construction from the method's inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As an applied ML framework paper, no mathematical axioms or invented physical entities are introduced. The 'deep model guidance' and 'evidential prioritization' are procedural innovations rather than new theoretical constructs. No free parameters are specified in the abstract.

pith-pipeline@v0.9.0 · 5526 in / 1158 out tokens · 65419 ms · 2026-05-10T16:00:33.813911+00:00 · methodology

discussion (0)

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Reference graph

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