arxiv: 2604.11835 · v2 · submitted 2026-04-12 · 💻 cs.LG · cs.AI

Recognition: unknown

Schema-Adaptive Tabular Representation Learning with LLMs for Generalizable Multimodal Clinical Reasoning

Hongxi Mao , Wei Zhou , Mengting Jia , Tao Fang , Huan Gao , Bin Zhang , Shangyang Li

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:53 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords schema-adaptive learningtabular datalarge language modelszero-shot transfermultimodal reasoningclinical diagnosiselectronic health recordsdementia

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The pith

Converting tabular clinical data into natural language statements allows LLMs to generalize across different EHR schemas for multimodal dementia diagnosis.

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

This paper proposes a method to learn transferable representations from tabular clinical data by converting structured variables into semantic natural language statements and encoding them with a pretrained large language model. The goal is to overcome the limitation of poor schema generalization in machine learning for tabular data, which is especially problematic in clinical domains where electronic health record schemas differ widely between sources. By integrating these embeddings with MRI data in a multimodal framework, the approach enables zero-shot alignment to unseen schemas without retraining or manual engineering. Experiments show state-of-the-art results on NACC and ADNI datasets for dementia diagnosis, outperforming clinical baselines including board-certified neurologists. A sympathetic reader cares because this could make AI systems more practical for real-world heterogeneous medical data.

Core claim

The authors claim that Schema-Adaptive Tabular Representation Learning leverages LLMs to transform structured tabular variables into semantic natural language statements, producing embeddings that support zero-shot transfer across unseen EHR schemas. When combined with MRI imaging in a multimodal setup for dementia diagnosis, this yields superior performance on the NACC and ADNI datasets compared to existing methods and even human experts in retrospective tasks. The discovery establishes that LLM-driven semantic encoding can bridge schema variations in structured data, providing a scalable solution for heterogeneous clinical datasets and extending LLM capabilities to tabular domains.

What carries the argument

The schema-adaptive tabular encoder, which converts structured variables to natural language statements for encoding by a pretrained LLM to create semantically aligned embeddings.

If this is right

Clinical AI systems can be deployed across institutions with differing EHR formats without per-schema retraining.
Multimodal models combining tabular and imaging data become more robust to data heterogeneity.
The approach offers a general pathway to apply LLM reasoning to other structured data domains beyond medicine.
Zero-shot capabilities reduce the data collection and harmonization burden in multi-site studies.

Where Pith is reading between the lines

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

If the embeddings truly preserve clinical semantics, the method could enable more explainable AI by tracing decisions back to the natural language descriptions.
This technique might extend to other high-stakes domains with variable schemas, such as legal or financial records, though domain-specific validation would be needed.
Future work could test whether the LLM encoding introduces biases from its pretraining data that affect clinical accuracy in underrepresented populations.

Load-bearing premise

Converting structured tabular variables into semantic natural language statements and encoding them with a pretrained LLM produces embeddings that preserve clinically relevant information and enable reliable zero-shot alignment across unseen schemas without introducing LLM-specific biases or hallucinations.

What would settle it

If embeddings from the same clinical concept in different schemas fail to align closely in the LLM embedding space, or if performance on a new schema drops significantly below baselines despite the conversion, the central claim would be falsified.

Figures

Figures reproduced from arXiv: 2604.11835 by Bin Zhang, Hongxi Mao, Huan Gao, Mengting Jia, Shangyang Li, Tao Fang, Wei Zhou.

**Figure 1.** Figure 1: Overview of our proposed architecture. Patient records comprising tabular and imaging data are processed [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Figure 2: Comparison between table-only and multi [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Image-only vs. multimodal training perfor [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: SHAP summary plot for AD prediction show [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

read the original abstract

Machine learning for tabular data remains constrained by poor schema generalization, a challenge rooted in the lack of semantic understanding of structured variables. This challenge is particularly acute in domains like clinical medicine, where electronic health record (EHR) schemas vary significantly. To solve this problem, we propose Schema-Adaptive Tabular Representation Learning, a novel method that leverages large language models (LLMs) to create transferable tabular embeddings. By transforming structured variables into semantic natural language statements and encoding them with a pretrained LLM, our approach enables zero-shot alignment across unseen schemas without manual feature engineering or retraining. We integrate our encoder into a multimodal framework for dementia diagnosis, combining tabular and MRI data. Experiments on NACC and ADNI datasets demonstrate state-of-the-art performance and successful zero-shot transfer to unseen schemas, significantly outperforming clinical baselines, including board-certified neurologists, in retrospective diagnostic tasks. These results validate our LLM-driven approach as a scalable, robust solution for heterogeneous real-world data, offering a pathway to extend LLM-based reasoning to structured domains.

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.

Desk Editor's Note private letter to a colleague

The paper's LLM-driven conversion of tabular EHR to semantic text for zero-shot schema transfer is a direct response to a real clinical data problem, but the supporting experiments lack the validation and controls needed to back the SOTA and neurologist-beating claims.

read the letter

The main takeaway is that this work turns structured clinical variables into natural language statements, embeds them with a pretrained LLM, and uses the result in a multimodal model with MRI for dementia diagnosis. It reports zero-shot transfer across schemas on NACC and ADNI plus better performance than board-certified neurologists. That pipeline targets a practical barrier in EHR data without manual feature work or retraining per schema. The multimodal angle and focus on real heterogeneity are reasonable extensions of existing LLM tabular work. The paper does a clean job laying out why schema differences matter in medicine and sketching an end-to-end route that could scale. The soft spots sit in the conversion and evaluation steps. The abstract gives no explicit procedure for generating the statements, no clinician check that they preserve exact values and thresholds, and no ablation isolating the LLM encoding from simpler baselines. Without those, the reported gains could trace to dataset artifacts or LLM biases rather than the claimed mechanism. The outperformance numbers also need the full methods, data splits, statistical tests, and controls for LLM variability to hold up. This is for researchers building multimodal clinical tools that must handle messy, changing tabular sources. A reader already working on LLM embeddings for structured data or deployment in healthcare would get the most from it, provided the experiments are tightened. I would send it to peer review rather than desk reject. The underlying problem is worth addressing and the approach is concrete enough to benefit from referee input on validation and reproducibility.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes Schema-Adaptive Tabular Representation Learning, a method that leverages large language models (LLMs) to transform structured tabular variables from clinical datasets into semantic natural language statements. These statements are encoded using a pretrained LLM to create transferable embeddings that enable zero-shot alignment across unseen schemas. The approach is integrated into a multimodal framework for dementia diagnosis combining tabular and MRI data. Experiments on the NACC and ADNI datasets are reported to achieve state-of-the-art performance, successful zero-shot transfer, and significant outperformance over clinical baselines including board-certified neurologists.

Significance. If the experimental results hold under rigorous validation, the work could have substantial significance for machine learning on tabular data in medicine. It addresses the critical issue of schema heterogeneity in EHRs by providing a semantic, LLM-based representation that generalizes without retraining or manual engineering. This could facilitate more robust multimodal clinical reasoning and scalable application to real-world heterogeneous data.

major comments (2)

The central mechanism relies on converting tabular clinical variables (e.g., from NACC/ADNI schemas) into semantic natural language statements. However, the manuscript provides no explicit description of the generation procedure (templated prompts vs. free-form), no expert clinical validation of the statements' fidelity to the original numeric/categorical values, and no ablation studies isolating the LLM encoding from simpler baselines. This is load-bearing for the zero-shot transfer claim, as LLMs may introduce hallucinations, omit granularity (e.g., exact lab thresholds), or inject biases.
The abstract and reported claims assert SOTA performance and outperformance over neurologists, but the provided details lack information on data splits, statistical significance tests, controls for LLM stochasticity (e.g., multiple runs, temperature settings), precise baseline definitions, and how zero-shot transfer was evaluated on unseen schemas. Without these, the support for the superiority claims cannot be fully assessed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and have revised the manuscript accordingly to improve clarity and rigor.

read point-by-point responses

Referee: The central mechanism relies on converting tabular clinical variables (e.g., from NACC/ADNI schemas) into semantic natural language statements. However, the manuscript provides no explicit description of the generation procedure (templated prompts vs. free-form), no expert clinical validation of the statements' fidelity to the original numeric/categorical values, and no ablation studies isolating the LLM encoding from simpler baselines. This is load-bearing for the zero-shot transfer claim, as LLMs may introduce hallucinations, omit granularity (e.g., exact lab thresholds), or inject biases.

Authors: We agree that the original manuscript lacked sufficient detail on the statement generation process. The revised version now includes an explicit description in Section 3.2, specifying the use of fixed templated prompts that convert each variable into a natural language statement by incorporating its name, value, units, and a brief clinical descriptor (e.g., 'The patient's age is 72 years'). We did not conduct formal expert clinical validation across the full dataset due to scale and resource constraints; however, we have added a set of representative examples in the appendix along with a limitations discussion acknowledging potential issues such as omitted granularity or hallucinations. To isolate the contribution of LLM semantic encoding, we have added ablation studies in Section 5.3 comparing the full approach against simpler baselines (direct numeric embedding and variable-name string concatenation), which demonstrate improved zero-shot transfer performance attributable to the semantic statements. revision: yes
Referee: The abstract and reported claims assert SOTA performance and outperformance over neurologists, but the provided details lack information on data splits, statistical significance tests, controls for LLM stochasticity (e.g., multiple runs, temperature settings), precise baseline definitions, and how zero-shot transfer was evaluated on unseen schemas. Without these, the support for the superiority claims cannot be fully assessed.

Authors: We appreciate the referee's emphasis on experimental rigor. The revised manuscript expands Section 4.1 to detail the data splits (stratified 70/15/15 on NACC with 5-fold cross-validation and hold-out zero-shot evaluation on ADNI), includes statistical significance testing via paired t-tests with p-values reported in Table 2, specifies LLM controls (temperature fixed at 0 with results averaged over three independent runs using different random seeds), provides precise baseline definitions (including retrospective neurologist performance derived from chart review protocols), and clarifies the zero-shot protocol in Section 5.2 (training on NACC schema and testing on ADNI after remapping variables to simulate unseen structures). These additions strengthen the evidential basis for the reported performance claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on external experiments

full rationale

The paper introduces a method for converting tabular clinical variables into natural language statements encoded by a pretrained LLM, then integrates the resulting embeddings into a multimodal dementia diagnosis framework. Performance claims (SOTA on NACC/ADNI, zero-shot schema transfer, outperforming neurologists) are supported solely by empirical results on held-out datasets rather than any internal derivation, equation, or self-referential construction. No mathematical steps, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. The central mechanism is an empirical proposal whose validity is tested externally, satisfying the default expectation of non-circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that pretrained LLMs can reliably extract transferable clinical semantics from natural-language renderings of tabular fields; no free parameters or invented entities are mentioned in the abstract.

axioms (1)

domain assumption Pretrained LLMs encode clinically meaningful semantics when tabular variables are rendered as natural language statements.
This assumption underpins the zero-shot alignment claim and is invoked by the core method description.

pith-pipeline@v0.9.0 · 5493 in / 1156 out tokens · 45676 ms · 2026-05-10T15:53:58.707880+00:00 · methodology

discussion (0)

Reference graph

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