A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models

Jingxi Zhu; Tianxiang Zhao; Vasant Honavar; Weijieying Ren; Zehao Liu

REVIEW 3 major objections 4 minor 2 cited by

A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This survey claims that EHR modeling research can be organized by five design dimensions, and it supplies a taxonomy, a dataset inventory, and a metric guide to back that claim.

desk verdict A useful taxonomic frame for EHR modeling, but the manuscript is too incomplete to support the 'comprehensive' claim. read the letter →

arxiv 2507.12774 v1 pith:CKCKGU7N submitted 2025-07-17 cs.LG cs.AIcs.CL

Weijieying Ren , Jingxi Zhu , Zehao Liu , Tianxiang Zhao , Vasant Honavar This is my paper

classification cs.LGcs.AIcs.CL

keywords electronichealthrecordsdeeplearninglargelanguagemodelstaxonomyself-supervisedmultimodalclinicalagentstabulardata

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that the sprawling research area of electronic health record (EHR) modeling can be organized under one coherent design space. It proposes a unified taxonomy with five dimensions: data-centric approaches, neural architecture design, learning-focused strategies, multimodal learning, and LLM-based modeling systems. Within each dimension it reviews representative methods, from active learning and code perturbation through graph and tree architectures to retrieval-augmented generation and clinical agents. The survey also consolidates public datasets and evaluation metrics so that future work can be positioned and compared on common ground. A sympathetic reader would take the paper's core contribution to be a structured roadmap that tells researchers where any given method sits and which open problems remain.

What carries the argument

The load-bearing object is the taxonomy itself: a coarse-to-fine grid whose top level has five design dimensions and whose lower levels split each dimension into concrete choices, such as sample selection, input-space transformation, tree, graph, and rule architectures, irregular-sampling handling, contrastive and masked objectives, retrieval-augmented generation, and agent planning. It does the work of turning a large citation list into a finite set of design decisions. A secondary organizing device is a three-projection view of EHR data covering $time \times feature$, $feature \times value$, and $time \times value$, which maps components of a record such as workflows, lab results, and longitudinal trends to the modeling challenges they create.

What would settle it

A reader could rerun the stated keyword search on the repositories listed in the paper and count how many EHR modeling papers published since 2020 fall outside the reviewed set; if a substantial method family, such as reinforcement-learning-based treatment policies or privacy-preserving federated EHR models, is absent from the taxonomy, the comprehensive-coverage claim would need revision.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that EHR modeling has matured into a design space that can be surveyed as a whole rather than as isolated model families. The authors build a coarse-to-fine taxonomy whose top level spans five design dimensions and use it to place hundreds of methods, from ontology-graph attention models and time-aware recurrent networks to EHR-to-text translation and memory-equipped LLM agents. The taxonomy is paired with an inventory of public datasets and a set of evaluation metrics mapped to downstream tasks, from classification and survival analysis to ranking and generative modeling. If the claim holds, nearly every recent method occupies a recognizable cell in this grid, and the grid itself makes the field's open problems visible as structural gaps.

Load-bearing premise

The survey is comprehensive only if its keyword search and inclusion criteria capture the field, but the paper reports the keywords without screening counts or an exclusion log.

Editorial extensions

If this is right

A researcher can locate any EHR modeling method by asking which design dimension it modifies, which makes method comparison and reuse more systematic.
The consolidated dataset and metric inventory lowers the entry barrier for new groups and gives the community a shared evaluation vocabulary.
The survey's open-problem list becomes a concrete agenda: benchmarking, explainability, clinical alignment, and generalization across settings follow directly from gaps in the taxonomy.
Emerging families such as foundation models for multimodal clinical data, EHR-to-text translation, and LLM-driven agents appear as natural extensions of the multimodal and LLM dimensions rather than as disconnected outliers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the taxonomy is maintained on the companion website the paper points to, it could evolve from a static survey into a living registry that tracks new methods beyond 2025.
The taxonomy's design-choice framing may transfer to other longitudinal structured data domains, such as wearable-device streams or insurance claims, because it organizes methods by what they change rather than by clinical task; the paper does not make this claim.
A testable consequence of the taxonomy's completeness is that every recent EHR modeling paper should fit in at least one primary cell; papers that straddle multiple dimensions would expose where the grid needs refinement.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Bottom line: this survey has the right rough shape—a five-dimensional taxonomy (data, architecture, learning objectives, multimodal, LLM systems) that genuinely exceeds previous surveys in scope—but the manuscript is not in a state where I can trust its central claim. There are multiple editorial artifacts (placeholder 'over *** papers' in Section 1.4, 'added [244, 346]' notes in Table 2, wrong ACM year, duplicate paragraphs) and, more importantly, the stated literature-search protocol in Section 1.3 is inconsistent with the reference list: it says 'published in or after 2020', yet foundational works like RETAIN (2016), T-LSTM (2017), GRAM (2017), and Neural ODEs (2018) appear throughout as core methods. Without a reproducible search strategy or an exclusion log, the 'comprehensive' claim rests on shaky ground.

What is actually good: the taxonomy is a useful organizing frame. Table 1's comparison with prior surveys is fair and the paper covers a wide range of topics—data-centric approaches, graph and tree architectures, self-supervised learning, medical vision-language models, LLM prompting/RAG/agents, plus datasets and metrics. For someone entering the field, this could serve as a genuinely helpful roadmap, provided the completeness issues are fixed.

The soft spots are proportionate to the paper's importance: these are not fatal conceptual errors, but they are load-bearing for a survey whose entire value is coverage and reliability. The paper should be revised with a reproducible search protocol (including how pre-2020 foundational works are handled), screening counts, and a careful copyedit to remove placeholders and stray notes. If the authors fix that, I'd be happy to cite it. As it stands, I wouldn't point a student to it without reservations.

Who is it for? Early-career researchers and practitioners looking for an entry point into EHR modeling. It deserves a serious referee—the scope is big enough and the field needs a unified reference—but I'd recommend major revision before acceptance, not desk rejection.

Referee Report

3 major / 4 minor

Summary. The manuscript is a survey of electronic health record (EHR) modeling that proposes a unified taxonomy spanning data-centric approaches, neural architecture design, learning-focused strategies, multimodal learning, and LLM-based systems. It reviews representative methods in each area, outlines clinical applications, summarizes public datasets and evaluation metrics, and discusses open problems such as benchmarking, explainability, and clinical alignment. The paper claims to be the first comprehensive survey to unify these dimensions and provides a companion website for updates.

Significance. If the survey's coverage and organization are reliable, it would be a useful entry point for researchers: the five-dimension taxonomy is intuitively structured, the consolidated dataset tables (Tables 6 and 7) and metric table (Table 8) are practical resources, and the discussion of LLM-based agents and multimodal clinical AI reflects current trends. The paper also gives credit to a broad range of recent work and explicitly identifies open problems. However, the central 'comprehensive scope' contribution is not currently verifiable because the literature collection protocol is unreproducible and internally inconsistent with the actual reference set. The survey's value as a roadmap depends on fixing this gap.

major comments (3)

[§1.3 and §1.4] The literature-collection protocol is not reproducible and is internally inconsistent with the reviewed corpus. Section 1.3 restricts inclusion to papers 'Published in or after 2020,' yet the survey discusses and cites foundational pre-2020 works as core methods, including RETAIN [60] (2016), T-LSTM [18] (2017), GRAM [59] (2017), and Neural ODEs [46] (2018), both in the narrative (e.g., §4.3.1, §8.2.2) and in Table 2. Section 1.4 also states 'over *** papers' without a completed count, and no screening counts, exclusion log, or coverage analysis are reported. Because the stated inclusion criteria cannot reproduce the actual reference set, the central 'Comprehensive scope' claim is unsupported as written. Please revise the protocol to explain how pre-2020 foundational works were treated, add screening and exclusion counts, and complete the paper count.
[Abstract and §1.4] The paper is inconsistent about the number of design dimensions in its central taxonomy. The abstract and §1.1 describe five dimensions, including multimodal learning, and Table 2 contains a Multimodal Learning category, but §1.4 states that the taxonomy spans 'four key design dimensions' and lists only data quantity/quality, neural architecture, learning objectives, and LLM paradigms, omitting multimodal learning from the list. This inconsistency directly affects the claimed contribution of a 'unified taxonomy' and should be reconciled.
[Table 2 and §1.4] The manuscript contains editorial and placeholder artifacts that undermine its reliability as a survey. Table 2 includes the editorial note 'added [244, 346]' in the Zero/Few-Shot Prompting row and in the Retrieval-Augmented Methods row, and §1.4 contains the placeholder 'over *** papers' for the number of reviewed papers. These are not merely stylistic issues: the paper count is part of the comprehensiveness claim, and the editorial notes suggest the table was assembled from an unfinished draft. Please remove all editorial notes and provide a final, verified count.

minor comments (4)

[§10.1] The 'Clinical Agent' paragraph is repeated verbatim twice in succession; one copy should be deleted.
[§1.1] The structure description says 'Section 7 provides a comprehensive overview... Section 6 discusses...' while the actual section order in the text lists Section 5 then Section 7 then Section 6; reordering either the prose or the sections would improve readability.
[§9.2] The opening sentence contains a typo: 'AA summary of the metrics' should be 'A summary of the metrics.'
[Title page and front matter] The ACM Reference Format line gives the year 2018, the CCS Concepts block still contains placeholder text such as 'Do Not Use This Code,' and the running header retains 'Trovato and Tobin, et al.' These template artifacts should be cleaned before resubmission.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey proposes an organizing taxonomy and reviews prior work, with no derivation that reduces to its own inputs or to self-citation.

full rationale

This is a survey paper rather than a paper that derives predictions from fitted parameters or from a formal model. Its central contribution is a five-dimensional taxonomy for organizing EHR modeling methods, and a taxonomy is a categorization chosen by the authors, not a result derived from the reviewed papers in a way that could be circular. The paper does not fit parameters to data and then rename those fits as predictions; it does not invoke a uniqueness theorem from the authors' own prior work to force a modeling choice; and it does not smuggle in an ansatz via self-citation. The main weakness identified by the reviewer is a reproducibility and consistency issue in the literature-collection protocol: Section 1.3 states inclusion requires publication in or after 2020, yet the survey reviews and cites foundational pre-2020 works such as RETAIN [60], T-LSTM [18], GRAM [59], and Neural ODEs [46], and Section 1.4 contains a placeholder ('over *** papers'). That inconsistency undermines the 'comprehensive' claim and the reproducibility of the search, but it is not circularity: the taxonomy and the narrative summaries do not depend on the protocol by definition, and no load-bearing step is equivalent to its own input. The self-citations present (e.g., Ren et al. TabLog [237]) are ordinary citations to the authors' prior work and are not used to justify the survey's organizing framework. Because there is no derivation chain whose conclusions are presupposed by its premises, the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The two axioms are the assumptions behind the comprehensiveness claim: the taxonomy dimensions are a chosen frame, and the keyword search is treated as representative. Neither is justified by evidence in the paper.

assumptions (2)

ad hoc to paper The five selected design dimensions fully organize EHR modeling research.
The taxonomy is introduced as the paper's own contribution in Sections 1.4 and 2. No comparison against alternative taxonomies or validation is provided.
domain assumption The keyword-based search returns the relevant literature.
Section 1.3 lists repositories and keywords but no PRISMA-style process; the comprehensiveness claim depends on this assumption.

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Cite this review

Pith. "Pith review of A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models." pith.science (2026). https://pith.science/paper/CKCKGU7N

@misc{pith2026250712774,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CKCKGU7N}},
  note         = {Machine review of arXiv:2507.12774}
}

read the original abstract

Artificial intelligence (AI) has demonstrated significant potential in transforming healthcare through the analysis and modeling of electronic health records (EHRs). However, the inherent heterogeneity, temporal irregularity, and domain-specific nature of EHR data present unique challenges that differ fundamentally from those in vision and natural language tasks. This survey offers a comprehensive overview of recent advancements at the intersection of deep learning, large language models (LLMs), and EHR modeling. We introduce a unified taxonomy that spans five key design dimensions: data-centric approaches, neural architecture design, learning-focused strategies, multimodal learning, and LLM-based modeling systems. Within each dimension, we review representative methods addressing data quality enhancement, structural and temporal representation, self-supervised learning, and integration with clinical knowledge. We further highlight emerging trends such as foundation models, LLM-driven clinical agents, and EHR-to-text translation for downstream reasoning. Finally, we discuss open challenges in benchmarking, explainability, clinical alignment, and generalization across diverse clinical settings. This survey aims to provide a structured roadmap for advancing AI-driven EHR modeling and clinical decision support. For a comprehensive list of EHR-related methods, kindly refer to https://survey-on-tabular-data.github.io/.

Figures

Figures reproduced from arXiv: 2507.12774 by the authors.

**Figure 2.** An illustration of data-centric approaches. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** An illustration of binning methods. 4.1.1 Discretization and Binning-Based Methods. The heterogeneous nature of EHRs presents several challenges to deep learning models. First, EHRs contain diverse feature types, including boolean, categorical, and numerical variables, each with distinct underlying distributions. Second, the dependencies among these features are often complex and not known a priori, making it dif… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: An illustration of structure-aware designs. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]

**Figure 5.** Figure 5: An illustration of temporal dependency modeling strategies. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]

**Figure 6.** Figure 6: An illustration of learning with large language model strategies. [PITH_FULL_IMAGE:figures/full_fig_p023_6.png]

**Figure 7.** Figure 7: Illustration of the clinical decision support landscape across the care timeline. A typical patient trajectory can be categorized [PITH_FULL_IMAGE:figures/full_fig_p029_7.png]

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records
cs.IR 2026-05 unverdicted novelty 5.0 of 10

EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.
Learning temporal embeddings from electronic health records of chronic kidney disease patients
cs.LG 2026-01 unverdicted novelty 4.0 of 10

T-LSTM embeddings on CKD EHR data yield lower DBI (9.91), higher stage classification accuracy (0.74), and better mortality prediction (0.82-0.83) than vanilla LSTM, attention LSTM, or end-to-end baselines.

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