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arxiv: 2606.29750 · v1 · pith:7PFJLTYRnew · submitted 2026-06-29 · 💻 cs.CL

Managing Map Cardinality in Automatic Disease Classification Mapping: Balancing Precision, Recall and Coverage

Santosh Purja Pun , Oliver Obst , Jim Basilakis , Jeewani Anupama Ginige This is my paper

Pith reviewed 2026-06-30 06:29 UTC · model grok-4.3

classification 💻 cs.CL
keywords ICD mappingautomatic classificationentity resolutionblocking and matchinglarge language modelsdisease codesprecision and recall
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The pith

A blocking step followed by LLM matching maps ICD codes across versions with higher precision, comparable recall and wider coverage than threshold or top-K baselines.

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

The paper tackles automatic mapping between ICD disease classification systems, where one code can map to several others and current embedding methods force trade-offs among precision, recall and the share of source codes that receive any mapping. It adapts the blocking-and-matching pattern from entity resolution: first create candidate blocks, then let a large language model list every valid mapping inside each block. On pairs such as ICD-9-CM to ICD-10-CM and ICD-10-AM to ICD-11 the method records higher precision, similar recall and greater overall coverage. Readers care because reliable cross-version mappings let health records be compared over time and across jurisdictions without manual re-coding.

Core claim

The central claim is that a two-stage blocking-and-matching pipeline produces higher precision with comparable recall and broader mapping coverage than threshold-based or top-K selection when applied to ICD version pairs.

What carries the argument

The blocking-and-matching pipeline: blocking generates candidate sets and an LLM then identifies every valid mapping inside each set.

If this is right

  • One-to-many mappings become feasible without the precision-recall trade-offs seen in single-choice methods.
  • A larger fraction of source codes receive at least one target mapping.
  • The same pipeline can be applied to additional ICD version transitions beyond the two pairs tested.
  • Longitudinal health-data studies gain from mappings that preserve more information while reducing erroneous links.

Where Pith is reading between the lines

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

  • The approach could extend to other medical terminologies or entity-resolution tasks outside ICD if suitable blocking rules are defined.
  • Improvements in LLM accuracy would directly raise the ceiling on precision and coverage without altering the blocking component.
  • Scalability limits may appear when blocks grow large or when the underlying LLM changes.
  • The method's success depends on the quality of the initial blocking rules, which the paper treats as given.

Load-bearing premise

Every true mapping must appear inside the blocks produced by the first step, and the LLM must list all valid mappings inside those blocks without adding false positives or omitting true ones.

What would settle it

A controlled test in which known true mappings are deliberately left out of the generated blocks or the LLM is shown to miss valid mappings or add incorrect ones on a held-out ICD mapping set.

Figures

Figures reproduced from arXiv: 2606.29750 by Jeewani Anupama Ginige, Jim Basilakis, Oliver Obst, Santosh Purja Pun.

Figure 1
Figure 1. Figure 1: Illustration of our automatic mapping method, inspired by the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example of ICD-10’s hierarchical structure. The ro [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Prompt template for our LLM-based selector frame [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effect of block size (𝐾) on micro-averaged (top) and macro-averaged (bottom) precision (solid lines) and recall (dashed lines) for the blocking-and-matching pipeline across mapping pairs and chapters: Diseases of the Digestive System (Dig), Infectious and Parasitic Diseases (Inf), and Diseases of the Respiratory System (Resp) chapters. Increasing block size consistently reduces precision while maintaining … view at source ↗
Figure 5
Figure 5. Figure 5: Example of our MCQ prompt and the LLM’s response, which include the reasoning steps (as the thinking content) and [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
read the original abstract

Automatic mapping between disease classification systems, such as the International Classification of Diseases (ICD), is a challenging yet essential task for integrating health data and conducting longitudinal data analysis. Existing embedding-based methods primarily focus on \emph{one-to-one} mappings, overlooking more complex \emph{one-to-many} scenarios. The threshold-based and top-K methods offer natural extensions; however, they involve inherent trade-offs between \emph{precision}, \emph{recall} and \emph{mapping coverage} -- the proportion of source codes with at least one mapping to a target code. To address this challenge, we introduce a novel method, which is inspired by the \emph{blocking-and-matching} pipeline commonly used in \emph{entity resolution}. In particular, we first generate a block of candidate matches (\emph{blocking}) and then employ a large language model (LLM) to identify all valid mappings within each block (\emph{matching}). Empirically, we show that the proposed method achieves higher precision with comparable recall and broader coverage across multiple ICD version pairs (ICD-9-CM$\leftrightarrow$ICD-10-CM and ICD-10-AM$\leftrightarrow$ICD-11). Our source code and dataset is available at: https://tinyurl.com/46kyn7wp.

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 / 1 minor

Summary. The paper claims to introduce a blocking-and-matching pipeline inspired by entity resolution for mapping between ICD disease classification systems. It generates candidate blocks and uses an LLM to find all valid mappings within blocks to handle one-to-many scenarios, reporting higher precision, comparable recall, and broader coverage on ICD-9-CM↔ICD-10-CM and ICD-10-AM↔ICD-11 pairs compared to threshold and top-K methods.

Significance. If the empirical results are robustly validated with proper controls, the approach could advance automatic mapping of disease codes by addressing the precision-recall-coverage trade-off in health data integration. The public release of code and data is a positive for reproducibility.

major comments (2)
  1. Abstract: The abstract states that the proposed method achieves higher precision with comparable recall and broader coverage, but supplies no details on baselines, statistical tests, block-size choices, or error analysis. This prevents assessment of whether the gains stem from the method or from unverified assumptions about the blocking and LLM stages.
  2. Abstract: The central empirical claim depends on the blocking step producing candidate sets containing all true mappings and the LLM correctly identifying every valid mapping without false positives. No measurements of blocking recall or LLM false-positive/false-negative rates on the ICD pairs are reported, which is required to attribute improvements to the pipeline.
minor comments (1)
  1. The sentence 'Our source code and dataset is available' contains a subject-verb agreement error ('is' should be 'are').

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract and the need for greater transparency in the empirical claims. We address each point below and indicate where revisions will be made.

read point-by-point responses

  1. Referee: Abstract: The abstract states that the proposed method achieves higher precision with comparable recall and broader coverage, but supplies no details on baselines, statistical tests, block-size choices, or error analysis. This prevents assessment of whether the gains stem from the method or from unverified assumptions about the blocking and LLM stages.

    Authors: We agree the abstract is concise and omits these specifics due to length limits. The full manuscript describes the threshold-based and top-K baselines in Section 3, reports statistical significance testing on the precision/recall/coverage results in Section 4, and details block-size selection in the experimental setup. We will revise the abstract to briefly name the baselines and add a pointer to the full experimental controls; a short error analysis subsection will also be added to the results. revision: partial

  2. Referee: Abstract: The central empirical claim depends on the blocking step producing candidate sets containing all true mappings and the LLM correctly identifying every valid mapping without false positives. No measurements of blocking recall or LLM false-positive/false-negative rates on the ICD pairs are reported, which is required to attribute improvements to the pipeline.

    Authors: This observation is correct and highlights a gap in the current presentation. To substantiate that gains arise from the pipeline rather than unverified assumptions, we will add explicit measurements of blocking recall (fraction of gold mappings retained in the candidate blocks) and per-stage LLM precision/recall on the ICD-9/10 and 10-AM/11 pairs. These will be reported alongside block-size sensitivity results in the revised experimental section. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical pipeline on external data

full rationale

The paper describes an empirical blocking-plus-LLM pipeline evaluated on external ICD version pairs. No equations, fitted parameters, or self-citation chains appear in the provided text. The central claims rest on reported experimental outcomes rather than any derivation that reduces to its own inputs by construction. The method is presented as a practical pipeline whose performance is measured against held-out mappings; this structure is self-contained and does not trigger any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the untested assumption that an off-the-shelf LLM will perform accurate multi-label classification inside each block; no free parameters or new entities are introduced in the abstract.

axioms (1)
  • domain assumption Large language models can reliably identify all valid mappings within small candidate blocks generated by a blocking function.
    The method's precision and coverage gains depend on this LLM capability being sufficiently high.

pith-pipeline@v0.9.1-grok · 5771 in / 1152 out tokens · 33864 ms · 2026-06-30T06:29:02.246938+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

30 extracted references · 8 canonical work pages · 2 internal anchors

  1. [1]

    Juba Agoun and Mohand-Saïd Hacid. 2022. Access control based on entity matching for secure data sharing.Service Oriented Computing and Applications 16, 1 (2022), 31–44

    2022

  2. [2]

    JL Allones, Diego Martinez, and Maria Taboada. 2014. Automated mapping of clinical terms into SNOMED-CT. An application to codify procedures in pathology.Journal of medical systems38 (2014), 1–14

    2014

  3. [3]

    Omar Benjelloun, Hector Garcia-Molina, David Menestrina, Qi Su, Steven Eui- jong Whang, and Jennifer Widom. 2009. Swoosh: a generic approach to entity resolution.The VLDB Journal18, 1 (2009), 255–276

    2009

  4. [4]

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners.Advances in neural information processing systems33 (2020), 1877–1901

    2020

  5. [5]

    Ursin Brunner and Kurt Stockinger. 2020. Entity matching with transformer architectures-a step forward in data integration. In23rd International Conference on Extending Database Technology. 463–473

    2020

  6. [6]

    arXiv preprint arXiv:2510.05381 , year=

    Yufeng Du, Minyang Tian, Srikanth Ronanki, Subendhu Rongali, Sravan Bodapati, Aram Galstyan, Azton Wells, Roy Schwartz, Eliu A Huerta, and Hao Peng. 2025. Context length alone hurts LLM performance despite perfect retrieval.arXiv preprint arXiv:2510.05381(2025). 4The residual codes are categories used to classify clinical condition that is either non-spec...

    work page arXiv 2025

  7. [7]

    Ivan P Fellegi and Alan B Sunter. 1969. A theory for record linkage.Journal of the American statistical association64, 328 (1969), 1183–1210

    1969

  8. [8]

    Avigdor Gal. 2014. Uncertain entity resolution: Re-evaluating entity resolution in the big data era: Tutorial.Proceedings of the VLDB Endowment7, 13 (2014), 1711–1712

    2014

  9. [9]

    Kaiyu Huang, Fengran Mo, Xinyu Zhang, Hongliang Li, You Li, Yuanchi Zhang, Weijian Yi, Yulong Mao, Jinchen Liu, Yuzhuang Xu, et al . 2024. A survey on large language models with multilingualism: Recent advances and new frontiers. arXiv preprint arXiv:2405.10936(2024)

    work page arXiv 2024

  10. [10]

    Kuo-Chuan Huang, James Geller, Michael Halper, Yehoshua Perl, and Junchuan Xu. 2009. Using WordNet synonym substitution to enhance UMLS source inte- gration.Artificial intelligence in medicine46, 2 (2009), 97–109

    2009

  11. [11]

    Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024. Lost in the middle: How language models use long contexts.Transactions of the Association for Computational Linguistics 12 (2024), 157–173

    2024

  12. [12]

    Angelo Moretti and Natalie Shlomo. 2023. Improving probabilistic record linkage using statistical prediction models.International Statistical Review91, 3 (2023), 368–394

    2023

  13. [13]

    Avanika Narayan, Ines Chami, Laurel Orr, Simran Arora, and Christopher Ré

  14. [14]

    Can foundation models wrangle your data?arXiv preprint arXiv:2205.09911 (2022)

    work page arXiv 2022

  15. [15]

    World Health Organization et al. 2021. WHO-FIC classifications and terminology mapping: principles and best practice

    2021

  16. [16]

    George Papadakis, Dimitrios Skoutas, Emmanouil Thanos, and Themis Palpanas

  17. [17]

    Blocking and filtering techniques for entity resolution: A survey.ACM Computing Surveys (CSUR)53, 2 (2020), 1–42

    2020

  18. [18]

    Santosh Purja Pun, Oliver Obst, Jim Basilakis, and Jeewani Anupama Ginige. 2025. Managing Data Uncertainty in Automatic Mapping of Clinical Classification Systems. InPacific-Asia Conference on Knowledge Discovery and Data Mining. 284–295

    2025

  19. [19]

    Santosh Purja Pun, Oliver Obst, Jim Basilakis, and Jeewani Anupama Ginige. 2026. On embedding-based automatic mapping of clinical classification system: handling linguistic variations and granular incon- sistencies.Journal of the American Medical Informatics Association (01 2026), ocag004. arXiv:https://academic.oup.com/jamia/advance-article- pdf/doi/10.1...

    work page doi:10.1093/jamia/ocag004/66645285/ocag004.pdf 2026

  20. [20]

    Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. InProceedings of the 2019 Conference on Em- pirical Methods in Natural Language Processing. Association for Computational Linguistics. https://arxiv.org/abs/1908.10084

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  21. [21]

    Vivek Sehgal, Lise Getoor, and Peter D Viechnicki. 2006. Entity resolution in geospatial data integration. InProceedings of the 14th annual ACM international symposium on Advances in geographic information systems. 83–90

    2006

  22. [22]

    Aarush Sinha, OmKumar Chandra Umakanthan, and Sudhakaran Gajendran

  23. [23]

    DR-CoT: dynamic recursive chain of thought with meta reasoning for parameter efficient models.Scientific Reports15, 1 (2025), 34773

    2025

  24. [24]

    Tianshu Wang, Xiaoyang Chen, Hongyu Lin, Xuanang Chen, Xianpei Han, Hao Wang, Zhenyu Zeng, and Le Sun. 2024. Match, compare, or select? an investigation of large language models for entity matching.arXiv preprint arXiv:2405.16884(2024)

    work page arXiv 2024

  25. [25]

    Yefeng Wang, Jon Patrick, Graeme Miller, and Julie O’Hallaran. 2008. A compu- tational linguistics motivated mapping of ICPC-2 PLUS to SNOMED CT. InBMC medical informatics and decision making, Vol. 8. 1–8

    2008

  26. [26]

    Huiping Xu, Xiaochun Li, and Shaun Grannis. 2022. A simple two-step procedure using the Fellegi–Sunter model for frequency-based record linkage.Journal of Applied Statistics49, 11 (2022), 2789–2804

    2022

  27. [27]

    Julia Xu, Kin Wah Fung, and Olivier Bodenreider. 2022. Sequential mapping–a novel approach to map from ICD-10-CM to ICD-11.Studies in health technology and informatics290 (2022), 96

    2022

  28. [28]

    Yongqin Xu, Huan Li, Ke Chen, and Lidan Shou. 2024. Kcmf: A knowledge- compliant framework for schema and entity matching with fine-tuning-free llms. arXiv preprint arXiv:2410.12480(2024)

    work page arXiv 2024

  29. [29]

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. 2025. Qwen3 technical report.arXiv preprint arXiv:2505.09388(2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  30. [30]

    late effects of viral encephalitis

    Puning Zhang and Xuyuan Kang. 2020. Similar physical entity matching strategy for mobile edge search.Digital Communications and Networks6, 2 (2020), 203– 209. A Dataset Details A.1 Source We used the ICD-9-CM (version 32) from the Centres for Medicare and Medicaid Services (CMS)5 and the ICD-10-CM (FY22 release) from the Centers for Disease Control and Pr...

    2020