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arxiv: 2605.01189 · v2 · pith:HW7S6OD5new · submitted 2026-05-02 · 💻 cs.AI

NEURON: A Neuro-symbolic System for Grounded Clinical Explainability

Anuradha Chandrasekaran , Dimitrios Zikos , Mutlu Mete , Alan Pang , Brady D. Lund , Kewei Sha This is my paper

Pith reviewed 2026-07-01 01:01 UTC · model grok-4.3

classification 💻 cs.AI
keywords neuro-symbolic AIclinical explainabilitySNOMED CTSHAP attributionsRAGheart failure predictionMIMIC-IVLLM
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The pith

NEURON combines SNOMED CT ontology with RAG-LLM to raise clinical prediction AUC and human-aligned explainability.

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

The paper presents NEURON as a neuro-symbolic system that addresses the black-box problem in clinical AI by grounding predictions in medical ontology. It fuses SNOMED CT structural representations with machine learning models for improved accuracy. A RAG-grounded LLM then converts SHAP feature attributions and patient notes into natural language explanations. Validation on MIMIC-IV acute heart failure data shows AUC rising from 0.74-0.77 to 0.84-0.88 and superior human metrics over raw SHAP. This approach aims to enable trustworthy AI in connected health.

Core claim

NEURON integrates SNOMED CT ontology-informed structural representations with machine learning models to bridge raw data and medical nomenclature, then employs a Retrieval-Augmented Generation grounded LLM layer to synthesize SHAP feature attributions and patient-specific clinical notes into coherent natural-language explanations, yielding both higher predictive performance and more clinically interpretable outputs on the MIMIC-IV acute heart failure mortality task.

What carries the argument

Neuro-symbolic integration of SNOMED CT ontology structural representations with ML models, followed by RAG-grounded LLM synthesis of SHAP attributions and notes into explanations.

Load-bearing premise

The assumption that SNOMED CT ontology-informed structural representations combined with a RAG-grounded LLM layer will reliably synthesize SHAP attributions and patient notes into coherent, human-aligned natural-language explanations that deliver professional-level clinical interpretability.

What would settle it

A controlled evaluation in which clinical experts rate NEURON-generated explanations as no more interpretable or useful than raw SHAP visualizations or standard model outputs.

Figures

Figures reproduced from arXiv: 2605.01189 by Alan Pang, Anuradha Chandrasekaran, Brady D. Lund, Dimitrios Zikos, Kewei Sha, Mutlu Mete.

Figure 1
Figure 1. Figure 1: Overall design of the NEURON framework. Node2Vec embeddings are created based on this graph, and admission-level embeddings are further generated using TF-IDF weighted pooling, which results in one fixed 32-dimensional vector per admission. The first two levels of ontology are automatically selected, and a bag of ontology features mapped to these two levels is derived for each admission. The tabular featur… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the neuro-symbolic modeling and explanation pipeline for in-hospital mortality prediction in AHF. view at source ↗
Figure 3
Figure 3. Figure 3: SHAP results - Neuro-symbolic MLP configuration. view at source ↗
Figure 4
Figure 4. Figure 4: Excerpt of the constrained prompt template. view at source ↗
Figure 5
Figure 5. Figure 5: Representative integrated narrative explanation. view at source ↗
read the original abstract

Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability. We present NEURON, a neuro-symbolic system designed to enhance both predictive reliability and clinical interpretability. NEURON integrates SNOMED CT ontology-informed structural representations with machine learning models to bridge the gap between raw data and medical nomenclature. To facilitate human-aligned interaction, the system utilizes a Retrieval-Augmented Generation (RAG) grounded LLM layer to synthesize SHAP feature attributions and patient-specific clinical notes into coherent, natural-language explanations. Validated on the MIMIC-IV dataset for Acute Heart Failure mortality prediction, NEURON improved the AUC from 0.74-0.77 to 0.84-0.88 and significantly outperformed raw SHAP visualizations in human-aligned metrics (0.85 vs. 0.50). Our results demonstrate that NEURON offers a robust, scalable engineering solution for deploying trustworthy, human-centered connected health applications.

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 / 0 minor

Summary. The paper presents NEURON, a neuro-symbolic system that integrates SNOMED CT ontology-informed structural representations with ML models and a RAG-grounded LLM layer. The LLM synthesizes SHAP attributions and patient notes into natural-language explanations. On the MIMIC-IV dataset for Acute Heart Failure mortality prediction, it reports AUC improvement from 0.74-0.77 to 0.84-0.88 and human-aligned metrics of 0.85 versus 0.50 for raw SHAP visualizations.

Significance. If the reported gains are supported by rigorous evaluation, the approach could advance clinical AI by grounding explanations in medical ontologies while using LLM synthesis for narrative transparency. The combination of symbolic structure with RAG-LLM offers a concrete engineering path toward human-centered interpretability in high-stakes domains.

major comments (2)
  1. [Abstract] Abstract and Methods: The central performance claims (AUC lift to 0.84-0.88 and human metric 0.85 vs 0.50) are stated without any description of the base predictive model architecture, training procedure, data splits, statistical testing, or controls. This information is load-bearing for assessing whether the ontology integration and RAG layer produce the claimed predictive reliability and interpretability gains.
  2. [Methods / RAG-LLM layer] RAG-LLM component (likely §3 or Methods): No details are supplied on retrieval corpus construction, prompt design, hallucination mitigation strategies, or the exact protocol and inter-rater reliability for the human-aligned metric. Without these, it is impossible to verify that the LLM layer reliably produces coherent, professional-level explanations rather than superficial fluency.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments correctly identify that key methodological details supporting the reported performance gains are insufficiently described in the current version. We will revise the manuscript to incorporate the requested information.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Methods: The central performance claims (AUC lift to 0.84-0.88 and human metric 0.85 vs 0.50) are stated without any description of the base predictive model architecture, training procedure, data splits, statistical testing, or controls. This information is load-bearing for assessing whether the ontology integration and RAG layer produce the claimed predictive reliability and interpretability gains.

    Authors: We agree that the abstract and Methods section as submitted do not provide adequate detail on these elements. In the revised manuscript we will expand the Methods section to specify the base model architecture (including feature engineering from SNOMED CT embeddings), training procedure and hyperparameters, train/validation/test splits on MIMIC-IV, statistical testing (e.g., DeLong tests or bootstrap confidence intervals for AUC differences), and control experiments that isolate the contribution of the ontology and RAG components. revision: yes

  2. Referee: [Methods / RAG-LLM layer] RAG-LLM component (likely §3 or Methods): No details are supplied on retrieval corpus construction, prompt design, hallucination mitigation strategies, or the exact protocol and inter-rater reliability for the human-aligned metric. Without these, it is impossible to verify that the LLM layer reliably produces coherent, professional-level explanations rather than superficial fluency.

    Authors: We acknowledge the absence of these implementation details. The revised Methods section will describe: (1) retrieval corpus construction (SNOMED CT concepts plus de-identified MIMIC-IV notes indexed for RAG), (2) prompt templates and few-shot examples, (3) hallucination mitigation (document grounding, citation enforcement, and post-hoc fact-checking), and (4) the human evaluation protocol (number of clinicians, rating scale, inter-rater reliability via Cohen’s or Fleiss’ kappa, and exact computation of the 0.85 human-aligned score). revision: yes

Circularity Check

0 steps flagged

No derivation chain or equations; empirical results on external benchmark

full rationale

The paper describes a neuro-symbolic system (SNOMED CT + ML + RAG-LLM) and reports AUC gains (0.74-0.77 to 0.84-0.88) plus human-aligned metric improvement (0.85 vs 0.50) on the external MIMIC-IV dataset. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. The central claims rest on empirical validation rather than any internal reduction to inputs by construction. This is the normal non-circular case for an applied engineering paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities are described or can be extracted.

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105 extracted references · 85 canonical work pages · 3 internal anchors

  1. [1]

    Amina Adadi and Mohammed Berrada. 2018. Peeking inside the black-box: A survey on explainable artificial intelligence (XAI).IEEE Access6 (2018), 52138–52160. doi:10.1109/ACCESS.2018.2870052

    work page doi:10.1109/access.2018.2870052 2018

  2. [2]

    Sara Alhasan and Reem Alnanih. 2025. Enhancing AI explainability through the EXACT framework: A user-centric approach.IEEE Access13 (2025), 98208–98228. doi:10.1109/ACCESS.2025.3576234

    work page doi:10.1109/access.2025.3576234 2025

  3. [3]

    Sajid Ali, Tamer Abuhmed, Shaker El-Sappagh, Khan Muhammad, Jose M Alonso-Moral, Roberto Confalonieri, Riccardo Guidotti, Javier Del Ser, Natalia Díaz-Rodríguez, and Francisco Herrera. 2023. Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence.Information Fusion99 (2023), 101805. doi:10.101...

    work page doi:10.1016/j.inffus.2023.101805 2023

  4. [4]

    Shuroug A Alowais, Sahar S Alghamdi, Nada Alsuhebany, Tariq Alqahtani, Abdulrahman I Alshaya, Sumaya N Almohareb, Atheer Aldairem, Mohammed Alrashed, Khalid Bin Saleh, Hisham A Badreldin, et al. 2023. Revolutionizing healthcare: the role of artificial intelligence in clinical practice.BMC Medical Education23, 1 (2023), 689. doi:10.1186/s12909-023-04698-z

    work page doi:10.1186/s12909-023-04698-z 2023

  5. [5]

    David Alvarez-Melis and Tommi S Jaakkola. 2018. On the robustness of interpretability methods.arXiv:1806.08049(2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  6. [6]

    Julia Amann, Alessandro Blasimme, Effy Vayena, Dietmar Frey, Vince I Madai, and Precise4Q Consortium. 2020. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective.BMC Medical Informatics and Decision Making20, 1 (2020), 310. doi:10.1186/s12911-020- 01332-6

    work page doi:10.1186/s12911-020- 2020

  7. [7]

    Lars W Andersen, Won Young Kim, Maureen Chase, Sharri J Mortensen, Ari Moskowitz, Victor Novack, Michael N Cocchi, Michael W Donnino, et al. 2016. The prevalence and significance of abnormal vital signs prior to in-hospital cardiac arrest.Resuscitation98 (2016), 112–117. doi:10.1016/j.resuscitation.2015.08.016

    work page doi:10.1016/j.resuscitation.2015.08.016 2016

  8. [8]

    Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, et al . 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI.Information Fusion58 (2020), 82–115. doi:10....

    work page doi:10.1016/j.inffus.2019.12.012 2020

  9. [9]

    Sujatha Baddam and Bracken Burns. 2025. Systemic inflammatory response syndrome. InStatPearls [Internet]. StatPearls Publishing, Treasure Island, FL, USA. https://www.ncbi.nlm.nih.gov/books/NBK547669/

    2025

  10. [10]

    Sher Badshah and Hassan Sajjad. 2025. Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form QA. InProceedings of the 9th Widening NLP Workshop. 251–267. doi:10.18653/v1/2025.winlp-main.37

    work page doi:10.18653/v1/2025.winlp-main.37 2025

  11. [11]

    TT Bauer, S Ewig, R Marre, N Suttorp, T Welte, and CAPNETZ Study Group. 2006. CRB-65 predicts death from community-acquired pneumonia. Journal of Internal Medicine260, 1 (2006), 93–101. doi:10.1111/j.1365-2796.2006.01657.x

    work page doi:10.1111/j.1365-2796.2006.01657.x 2006

  12. [12]

    Wobo Bekwelem, Pamela L Lutsey, Laura R Loehr, Sunil K Agarwal, Brad C Astor, Cameron Guild, Christie M Ballantyne, and Aaron R Folsom

  13. [13]

    doi:10.1016/j.annepidem.2011.06.005

    White blood cell count, C-reactive protein, and incident heart failure in the Atherosclerosis Risk in Communities (ARIC) Study.Annals of Epidemiology21, 10 (2011), 739–748. doi:10.1016/j.annepidem.2011.06.005

    work page doi:10.1016/j.annepidem.2011.06.005 2011

  14. [14]

    Subrato Bharati, M Rubaiyat Hossain Mondal, and Prajoy Podder. 2023. A review on explainable artificial intelligence for healthcare: Why, how, and when?IEEE Transactions on Artificial Intelligence5, 4 (2023), 1429–1442. doi:10.1109/TAI.2023.3266418

    work page doi:10.1109/tai.2023.3266418 2023

  15. [15]

    Mikołaj Błaziak, Szymon Urban, Weronika Wietrzyk, Maksym Jura, Gracjan Iwanek, Bartłomiej Stańczykiewicz, Wiktor Kuliczkowski, Robert Zymliński, Maciej Pondel, Petr Berka, et al. 2022. An artificial intelligence approach to guiding the management of heart failure patients using predictive models: A systematic review.Biomedicines10, 9 (2022), 2188. doi:10....

    work page doi:10.3390/biomedicines10092188 2022

  16. [16]

    Michael Böhm, Karl Swedberg, Michel Komajda, Jeffrey S Borer, Ian Ford, Ariane Dubost-Brama, Guy Lerebours, and Luigi Tavazzi. 2010. Heart rate as a risk factor in chronic heart failure (SHIFT): the association between heart rate and outcomes in a randomised placebo-controlled trial.The 16 Anuradha Chandrasekaran, Dimitrios Zikos, Mutlu Mete, Alan Pang, B...

    work page doi:10.1016/s0140-6736(10)61259-7 2010

  17. [17]

    Giovanni Briganti and Olivier Le Moine. 2020. Artificial intelligence in medicine: Today and tomorrow.Frontiers in Medicine7 (2020), 509744. doi:10.3389/fmed.2020.00027

    work page doi:10.3389/fmed.2020.00027 2020

  18. [18]

    Simone Britsch, Markward Britsch, Simon Lindner, Leonie Hahn, Verena Schneider-Lindner, Thomas Helbing, Manfred Thiel, Daniel Duerschmied, and Tobias Becher. 2025. An interpretable machine learning algorithm enables dynamic 48-hour mortality prediction during an ICU stay. Communications Medicine5, 1 (2025), 426. doi:10.1038/s43856-025-01192-z

    work page doi:10.1038/s43856-025-01192-z 2025

  19. [19]

    Federico Cabitza, Raffaele Rasoini, and Gian Franco Gensini. 2017. Unintended consequences of machine learning in medicine.JAMA318, 6 (2017), 517–518. doi:10.1001/jama.2017.7797

    work page doi:10.1001/jama.2017.7797 2017

  20. [20]

    Zijun Chen, Tingming Li, Sheng Guo, Deli Zeng, and Kai Wang. 2023. Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure.Frontiers in Cardiovascular Medicine10 (2023), 1119699. doi:10.3389/fcvm.2023.1119699

    work page doi:10.3389/fcvm.2023.1119699 2023

  21. [21]

    Komala Subramanyam Cherukuri and Haihua Chen. 2024. Investigating the applications of AI in oncology from NIH funded projects: Case study of lung cancer and pancreatic cancer.Proceedings of the Association for Information Science and Technology61, 1 (2024), 871–873. doi:10.1002/pra2.1124

    work page doi:10.1002/pra2.1124 2024

  22. [22]

    Ovidiu Chioncel, Sean P Collins, Andrew P Ambrosy, Mihai Gheorghiade, and Gerasimos Filippatos. 2015. Pulmonary oedema—therapeutic targets. Cardiac Failure Review1, 1 (2015), 38. doi:10.15420/CFR.2015.01.01.38

    work page doi:10.15420/cfr.2015.01.01.38 2015

  23. [23]

    Carlo Combi, Beatrice Amico, Riccardo Bellazzi, Andreas Holzinger, Jason H Moore, Marinka Zitnik, and John H Holmes. 2022. A manifesto on explainability for artificial intelligence in medicine.Artificial Intelligence in Medicine133 (2022), 102423. doi:10.1016/j.artmed.2022.102423

    work page doi:10.1016/j.artmed.2022.102423 2022

  24. [24]

    Roberto Confalonieri and Giancarlo Guizzardi. 2025. On the multiple roles of ontologies in explanations for neuro-symbolic AI.Neurosymbolic Artificial Intelligence1 (2025), NAI–240754. doi:10.3233/NAI-240754

    work page doi:10.3233/nai-240754 2025

  25. [25]

    Daniel De Backer, Patrick Biston, Jacques Devriendt, Christian Madl, Didier Chochrad, Cesar Aldecoa, Alexandre Brasseur, Pierre Defrance, Philippe Gottignies, and Jean-Louis Vincent. 2010. Comparison of dopamine and norepinephrine in the treatment of shock.New England Journal of Medicine362, 9 (2010), 779–789. doi:10.1056/NEJMoa0907118

    work page doi:10.1056/nejmoa0907118 2010

  26. [26]

    Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C Wallace. 2020. ERASER: A benchmark to evaluate rationalized NLP models. InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 4443–4458. doi:10.18653/v1/2020.acl-main.408

    work page doi:10.18653/v1/2020.acl-main.408 2020

  27. [27]

    Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning.arXiv:1702.08608(2017)

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  28. [28]

    Rudresh Dwivedi, Devam Dave, Het Naik, Smiti Singhal, Rana Omer, Pankesh Patel, Bin Qian, Zhenyu Wen, Tejal Shah, Graham Morgan, et al

  29. [29]

    Surveys55, 9 (2023), 1–33

    Explainable AI (XAI): Core ideas, techniques, and solutions.Comput. Surveys55, 9 (2023), 1–33. doi:10.1145/3561048

    work page doi:10.1145/3561048 2023

  30. [30]

    Hui Feng, Yuntzu Yin, Emiliano Reynares, and Jay Nanavati. 2025. OntologyRAG: Better and faster biomedical code mapping with retrieval- augmented generation (RAG) leveraging ontology knowledge graphs and large language models. InInternational Workshop on Knowledge-Enhanced Information Retrieval. Springer, 71–86. doi:10.1007/978-3-032-02899-0_4

    work page doi:10.1007/978-3-032-02899-0_4 2025

  31. [31]

    Gregg C Fonarow, Kirkwood F Adams, William T Abraham, Clyde W Yancy, W John Boscardin, ADHERE Scientific Advisory Committee, et al

  32. [32]

    doi:10.1001/jama.293.5.572

    Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis.JAMA293, 5 (2005), 572–580. doi:10.1001/jama.293.5.572

    work page doi:10.1001/jama.293.5.572 2005

  33. [33]

    Artur d’Avila Garcez and Luis C Lamb. 2023. Neurosymbolic AI: the 3rd wave.Artificial Intelligence Review56, 11 (2023), 12387–12406. doi:10.1007/s10462-023-10448-w

    work page doi:10.1007/s10462-023-10448-w 2023

  34. [34]

    Manas Gaur and Amit Sheth. 2024. Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety.AI Magazine45, 1 (2024), 139–155. doi:10.1002/aaai.12149

    work page doi:10.1002/aaai.12149 2024

  35. [35]

    Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals.Circulation101, 23 (2000), e215–e220. doi:10.1161/01.CIR.101.23.e215

    work page doi:10.1161/01.cir.101.23.e215 2000

  36. [36]

    Jiawei Gu, Xuhui Jiang, Zhichao Shi, Hexiang Tan, Xuehao Zhai, Chengjin Xu, Wei Li, Yinghan Shen, Shengjie Ma, Honghao Liu, et al. 2024. A survey on LLM-as-a-judge.The Innovation(2024). doi:10.1016/j.xinn.2025.101253

    work page doi:10.1016/j.xinn.2025.101253 2024

  37. [37]

    Rosita Guido, Stefania Ferrisi, Danilo Lofaro, and Domenico Conforti. 2024. An overview on the advancements of support vector machine models in healthcare applications: A review.Information15, 4 (2024), 235

    2024

  38. [38]

    Hajar Hakkoum, Ali Idri, and Ibtissam Abnane. 2024. Global and local interpretability techniques of supervised machine learning black box models for numerical medical data.Engineering Applications of Artificial Intelligence131 (2024), 107829. doi:10.1016/j.engappai.2023.107829

    work page doi:10.1016/j.engappai.2023.107829 2024

  39. [39]

    Peter Hase and Mohit Bansal. 2021. When can models learn from explanations? A formal framework for understanding the roles of explanation data.arXiv:2102.02201(2021)

    work page arXiv 2021

  40. [40]

    2002.Accuracy and stability of numerical algorithms

    Nicholas J Higham. 2002.Accuracy and stability of numerical algorithms. SIAM

    2002

  41. [41]

    Scorecard

    Robert R Hoffman, Mohammadreza Jalaeian, Connor Tate, Gary Klein, and Shane T Mueller. 2023. Evaluating machine-generated explanations: a “Scorecard” method for XAI measurement science.Frontiers in Computer Science5 (2023), 1114806. doi:10.3389/fcomp.2023.1114806

    work page doi:10.3389/fcomp.2023.1114806 2023

  42. [42]

    Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal, and Heimo Müller. 2019. Causability and explainability of artificial intelligence in medicine.WIREs Data Mining and Knowledge Discovery9, 4 (2019), e1312. doi:10.1002/widm.1312

    work page doi:10.1002/widm.1312 2019

  43. [43]

    Delower Hossain and Jake Y Chen. 2025. A study on neuro-symbolic artificial intelligence: Healthcare perspectives.arXiv:2503.18213(2025)

    work page arXiv 2025

  44. [44]

    Jicheng Huang, Yufeng Cai, Xusheng Wu, Xin Huang, Jianwei Liu, and Dehua Hu. 2024. Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.Computer Methods and Programs in Biomedicine256 (2024), 108403. NEURON: A Neuro-symbolic System for Grounded Clinical Explainability 17 doi:10.1016/j.cmpb...

    work page doi:10.1016/j.cmpb.2024.108403 2024

  45. [45]

    Shaoyan Huang, Qiuwang Zhang, Lei Liu, Michael JB Kutryk, and Jianzhong Zhang. 2025. Relationship between albumin-corrected anion gap and short-and medium-term all-cause mortality in heart failure patients with a single ICU admission.Frontiers in Cardiovascular Medicine12 (2025), 1608383. doi:10.3389/fcvm.2025.1608383

    work page doi:10.3389/fcvm.2025.1608383 2025

  46. [46]

    Sujeong Hur, Yura Lee, Joongheum Park, Yeong Jeong Jeon, Jong Ho Cho, Duck Cho, Dobin Lim, Wonil Hwang, Won Chul Cha, and Junsang Yoo

  47. [47]

    doi:10.1038/s41746-025-01958-8

    Comparison of SHAP and clinician friendly explanations reveals effects on clinical decision behaviour.NPJ Digital Medicine8, 1 (2025), 578. doi:10.1038/s41746-025-01958-8

    work page doi:10.1038/s41746-025-01958-8 2025

  48. [48]

    Alon Jacovi and Yoav Goldberg. 2020. Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?. In Proceedings of the 58th annual meeting of the Association for Computational Linguistics. 4198–4205. doi:10.18653/v1/2020.acl-main.386

    work page doi:10.18653/v1/2020.acl-main.386 2020

  49. [49]

    Pengcheng Jiang, Cao (Danica) Xiao, Minhao Jiang, Parminder Bhatia, Taha Kass-Hout, Jimeng Sun, and Jiawei Han. 2025. Reasoning-enhanced healthcare predictions with knowledge graph community retrieval. InProceedings of the Thirteenth International Conference on Learning Represen- tations (ICLR)

    2025

  50. [50]

    Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Brian Gow, Benjamin Moody, Steven Horng, Leo Anthony Celi, and Roger Mark. 2024. MIMIC-IV. PhysioNet. doi:10.13026/kpb9-mt58 Version 3.1

    work page doi:10.13026/kpb9-mt58 2024

  51. [51]

    Alistair EW Johnson, Lucas Bulgarelli, Lu Shen, Alvin Gayles, Ayad Shammout, Steven Horng, Tom J Pollard, Sicheng Hao, Benjamin Moody, Brian Gow, et al. 2023. MIMIC-IV, a freely accessible electronic health record dataset.Scientific Data10, 1 (2023), 1. doi:10.1038/s41597-022-01899-x

    work page doi:10.1038/s41597-022-01899-x 2023

  52. [52]

    Md Abdul Kadir, Amir Mosavi, and Daniel Sonntag. 2023. Evaluation metrics for XAI: A review, taxonomy, and practical applications. In2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES). IEEE, 000111–000124. doi:10.1109/INES59282.2023.10297629

    work page doi:10.1109/ines59282.2023.10297629 2023

  53. [53]

    Harmanpreet Kaur, Eytan Adar, Eric Gilbert, and Cliff Lampe. 2022. Sensible AI: Re-imagining interpretability and explainability using sensemaking theory. InProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 702–714. doi:10.1145/3531146.3533135

    work page doi:10.1145/3531146.3533135 2022

  54. [54]

    Gwladys Kelodjou, Laurence Rozé, Véronique Masson, Luis Galárraga, Romaric Gaudel, Maurice Tchuente, and Alexandre Termier. 2024. Shaping up SHAP: enhancing stability through layer-wise neighbor selection. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 13094–13103. doi:10.1609/aaai.v38i12.29208

    work page doi:10.1609/aaai.v38i12.29208 2024

  55. [55]

    Arif Khwaja. 2012. KDIGO clinical practice guidelines for acute kidney injury.Nephron Clinical Practice120, 4 (2012), c179–c184. doi:10.1159/ 000339789

    2012

  56. [56]

    Sujung Lee, Won Ik Cho, Youngrong Lee, Duck Ju Kim, Kyeng Hyun Nam, Sangmin Lee, Jungyo Suh, and Taehoon Ko. 2025. A prompt framework for enhancing LLM-based explainability of medical machine learning models: an intensive care unit application.BMC Medical Informatics and Decision Making25, 1 (2025), 430. doi:10.1186/s12911-025-03239-6

    work page doi:10.1186/s12911-025-03239-6 2025

  57. [57]

    Marcel Levi and Steven M Opal. 2006. Coagulation abnormalities in critically ill patients.Critical Care10, 4 (2006), 222. doi:10.1186/cc4975

    work page doi:10.1186/cc4975 2006

  58. [58]

    Avivit Levy, B Riva Shalom, and Michal Chalamish. 2025. A guide to similarity measures and their data science applications.Journal of Big Data 12, 1 (2025), 188. doi:10.1186/s40537-025-01227-1

    work page doi:10.1186/s40537-025-01227-1 2025

  59. [59]

    Jun Li, Yiwu Sun, Jie Ren, Yifan Wu, and Zhaoyi He. 2025. Machine learning for in-hospital mortality prediction in critically ill patients with acute heart failure: A retrospective analysis based on the MIMIC-IV database.Journal of Cardiothoracic and Vascular Anesthesia39, 3 (2025), 666–674. doi:10.1053/j.jvca.2024.12.016

    work page doi:10.1053/j.jvca.2024.12.016 2025

  60. [60]

    Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, et al. 2024. Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Information Fusion106 (2024), 102301. doi:10.1016/j.inff...

    work page doi:10.1016/j.inffus.2024.102301 2024

  61. [61]

    Zeying Lou, Fanghua Zeng, Wenbao Huang, Li Xiao, Kang Zou, and Huasheng Zhou. 2024. Association between the anion-gap and 28-day mortality in critically ill adult patients with sepsis: A retrospective cohort study.Medicine103, 30 (2024), e39029. doi:10.1097/MD.0000000000039029

    work page doi:10.1097/md.0000000000039029 2024

  62. [62]

    Scott M Lundberg, Gabriel G Erion, and Su-In Lee. 2018. Consistent individualized feature attribution for tree ensembles.arXiv:1802.03888(2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  63. [63]

    Lundberg and Su-In Lee

    Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. InProceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS). 4768–4777

    2017

  64. [64]

    Alexandre Mebazaa, Markku S Nieminen, Milton Packer, Alain Cohen-Solal, Franz X Kleber, Stuart J Pocock, Roopal Thakkar, Robert J Padley, Pentti Põder, Matti Kivikko, et al. 2007. Levosimendan vs dobutamine for patients with acute decompensated heart failure: the SURVIVE Randomized Trial.JAMA297, 17 (2007), 1883–1891. doi:10.1001/jama.297.17.1883

    work page doi:10.1001/jama.297.17.1883 2007

  65. [65]

    Munib Mesinovic, Peter Watkinson, and Tingting Zhu. 2025. Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data.Scientific Reports15, 1 (2025), 27887. doi:10.1038/s41598-025-13299-3

    work page doi:10.1038/s41598-025-13299-3 2025

  66. [66]

    Riccardo Miotto, Li Li, Brian A Kidd, and Joel T Dudley. 2016. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records.Scientific Reports6, 1 (2016), 26094. doi:10.1038/srep26094

    work page doi:10.1038/srep26094 2016

  67. [67]

    Kayo Misumi, Yuya Matsue, Kazutaka Nogi, Yudai Fujimoto, Nobuyuki Kagiyama, Takatoshi Kasai, Takeshi Kitai, Shogo Oishi, Eiichi Akiyama, Satoshi Suzuki, et al. 2023. Derivation and validation of a machine learning-based risk prediction model in patients with acute heart failure. Journal of Cardiology81, 6 (2023), 531–536. doi:10.1016/j.jjcc.2023.02.006

    work page doi:10.1016/j.jjcc.2023.02.006 2023

  68. [68]

    Sina Mohseni, Jeremy E Block, and Eric Ragan. 2021. Quantitative evaluation of machine learning explanations: A human-grounded benchmark. InProceedings of the 26th International Conference on Intelligent User Interfaces. 22–31. doi:10.1145/3397481.3450689

    work page doi:10.1145/3397481.3450689 2021

  69. [69]

    2020.Interpretable machine learning

    Christoph Molnar. 2020.Interpretable machine learning. Lulu.com. 18 Anuradha Chandrasekaran, Dimitrios Zikos, Mutlu Mete, Alan Pang, Brady D. Lund, and Kewei Sha

    2020

  70. [70]

    Ramesh Nadarajah, Tanina Younsi, Elizabeth Romer, Keerthenan Raveendra, Yoko M Nakao, Kazuhiro Nakao, Farag Shuweidhi, David C Hogg, Ronen Arbel, Doron Zahger, et al. 2023. Prediction models for heart failure in the community: A systematic review and meta-analysis.European Journal of Heart Failure25, 10 (2023), 1724–1738. doi:10.1002/ejhf.2970

    work page doi:10.1002/ejhf.2970 2023

  71. [71]

    National Library of Medicine. 2025. SNOMED CT United States Edition. https://www.nlm.nih.gov/healthit/snomedct/index.html Accessed: Dec. 8, 2025

    2025

  72. [72]

    Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice Van Keulen, and Christin Seifert. 2023. From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable AI.Comput. Surveys55, 13s (2023), 1–42. doi:10.1145/3583558

    work page doi:10.1145/3583558 2023

  73. [73]

    Sidra Naveed, Gunnar Stevens, and Dean Robin-Kern. 2024. An overview of the empirical evaluation of explainable AI (XAI): A comprehensive guideline for user-centered evaluation in XAI.Applied Sciences14, 23 (2024), 11288. doi:10.3390/app142311288

    work page doi:10.3390/app142311288 2024

  74. [74]

    Fnu Neha, Deepshikha Bhati, and Deepak Kumar Shukla. 2025. Retrieval-augmented generation (RAG) in healthcare: A comprehensive review.AI 6, 9 (2025), 226. doi:10.3390/ai6090226

    work page doi:10.3390/ai6090226 2025

  75. [75]

    Ke Niu, You Lu, Xueping Peng, and Jingni Zeng. 2022. Fusion of sequential visits and medical ontology for mortality prediction.Journal of Biomedical Informatics127 (2022), 104012. doi:10.1016/j.jbi.2022.104012

    work page doi:10.1016/j.jbi.2022.104012 2022

  76. [76]

    Sarah Nogueira, Konstantinos Sechidis, and Gavin Brown. 2018. On the stability of feature selection algorithms.Journal of Machine Learning Research18, 174 (2018), 1–54

    2018

  77. [77]

    Atsushi Ohgushi, Takayuki Ohtani, Natsumi Nakayama, Shigeo Asai, Yoshiyuki Ishii, Atsuo Namiki, Manabu Akazawa, and Hirotoshi Echizen

  78. [78]

    doi:10.1186/s40780-015-0036-1

    Risk of major bleeding at different PT-INR ranges in elderly Japanese patients with non-valvular atrial fibrillation receiving warfarin: a nested case-control study.Journal of Pharmaceutical Health Care and Sciences2, 1 (2016), 2. doi:10.1186/s40780-015-0036-1

    work page doi:10.1186/s40780-015-0036-1 2016

  79. [79]

    Evandro S Ortigossa, Thales Gonçalves, and Luis Gustavo Nonato. 2024. Explainable artificial intelligence (XAI)—From theory to methods and applications.IEEE Access12 (2024), 80799–80846

    2024

  80. [80]

    Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, and Francesco Osborne. 2023. Knowledge graphs: Opportunities and challenges.Artificial Intelligence Review56, 11 (2023), 13071–13102. doi:10.1007/s10462-023-10465-9

    work page doi:10.1007/s10462-023-10465-9 2023

Showing first 80 references.