Paper Citation Record · LEDGER

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data

As of 18 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2505.20731.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.20731 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-15T16:51:41.340064Z

Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation bc0f4c8e-8864-45f3-afc8-22fc0a9ae7e5 · outbound

This paper cites The multivariate poisson-log normal distribution.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data The multivariate poisson-log normal distribution

Reference 1

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Observation 8f8f0837-d65c-44d6-b321-ce7c3b586e55 · outbound

This paper cites Statistical guarantees for the em algorithm: From population to sample-based analysis.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Statistical guarantees for the em algorithm: From population to sample-based analysis

Reference 2

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Observation 68e7e167-3045-4b3a-a1a0-e0700069496f · outbound

This paper cites Zero-inflation in the Multivariate Poisson Lognormal Family.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Zero-inflation in the Multivariate Poisson Lognormal Family

Reference 3

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Observation eff4e5ea-6fbc-44b6-98c9-2566ae256350 · outbound

This paper cites Representation learning: A review and new perspectives.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Representation learning: A review and new perspectives

Reference 4

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Observation 2cea3f7d-7abe-4150-8963-6b4e7bb548ee · outbound

This paper cites Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels

Reference 5

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Observation db4721f2-75be-4095-a0be-6583d2a580b6 · outbound

This paper cites Variational inference: A review for statisticians.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Variational inference: A review for statisticians

Reference 6

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This paper cites Chime: Clustering of high-dimensional gaussian mixtures with em algorithm and its optimality.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Chime: Clustering of high-dimensional gaussian mixtures with em algorithm and its optimality

Reference 7

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Observation c0ab6dd5-69e4-4b92-af1b-52bc84ed86bf · outbound

This paper cites Robust principal component analysis? Journal of the ACM (JACM), 58 0 (3): 0 1--37, 2011.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Robust principal component analysis? Journal of the ACM (JACM), 58 0 (3): 0 1--37, 2011

Reference 8

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Observation 624453bc-9a40-4d25-8fe6-07f584facea0 · outbound

This paper cites Consistency of maximum-likelihood and variational estimators in the stochastic block model.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Consistency of maximum-likelihood and variational estimators in the stochastic block model

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:37.747744Z

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Observation b7972032-c066-473a-89ba-ca15c75d6998 · outbound

This paper cites Joint maximum likelihood estimation for high-dimensional exploratory item factor analysis.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Joint maximum likelihood estimation for high-dimensional exploratory item factor analysis

Reference 10

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Observation 02eadeff-164c-4ae8-8484-66d46b474f48 · outbound

This paper cites Variational inference for probabilistic poisson pca.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Variational inference for probabilistic poisson pca

Reference 11

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This paper cites Variational inference for sparse network reconstruction from count data.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Variational inference for sparse network reconstruction from count data

Reference 12

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Observation f8efc8a2-e722-4447-afa6-988839d4f041 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 13

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 14

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Observation 9e00ac32-10ad-4d8a-9589-02d3b7677a5e · outbound

This paper cites A markov chain monte carlo approach to confirmatory item factor analysis.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data A markov chain monte carlo approach to confirmatory item factor analysis

Reference 15

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Observation 805a94d2-761b-4cfa-8823-cf02341f0a71 · outbound

This paper cites Factor augmented sparse throughput deep relu neural networks for high dimensional regression.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Factor augmented sparse throughput deep relu neural networks for high dimensional regression

Reference 16

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Observation 90710d82-b5b7-4cc3-a0de-e8b988b567f2 · outbound

This paper cites The patient-determined disease steps scale is not interchangeable with the expanded disease status scale in mild to moderate multiple sclerosis.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data The patient-determined disease steps scale is not interchangeable with the expanded disease status scale in mild to moderate multiple sclerosis

Reference 17

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This paper cites Theory of gaussian variational approximation for a poisson mixed model.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Theory of gaussian variational approximation for a poisson mixed model

Reference 18

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Asymptotic normality and valid inference for gaussian variational approximation

Reference 19

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Modern factor analysis

Reference 20

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Observation a2186251-28d7-44c4-9846-9bc44ec704b7 · outbound

This paper cites Clinical knowledge extraction via sparse embedding regression (keser) with multi-center large scale electronic health record data.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Clinical knowledge extraction via sparse embedding regression (keser) with multi-center large scale electronic health record data

Reference 21

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Estimation of generalized linear latent variable models

Reference 22

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data An introduction to variational methods for graphical models

Reference 23

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Em algorithm for mixed poisson and other discrete distributions

Reference 24

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Observation 4f995de5-6cc9-4ab4-b8ef-a2375a563876 · outbound

This paper cites Disability in multiple sclerosis: a reference for patients and clinicians.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Disability in multiple sclerosis: a reference for patients and clinicians

Reference 25

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Automatic variational inference in stan

Reference 26

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (edss)

Reference 27

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Observation 281af754-d24d-4178-88e4-5ab7b8df35ab · outbound

This paper cites Deep representation learning of electronic health records to unlock patient stratification at scale.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Deep representation learning of electronic health records to unlock patient stratification at scale

Reference 28

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Observation 300d6124-ce1a-4b50-aeca-baed0a1270b5 · outbound

This paper cites Validation of patient determined disease steps (pdds) scale scores in persons with multiple sclerosis.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Validation of patient determined disease steps (pdds) scale scores in persons with multiple sclerosis

Reference 29

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Biobert: a pre-trained biomedical language representation model for biomedical text mining

Reference 30

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Multisource representation learning for pediatric knowledge extraction from electronic health records

Reference 31

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Efficient Estimation of Word Representations in Vector Space

Reference 32

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This paper cites Deep patient: an unsupervised representation to predict the future of patients from the electronic health records.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Deep patient: an unsupervised representation to predict the future of patients from the electronic health records

Reference 33

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Generalized latent trait models

Reference 34

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Observation cedb6290-76fc-4b5a-8a2b-f8ccbe3c3ab7 · outbound

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data The variational gaussian approximation revisited

Reference 35

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data A variational bayes approach to variable selection

Reference 36

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Observation 8ce2b69b-df2a-43e2-8a10-b87924645b57 · outbound

This paper cites Deep representation learning: Fundamentals, technologies, applications, and open challenges.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Deep representation learning: Fundamentals, technologies, applications, and open challenges

Reference 37

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This paper cites Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

Reference 38

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature

Reference 39

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This paper cites A multivariate poisson-log normal mixture model for clustering transcriptome sequencing data.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data A multivariate poisson-log normal mixture model for clustering transcriptome sequencing data

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:32.526905Z

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Observation 22ba9e4d-f3bf-4fd1-bb38-2462b7827f43 · outbound

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Probabilistic principal component analysis

Reference 41

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Convergence properties of a general algorithm for calculating variational bayesian estimates for a normal mixture model

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:32.254575Z

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Observation 3823a1bb-72d6-4f1b-85ee-842f451bd8d3 · outbound

This paper cites Asymptotic statistics, volume 3.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Asymptotic statistics, volume 3

Reference 43

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Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Outcome measures in clinical trials for multiple sclerosis

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:31.952512Z

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Observation f7fb49d0-7800-449f-adb7-42fbca2e101c · outbound

This paper cites Graphical models, exponential families, and variational inference.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Graphical models, exponential families, and variational inference

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:31.619011Z

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Observation 97fb97f7-0f1e-4da2-8d95-a8a56b298bef · outbound

This paper cites Knowledge-driven online multimodal automated phenotyping system.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Knowledge-driven online multimodal automated phenotyping system

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:31.388354Z

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Observation fd7e8347-03b3-49c5-a166-fbfdf3c408a6 · outbound

This paper cites Yes, but did it work?: Evaluating variational inference.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Yes, but did it work?: Evaluating variational inference

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:31.170409Z

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Observation 63ebd666-2bbe-4530-9f88-71cebb8cefbc · outbound

This paper cites On variational bayes estimation and variational information criteria for linear regression models.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data On variational bayes estimation and variational information criteria for linear regression models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:31.052237Z

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Observation 986ff5f8-0d21-4f70-8496-c300c123166d · outbound

This paper cites Coder: Knowledge-infused cross-lingual medical term embedding for term normalization.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Coder: Knowledge-infused cross-lingual medical term embedding for term normalization

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:30.929969Z

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This paper cites A note on exploratory item factor analysis by singular value decomposition.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data A note on exploratory item factor analysis by singular value decomposition

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:30.805154Z

Source-reported events for the cited work

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Observation e1f5dd1c-0e34-4e8f-8ee7-c2e0cdd8bd37 · outbound

This paper cites Multi-source learning via completion of block-wise overlapping noisy matrices.

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data Multi-source learning via completion of block-wise overlapping noisy matrices

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:56:30.707079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 0e5af6ac-f710-4ed5-8272-2b590487dc21 · inbound

Latent Factor Point Processes for Patient Representation in Electronic Health Records cites this paper.

Latent Factor Point Processes for Patient Representation in Electronic Health Records Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:51:41.344392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.