Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification | Paper Citation Record

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This paper cites Birkhead, Michael Klompas, and Nirav R.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Birkhead, Michael Klompas, and Nirav R

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This paper cites National electronic health records survey.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification National electronic health records survey

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Progress on implementing and using electronic health record systems

Reference 3

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This paper cites Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Reference 4

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This paper cites Identification of risk factors of long COVID and predictive modeling in the RECOVER EHR cohorts.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Identification of risk factors of long COVID and predictive modeling in the RECOVER EHR cohorts

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This paper cites Leveraging electronic health records and knowledge networks for alzheimer’s disease prediction and sex-specific biological insights.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Leveraging electronic health records and knowledge networks for alzheimer’s disease prediction and sex-specific biological insights

Reference 6

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This paper cites Multi-layer representation learning for medical concepts.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Multi-layer representation learning for medical concepts

Reference 7

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This paper cites Modelling 30-day hospital readmission after discharge for COPD patients based on electronic health records.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Modelling 30-day hospital readmission after discharge for COPD patients based on electronic health records

Reference 8

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This paper cites Deep learning prediction models based on EHR trajectories: A systematic review.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Deep learning prediction models based on EHR trajectories: A systematic review

Reference 9

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification ��

Reference 10

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Unsupervised pattern discovery in electronic health care data using probabilistic clustering models

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Machine learning and decision support in critical care

Reference 12

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

Reference 13

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This paper cites Goldberger, Luis A.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Goldberger, Luis A

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This paper cites The Danish National Patient Registry: a review of content, data quality, and research potential.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification The Danish National Patient Registry: a review of content, data quality, and research potential

Reference 15

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification MIMIC-IV (version 3.1)

Reference 16

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Data snapshots, 2025

Reference 17

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This paper cites Data resource profile: Clinical practice research datalink (CPRD) aurum.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Data resource profile: Clinical practice research datalink (CPRD) aurum

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification UK biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age

Reference 19

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This paper cites Parallel time-sensor attention for electronic health record classification.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Parallel time-sensor attention for electronic health record classification

Reference 20

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Risk prediction with electronic health records: A deep learning approach

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Stochastic imputation and uncertainty-aware attention to EHR for mortality prediction

Reference 22

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This paper cites Feature rearrangement based deep learning system for predicting heart failure mortality.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Feature rearrangement based deep learning system for predicting heart failure mortality

Reference 23

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Graph-guided network for irregularly sampled multivariate time series

Reference 24

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

Reference 25

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This paper cites A multi-layered GRU model for COVID-19 patient representation and phenotyping from large-scale EHR data.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification A multi-layered GRU model for COVID-19 patient representation and phenotyping from large-scale EHR data

Reference 26

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Recurrent neural networks for multivariate time series with missing values

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Payberah, Mariagrazia Zottoli, Milad Nazarzadeh, Nathalie Conrad, Kazem Rahimi, and Gholamreza Salimi-Khorshidi

Reference 28

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This paper cites A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction

Reference 29

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification EHRXQA: A multi-modal question answering dataset for electronic health records with chest x-ray images

Reference 30

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Fusion of medical imaging and electronic health records using deep learning: A systematic review and implementation guidelines

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Integrating multi-omics data with EHR for precision medicine using advanced artificial intelligence

Reference 32

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Mohr, Carmen P

Reference 33

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Larry Hill, Gretchen Sanders, Judith C

Reference 34

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Learning long-term dependencies with gradient descent is difficult

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification BEHRT: Transformer for electronic health records

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification TransEHR: Self-supervised transformer for clinical time series data

Reference 37

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Unresolved cited work

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 39

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification An image is worth 16x16 words: Transformers for image recognition at scale

Reference 40

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Med-BERT: Pre-trained contextualized embed- dings on large-scale structured electronic health records for disease prediction

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification ClinicalBERT: Modeling clinical notes and predicting hospital readmission

Reference 42

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Borgwardt

Reference 43

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Characterizing and managing missing structured data in electronic health records: Data analysis

Reference 44

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Deep time series forecasting models: A comprehensive survey

Reference 45

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification itransformer: Inverted transformers are effective for time series forecasting

Reference 46

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Are transformers effective for time series forecasting? In �� , 2023

Reference 47

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Long-term forecasting with TiDE: Time-series dense encoder

Reference 48

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting

Reference 49

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification TSMixer: Lightweight MLP-mixer model for multivariate time series forecasting

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Interpolation-prediction networks for irregularly sampled time series

Reference 51

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Unresolved cited work

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Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Axial Attention in Multidimensional Transformers

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This paper cites Early prediction of sepsis from clinical data: The Phys- ioNet/computing in cardiology challenge 2019, 2019.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Early prediction of sepsis from clinical data: The Phys- ioNet/computing in cardiology challenge 2019, 2019

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:26:37.542720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 22249915-8ecb-4c8d-bf16-b3c36f53bddc · outbound

This paper cites an unresolved cited work.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Unresolved cited work

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T17:26:36.728115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ad2bea1-072b-42e5-99a1-cf9fb168462e · outbound

This paper cites Human activity recognition using smartphones.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification Human activity recognition using smartphones

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T17:26:36.731779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f017369c-5b86-4811-b07f-ecf1cddf55fd · outbound

This paper cites doi:10.1145/3584371.3612986.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification doi:10.1145/3584371.3612986

Reference 2023

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T17:26:37.182866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d5a2d1d2-bfa0-49eb-b40d-e703d71962f8 · outbound

This paper cites doi:10.3390/biomedicines12071496.

Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification doi:10.3390/biomedicines12071496

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T17:26:36.648064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers