Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model | Paper Citation Record

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This paper cites Deep representation learning of patient data from electronic health records (ehr): A systematic review.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Deep representation learning of patient data from electronic health records (ehr): A systematic review

Reference 1

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This paper cites Attention is all you need.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Attention is all you need

Reference 2

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

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

Reference 3

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This paper cites Interpreting deep embeddings for disease progression clustering.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Interpreting deep embeddings for disease progression clustering

Reference 4

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This paper cites Generic medical concept embedding and time decay for diverse patient outcome prediction tasks.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Generic medical concept embedding and time decay for diverse patient outcome prediction tasks

Reference 5

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Unresolved cited work

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This paper cites Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records

Reference 7

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This paper cites An optimized stacked support vector machines based expert system for the effective prediction of heart failure.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model An optimized stacked support vector machines based expert system for the effective prediction of heart failure

Reference 8

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This paper cites Endpoint prediction of heart failure using electronic health records.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Endpoint prediction of heart failure using electronic health records

Reference 9

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Xgboost model for chronic kidney disease diagnosis

Reference 10

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Detection of the chronic kidney disease using xgboost classifier and explaining the influence of the attributes on the model using shap

Reference 11

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Xgboost-shap-based interpretable diagnostic framework for alzheimer’s disease

Reference 12

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This paper cites Interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database

Reference 13

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This paper cites Early prediction of heart disease via lstm-xgboost.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Early prediction of heart disease via lstm-xgboost

Reference 14

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This paper cites Predicting cardiovascular health trajectories in time-series electronic health records with lstm models.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Predicting cardiovascular health trajectories in time-series electronic health records with lstm models

Reference 15

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This paper cites Rodos- thenous, Aoxing Liu, Sara Hägg, Markus Perola, and Andrea Ganna.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Rodos- thenous, Aoxing Liu, Sara Hägg, Markus Perola, and Andrea Ganna

Reference 16

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This paper cites Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

Reference 17

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This paper cites MuST: Multimodal spatiotemporal graph-transformer for hospital readmission prediction.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model MuST: Multimodal spatiotemporal graph-transformer for hospital readmission prediction

Reference 18

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This paper cites Predicting unplanned readmissions in the intensive care unit: a multimodality evaluation.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Predicting unplanned readmissions in the intensive care unit: a multimodality evaluation

Reference 19

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This paper cites Patient phenotyping for atopic dermatitis with transformers and machine learning: Algorithm development and validation study.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Patient phenotyping for atopic dermatitis with transformers and machine learning: Algorithm development and validation study

Reference 20

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This paper cites BEHRT: transformer for electronic health records.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model BEHRT: transformer for electronic health records

Reference 21

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Hi-BEHRT: hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records

Reference 22

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This paper cites Learning the graphical structure of electronic health records with graph convolutional transformer.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Learning the graphical structure of electronic health records with graph convolutional transformer

Reference 23

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This paper cites Self- supervised forecasting in electronic health records with attention-free models.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Self- supervised forecasting in electronic health records with attention-free models

Reference 24

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

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

Reference 25

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This paper cites Exploiting hierarchy in medical concept embedding.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Exploiting hierarchy in medical concept embedding

Reference 26

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This paper cites $\mathtt{MedGraph:}$ Structural and Temporal Representation Learning of Electronic Medical Records.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model $\mathtt{MedGraph:}$ Structural and Temporal Representation Learning of Electronic Medical Records

Reference 27

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This paper cites Temporal phenotyping using deep predictive clustering of disease progression.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Temporal phenotyping using deep predictive clustering of disease progression

Reference 28

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This paper cites Interpreting Differentiable Latent States for Healthcare Time-series Data.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Interpreting Differentiable Latent States for Healthcare Time-series Data

Reference 29

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Multiple change-point detection for Poisson point processes

Reference 30

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Real-time change-point detection: A deep neural network- based adaptive approach for detecting changes in multivariate time series data

Reference 31

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 32

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Improving language understanding by generative pre-training

Reference 33

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Language models are unsupervised multitask learners

Reference 34

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This paper cites John Wiley & Sons, 2013.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model John Wiley & Sons, 2013

Reference 35

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model XGBoost: A scalable tree boosting system

Reference 36

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model SGPT: GPT Sentence Embeddings for Semantic Search

Reference 37

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Data resource profile: Nationwide registry data for high-throughput epidemiology and machine learning (FinRegistry)

Reference 38

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Unresolved cited work

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Pytorch: An imperative style, high-performance deep learning library

Reference 40

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This paper cites Harris, K.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Harris, K

Reference 41

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Scikit-learn: Machine learning in Python

Reference 42

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Biological age predictors

Reference 43

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model A unified approach to interpreting model predictions

Reference 44

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Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Axiomatic attribution for deep networks

Reference 45

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This paper cites Transformers in time series: a survey.

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model Transformers in time series: a survey

Reference 46

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