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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Observation 3072fff3-b1ad-4ca2-b8ab-844cf85251a0 · outbound

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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This paper cites Xgboost model for chronic kidney disease diagnosis.

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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This paper cites Detection of the chronic kidney disease using xgboost classifier and explaining the influence of the attributes on the model using shap.

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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This paper cites Xgboost-shap-based interpretable diagnostic framework for alzheimer’s disease.

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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This paper cites Hi-BEHRT: hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records.

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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This paper cites Multiple change-point detection for Poisson point processes.

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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This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

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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This paper cites Data resource profile: Nationwide registry data for high-throughput epidemiology and machine learning (FinRegistry).

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

Reference 39

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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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