A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data
Abstract The widespread adoption of Electronic Health Records (EHRs) and deep learning, particularly through Self-Supervised Representation Learning (SSRL) for categorical data, has transformed clinical decision-making. This scoping review, following PRISMA-ScR guidelines, examines 46 studies publis...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01692-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849225926324256768 |
|---|---|
| author | Yuanyuan Zheng Adel Bensahla Mina Bjelogrlic Jamil Zaghir Hugues Turbe Lydie Bednarczyk Christophe Gaudet-Blavignac Julien Ehrsam Stéphane Marchand-Maillet Christian Lovis |
| author_facet | Yuanyuan Zheng Adel Bensahla Mina Bjelogrlic Jamil Zaghir Hugues Turbe Lydie Bednarczyk Christophe Gaudet-Blavignac Julien Ehrsam Stéphane Marchand-Maillet Christian Lovis |
| author_sort | Yuanyuan Zheng |
| collection | DOAJ |
| description | Abstract The widespread adoption of Electronic Health Records (EHRs) and deep learning, particularly through Self-Supervised Representation Learning (SSRL) for categorical data, has transformed clinical decision-making. This scoping review, following PRISMA-ScR guidelines, examines 46 studies published from January 2019 to April 2024, sourced from PubMed, MEDLINE, Embase, ACM, and Web of Science, focusing on SSRL for unlabeled categorical EHR data. The review systematically assesses research trends in building computationally and data-efficient representations for medical tasks, identifying major trends in model families: Transformer-based (43%), Autoencoder-based (28%), and Graph Neural Network-based (17%) models. The analysis highlights scenarios where healthcare institutions can leverage or develop SSRL technologies. It also addresses current limitations in assessing the impact of these technologies and identifies research opportunities to enhance their influence on clinical practice. |
| format | Article |
| id | doaj-art-96ec731b920049a8b9233e23c66417d0 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-96ec731b920049a8b9233e23c66417d02025-08-24T11:52:07ZengNature Portfolionpj Digital Medicine2398-63522025-06-018111510.1038/s41746-025-01692-1A scoping review of self-supervised representation learning for clinical decision making using EHR categorical dataYuanyuan Zheng0Adel Bensahla1Mina Bjelogrlic2Jamil Zaghir3Hugues Turbe4Lydie Bednarczyk5Christophe Gaudet-Blavignac6Julien Ehrsam7Stéphane Marchand-Maillet8Christian Lovis9Division of Medical Information Sciences, Geneva University HospitalsDivision of Medical Information Sciences, Geneva University HospitalsDivision of Medical Information Sciences, Geneva University HospitalsDivision of Medical Information Sciences, Geneva University HospitalsDivision of Medical Information Sciences, Geneva University HospitalsDivision of Medical Information Sciences, Geneva University HospitalsDivision of Medical Information Sciences, Geneva University HospitalsDivision of Medical Information Sciences, Geneva University HospitalsDepartment of Computer Science, University of GenevaDivision of Medical Information Sciences, Geneva University HospitalsAbstract The widespread adoption of Electronic Health Records (EHRs) and deep learning, particularly through Self-Supervised Representation Learning (SSRL) for categorical data, has transformed clinical decision-making. This scoping review, following PRISMA-ScR guidelines, examines 46 studies published from January 2019 to April 2024, sourced from PubMed, MEDLINE, Embase, ACM, and Web of Science, focusing on SSRL for unlabeled categorical EHR data. The review systematically assesses research trends in building computationally and data-efficient representations for medical tasks, identifying major trends in model families: Transformer-based (43%), Autoencoder-based (28%), and Graph Neural Network-based (17%) models. The analysis highlights scenarios where healthcare institutions can leverage or develop SSRL technologies. It also addresses current limitations in assessing the impact of these technologies and identifies research opportunities to enhance their influence on clinical practice.https://doi.org/10.1038/s41746-025-01692-1 |
| spellingShingle | Yuanyuan Zheng Adel Bensahla Mina Bjelogrlic Jamil Zaghir Hugues Turbe Lydie Bednarczyk Christophe Gaudet-Blavignac Julien Ehrsam Stéphane Marchand-Maillet Christian Lovis A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data npj Digital Medicine |
| title | A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data |
| title_full | A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data |
| title_fullStr | A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data |
| title_full_unstemmed | A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data |
| title_short | A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data |
| title_sort | scoping review of self supervised representation learning for clinical decision making using ehr categorical data |
| url | https://doi.org/10.1038/s41746-025-01692-1 |
| work_keys_str_mv | AT yuanyuanzheng ascopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT adelbensahla ascopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT minabjelogrlic ascopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT jamilzaghir ascopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT huguesturbe ascopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT lydiebednarczyk ascopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT christophegaudetblavignac ascopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT julienehrsam ascopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT stephanemarchandmaillet ascopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT christianlovis ascopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT yuanyuanzheng scopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT adelbensahla scopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT minabjelogrlic scopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT jamilzaghir scopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT huguesturbe scopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT lydiebednarczyk scopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT christophegaudetblavignac scopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT julienehrsam scopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT stephanemarchandmaillet scopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata AT christianlovis scopingreviewofselfsupervisedrepresentationlearningforclinicaldecisionmakingusingehrcategoricaldata |