# A Survey of Deep Learning for Electronic Health Records
## 7 Conclusions  
At present, various models and algorithms of deep learning have been widely applied to EHR, which can effectively accelerate the solution of various medical tasks. Deep learning in EHR is a new field compared to other deep learning applications such as Computer Vision (CV) or Natural Language Processing (NLP). This paper composes and outlines the relevant research based on EHR and deep learning in recent years, and describes the development of deep learning in EHR from four application areas, namely information extraction, representation learning, outcome prediction, and privacy protection, respectively. In addition, this survey also provides an overview of common deep learning models in various application areas of EHR. The common English EHR datasets that have been publicly available are also summarized. By analyzing the recent research status in this area, this paper identifies the problems and challenges that still need to be addressed in the existing areas of representational learning, information extraction, and privacy preservation.  
Currently, there are still many challenges for EHR applications based on deep learning. As described in this paper, there are many similarities between the field and NLP, which means that it is possible to migrate techniques from NLP to EHR. For example, in representation learning the representation extraction model in NLP can be redesigned according to the characteristics of medical data itself. When performing medical privacy protection, in addition to using the adversarial algorithms used in the existing work mentioned in this paper, the protection algorithms in CV can also be improved for textual data protection. With the gradual progress of deep learning in EHR research, deep learning-based EHR will be widely promoted in terms of performance and security, which will bring great convenience to healthcare services.  
Conceptualization, J.X. and X.X.; methodology, J.X.; validation, X.X., J.C. and V.S.S.; investigation, J.X.; resources, X.X.; data curation, J.X.; writing-original draft preparation, J.X. and X.X.; writing-review and editing, J.X., X.X., V.S.S., J.M. and J.C.; visualization, J.X.; supervision, X.X. and J.M.; funding acquisition, X.X. and Z.C. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the National Natural Science Foundation of China under grants 61876217 and 62176175; the Innovative Team of Jiangsu Province under grant Y3 DXX-086; the Science and Technology Development Project of Suzhou under grants SGC2021078.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this survey. These data can be found here: MIMIC, [[https://mimic.mit.edu/](https://mimic.mit.edu/)]([https://mimic.mit.edu/](https://mimic.mit.edu/)); eICU-CRD, [[https://eicu-crd.mit.edu/](https://eicu-crd.mit.edu/)]([https://eicu-crd.mit.edu/](https://eicu-crd.mit.edu/)); PCORnet, [[https://pcornet.org/](https://pcornet.org/)]([https://pcornet.org/](https://pcornet.org/)); Open NHS, [[https://digital.nhs.uk/](https://digital.nhs.uk/)]([https://digital.nhs.uk/](https://digital.nhs.uk/)); NCBI-Disease, [[https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/](https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/)]([https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/](https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/)); i2b2/n2c NLP Research Data sets, [[https://www.i2b2.org/NLP/DataSets/](https://www.i2b2.org/NLP/DataSets/)]([https://www.i2b2.org/NLP/DataSets/](https://www.i2b2.org/NLP/DataSets/)). All these links are accessed on 16 November 2022.  
**Conflicts of Interest:** The authors declare no conflict of interest.